From 6da759cdce8221105a4bbfd3af2b0912e452e680 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 16:51:29 -0700 Subject: [PATCH 01/16] Initialize PWF baseline for #30 Continuous index-trajectory change detection (Sentinel-2 + BFAST). Four new exports across a config restructure, index registry, cube fetch mode, and a bfast reducer, plus a network-decoupled vignette. One branch, 0.3.0 release. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- planning/active/findings.md | 90 ++++++++++++++++++++++++++++++++++++ planning/active/progress.md | 12 +++++ planning/active/task_plan.md | 49 ++++++++++++++++++++ 3 files changed, 151 insertions(+) create mode 100644 planning/active/findings.md create mode 100644 planning/active/progress.md create mode 100644 planning/active/task_plan.md diff --git a/planning/active/findings.md b/planning/active/findings.md new file mode 100644 index 0000000..943c63d --- /dev/null +++ b/planning/active/findings.md @@ -0,0 +1,90 @@ +# Findings — Continuous index-trajectory change detection (Sentinel-2 + BFAST) (#30) + +## Design validation (session 2026-07-08, plan mode) + +Verified directly against the installed toolchain (read-only): gdalcubes 0.7.3, +rstac 1.0.1, terra 1.9.11, zoo 1.8.15. `bfast` / `strucchangeRcpp` NOT installed. + +### gdalcubes `reduce_time` R-callback contract (from `reduce_time.cube.Rd`) +- Signature: `reduce_time(x, expr, ..., FUN, names = NULL, load_pkgs = FALSE, load_env = FALSE)`. +- FUN receives a **2-D array: rows = bands, cols = time steps**. Row names ARE + band names — the shipped example indexes `x["B04", ]`, so `x["kndvi", ]` works + **iff the input cube's band is literally named `kndvi`**. +- FUN must return a numeric vector of length `length(names)` → those become the + output bands. +- **FUN runs in spawned worker R processes** (`load_pkgs`/`load_env`). Therefore: + - `bfast` must be loaded in workers → `reduce_time(..., load_pkgs = "bfast")`. + The main-process `rlang::check_installed("bfast")` gate alone is insufficient. + - `band`/`ts_start`/`frequency`/`start`/`level`/`min_obs` must be + **closure-captured scalars**, never derived from `cube` inside FUN. +- gdalcubes ships `L8NY18` (228 local Landsat TIFs at + `system.file("L8NY18", package="gdalcubes")`) → an **offline** cube fixture for + reduce_time / apply_pixel shape tests (no network). +- `create_image_collection(files, date_time=, band_names=)` builds a collection + from local GeoTIFFs deterministically → synthetic offline cube feasible. + +### Corrections to the issue-#30 sketch (all confirmed) +1. `tanh(ndvi^2)` → gdalcubes `apply_pixel` uses the **tinyexpr C engine**; `^` + is not R exponentiation. Use `tanh(pow((nir-red)/(nir+red), 2))`. tinyexpr + provides `pow`, `tanh`, `sqrt`, `exp`, `log`, etc. +2. `cube_view(extent = col)` sizes the cube to the union bbox of all returned + scenes (full S2 tile), then `filter_geom` masks most to NaN AFTER the work. + Build `extent` explicitly from `bbox_target` + parsed `t0`/`t1`, as + `dft_stac_fetch()` already does. +3. `ext_filter(\`eo:cloud_cover\` <= cloud_cover_max)` uses NSE — a runtime + variable needs `{{cloud_cover_max}}` and the property back-ticked. spacehakr's + example only ever used a literal `<= 10`, so it never hit this. +4. `start_of(cube)` and `terra_from_cube()` are pseudo-code. Real: ts start from + `dimension_values(cube, "M")$t` (main process); cube→terra via + `write_ncdf(overwrite=TRUE)` → `terra::rast()` → `terra::mask()` (repo idiom). +5. `stac_search` cannot take both `bbox` and `intersects` — cube mode uses + `intersects` only. +6. Config restructure is **forced backward-compatible** by the existing + `expect_named(cfg, c("stac_url","collection","asset","available_years"))` + test (exact-set). Cube sources add `cube = TRUE`; categorical sources keep + exactly 4 fields → marker is **absence = FALSE** (`isTRUE(cfg$cube)`). +7. Scale/offset must be applied **inside** the index expression as a per-band + affine token `(asset*scale+offset)`. A non-zero offset does NOT cancel in a + ratio index → NDVI-on-raw-DNs is wrong for Landsat (offset -0.2) and + baseline-04.00 S2 (offset -0.1). Only a pure multiplicative scale cancels. + +### API names verified present with the used signatures +`image_mask(band, min, max, values, bits, invert)`, `filter_geom(cube, geom, srs)`, +`apply_pixel(x, ...)` (generic; `expr`/`names`), `raster_cube(..., mask=)`, +`cube_view`, `stac_image_collection`, `write_ncdf`, `ncdf_cube`, +`stac_search(..., intersects=, bbox=, limit=)`, `ext_filter(q, expr)`, +`post_request`, `items_fetch`, `items_sign(items, sign_fn)`, +`sign_planetary_computer()`, `dimension_values`. `bands(cube)` → data.frame with +`name/type/scale/offset/unit`. + +### bfast::bfastmonitor return / semantics (knowledge; bfast not installed) +- `$breakpoint` = **decimal year** (e.g. 2022.34) or `NA` when no break (NOT an + error). `$magnitude` = median of monitoring-period residuals (observed − + predicted) → **negative = index drop = scour / veg loss; positive = + establishment**. Inherent to the method; must be documented for callers who + threshold on it. +- `start = c(year, period)` = monitoring-period start; `history=`, `level=` pass + through. Failure modes to `tryCatch`: too-few history obs for + `response ~ trend + harmon`, singular fits, gappy series → return `c(NA, NA)`. + +### Open correctness subtlety (documented, follow-up candidate) +- S2 baseline-04.00 (+1000 DN) offset is **not uniform** across a 2017→2024 + archive: scenes processed under baseline ≥ 04.00 (~≥ 2022-01-25) carry the + offset; earlier scenes have offset 0. A single `offset=-0.1` is slightly wrong + for the pre-2022 portion. kNDVI is fairly robust (scale cancels in the ratio; + residual is a small additive bias near the 2022 boundary). Ship documented + accepted-bias; per-scene harmonization from item metadata is a follow-up. + +## Reuse map +- `stac_cache_key()` / `auto_utm_epsg()` / sf-coercion / `write_ncdf→terra→mask` + idiom in `R/dft_stac_fetch.R` — reuse for `dft_stac_cube()`. +- `dft_class_table()` CSV-reader in `R/dft_stac_classes.R` — mirror for + `dft_index_table()` (`inst/lulc_classes/` → `inst/indices/`). +- `spacehakr::spk_stac_calc()` — pagination/intersects/cloud-filter proof and QA + chip reader; used ONLY in `data-raw/`, never a DESCRIPTION dependency. + +## Issue context + +Full body: `gh issue view 30 --repo NewGraphEnvironment/drift`. +Relates to #9 (deferred BFAST evaluation), #19 (arbitrary factor rasters — +the optional from-to labelling bolt-on depends on it). diff --git a/planning/active/progress.md b/planning/active/progress.md new file mode 100644 index 0000000..1c194ca --- /dev/null +++ b/planning/active/progress.md @@ -0,0 +1,12 @@ +# Progress — Continuous index-trajectory change detection (Sentinel-2 + BFAST) (#30) + +## Session 2026-07-08 + +- Plan-mode exploration — two Plan agents vetted the design against issue #30 and + the codebase (acquisition half: config + index registry + cube; reduction half: + bfast reducer + tests + vignette). Both independently confirmed the issue-sketch + corrections. Phases approved by user (one branch, single 0.3.0 release). +- Empirically verified the gdalcubes/rstac/terra APIs read-only (see findings.md). +- Created branch `30-continuous-index-trajectory-change-detec` off main. +- Scaffolded PWF baseline from issue #30 with approved phases. +- Next: start Phase 1 (config restructure + Sentinel-2 source + tests). diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md new file mode 100644 index 0000000..f547681 --- /dev/null +++ b/planning/active/task_plan.md @@ -0,0 +1,49 @@ +# Task: Continuous index-trajectory change detection (Sentinel-2 + BFAST) for floodplain reaches (#30) + +drift detects change today by differencing pre-classified annual land-cover maps +(`dft_stac_fetch` → `dft_rast_classify` → `dft_rast_transition`). Masked to a +floodplain polygon that is the *weakest* place for the Esri IO LULC 9-class +annual product: it collapses gravel bars / wet-dry channel / sparse seasonal +riparian into `bare`/`rangeland`, and annual compositing smears event timing. + +Issue #30 adds the stronger signal: a **continuous index trajectory** per pixel — +detect *when* a pixel's spectral history breaks and by how much — instead of +comparing two categorical labels. The machinery is already a dependency +(`gdalcubes`, `rstac`, `terra`); this is a new fetch mode + a reducer, not a new +toolchain. Four new exports: `dft_stac_cube()`, `dft_index_expr()` +(+ `dft_index_table()`), `dft_rast_break()`. Plus a config restructure and a +network-decoupled vignette. **Whole pipeline on one branch, single 0.3.0 release.** + +## Phase 1: Config — role-based schema + Sentinel-2 source +- [ ] `R/dft_stac_config.R`: expand `match.arg` to `c("io-lulc","esa-worldcover","sentinel-2-l2a","landsat-c2-l2")`; add `sentinel-2-l2a` entry (`cube=TRUE`, `roles=list(red="B04",nir="B08",swir16="B11",mask="SCL")`, `mask_values=c(3L,8L,9L,10L)`, `scale=1e-4`, `offset=-0.1`, `available_datetime`); keep `io-lulc`/`esa-worldcover` 4-field **verbatim**; add `landsat-c2-l2` as a commented drop-in template (`red`/`nir08`, `qa_pixel`, scale 2.75e-5 / offset -0.2) +- [ ] `R/dft_stac_fetch.R`: friendly guard in config-resolve block — if `isTRUE(cfg$cube)` and `asset` still `NULL`, `cli::cli_abort("Source '{source}' is a cube source; use dft_stac_cube().")`; categorical path untouched +- [ ] `tests/testthat/test-dft_stac_config.R`: keep the 3 existing tests byte-for-byte; add cube-source assertions (`isTRUE(cfg$cube)`, role names + `$red=="B04"`/`$nir=="B08"`/`$mask=="SCL"`, `mask_values`, `scale`, `offset`, `available_datetime`); backward-compat assertion that `io-lulc` has no `cube` field + +## Phase 2: `dft_index_expr()` + table-driven index registry +- [ ] `inst/indices/indices.csv`: registry (`index,formula,roles,description`) — `ndvi`, `kndvi=tanh(pow((nir-red)/(nir+red),2))`, `ndmi`; formulas in tinyexpr C syntax over **roles**, self-contained (kNDVI inlines NDVI) +- [ ] `R/dft_index_expr.R`: `dft_index_table()` (reads the CSV, mirrors `dft_class_table()`); internal `index_resolve_expr(index, roles, scale, offset)` — pure string builder, per-role scaled token `(asset*scale+offset)` when scale≠1 or offset≠0 else bare asset, longest-role-first word-boundary substitution, unknown-index `cli_abort` listing available; internal `index_roles(index)`; exported `dft_index_expr(cube, index="kndvi", source="sentinel-2-l2a", roles=NULL, scale=NULL, offset=NULL)` → `gdalcubes::apply_pixel(cube, expr, names = index)` +- [ ] `tests/testthat/test-dft_index_expr.R` (pure local, no gdalcubes/network): kNDVI-over-S2 resolves to the exact scaled string; scale/offset on (Landsat) vs off (bare) tokens; `ndmi` resolves `swir16→B11`; unknown-index error + +## Phase 3: `dft_stac_cube()` +- [ ] `R/dft_stac_cube.R`: signature `dft_stac_cube(aoi, source="sentinel-2-l2a", index="kndvi", datetime=NULL, res=10, crs=NULL, dt="P1M", aggregation="median", resampling="bilinear", cloud_cover_max=60, mask_values=NULL, cache_dir=NULL, force=FALSE, sign_fn=rstac::sign_planetary_computer())`; `check_installed("gdalcubes")`; `stopifnot(isTRUE(cfg$cube))`; reuse `auto_utm_epsg`/sf-coercion/`dft_cache_path`; derive assets from `index_roles(index)` ∩ `cfg$roles` + `cfg$roles$mask`; STAC query with **`intersects`** (not bbox) + `ext_filter({{cloud_cover_max}})` + `post_request() |> items_fetch() |> items_sign()`; **explicit** `cube_view` extent from `bbox_target` + parsed `t0`/`t1`; `raster_cube(col, v, mask=image_mask(mask_asset, values=mask_values)) |> filter_geom(...)`; `dft_index_expr(...)`; materialize to `/cube_.nc` via `write_ncdf(overwrite=TRUE)`, return `gdalcubes::ncdf_cube(f)`; `force` overwrites +- [ ] Internal `stac_cube_cache_key()` (leave `stac_cache_key()` byte-for-byte unchanged — pinned by fetch tests): hash AOI-WKB + `res`/`target_crs`/`dt`/`aggregation`/`resampling`/`stac_url`/`collection` + cube additions `band_assets`/`datetime`/`index`/`cloud_cover_max`/`sort(mask_values)`/`scale`/`offset` +- [ ] `tests/testthat/test-dft_stac_cube.R`: local gdalcubes-missing guard (mirror fetch test); `stac_cube_cache_key` unit tests (determinism/12-hex, per-param sensitivity incl. index/datetime/cloud_cover_max/mask_values/scale/offset, sf-attrs ignored, 10L≡10); network E2E `skip_on_cran()` + `skip_if_offline()` + `skip_if_not_installed("gdalcubes")` (short window on `example_aoi.gpkg`, assert single `kndvi` band + cache-hit on second call) + +## Phase 4: `dft_rast_break()` — bfast reducer (empirical gate FIRST) +- [ ] **Smoke-test gate before writing the function**: install `bfast` (+ `strucchangeRcpp`); run the 4-leg smoke script (scratchpad/data-raw): (a) synthetic 24-pt monthly series w/ injected step drop → `bfastmonitor(start=c(2022,1))` gives finite `$breakpoint` ≈ injected date, `$magnitude<0` (pins ts/frequency/`c(year,period)` semantics); (b) synthetic local cube (`create_image_collection(date_time=, band_names="kndvi")`→`cube_view(dt="P1M")`→`raster_cube`) through `reduce_time(names=c("break_date","break_mag"), load_pkgs="bfast", FUN=…)`→`write_ncdf`→`terra::rast` gives `nlyr==2`, correct names, `x["kndvi",]` resolves in FUN, break pixel finite / all-NA pixel NA; (c) `dt="P1M"` + `frequency=1` yields garbage (motivates the guard); (d) tiny real-S2 leg via `dft_stac_cube()` returns a 2-band raster. Proceed only if all pass +- [ ] `R/dft_rast_break.R`: internal `.dft_break_pixel(v, start, frequency, ts_start, history, level, min_obs)` — degenerate all-NA / ` Date: Wed, 8 Jul 2026 16:59:00 -0700 Subject: [PATCH 02/16] Add role-based STAC config schema + Sentinel-2 cube source (#30) Restructure dft_stac_config() so cube-capable sources carry a role->asset map (red/nir/swir16/mask), mask values, and reflectance scale/offset, marked with cube = TRUE. Categorical sources (io-lulc, esa-worldcover) keep their flat 4-field shape verbatim (backward compat, pinned by expect_named); the cube marker is absence = FALSE. Add sentinel-2-l2a (Planetary Computer band names B04/B08/B11/SCL). Guard dft_stac_fetch() to reject cube sources with a pointer to dft_stac_cube(). Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- R/dft_stac_config.R | 69 ++++++++++++++++++++++----- R/dft_stac_fetch.R | 6 +++ man/dft_stac_config.Rd | 39 ++++++++++----- planning/active/progress.md | 7 ++- planning/active/task_plan.md | 6 +-- tests/testthat/test-dft_stac_config.R | 28 +++++++++++ 6 files changed, 125 insertions(+), 30 deletions(-) diff --git a/R/dft_stac_config.R b/R/dft_stac_config.R index 4fdc261..f4f7709 100644 --- a/R/dft_stac_config.R +++ b/R/dft_stac_config.R @@ -1,26 +1,39 @@ -#' Get STAC configuration for a known land cover source +#' Get STAC configuration for a known source #' #' Returns connection details for pre-configured STAC collections. -#' Used as a convenience wrapper around [dft_stac_fetch()] so users -#' don't need to remember STAC URLs and collection IDs. +#' Used as a convenience wrapper around [dft_stac_fetch()] (categorical +#' sources) and `dft_stac_cube()` (continuous index-trajectory sources) so +#' users don't need to remember STAC URLs, collection IDs, and band names. #' -#' @param source Character. One of `"io-lulc"` (Esri IO LULC annual v02) -#' or `"esa-worldcover"` (ESA WorldCover). +#' Sources are of two kinds. **Categorical** sources (`"io-lulc"`, +#' `"esa-worldcover"`) host single-band classified rasters and carry a flat +#' `asset` name for [dft_stac_fetch()]. **Cube** sources (`"sentinel-2-l2a"`) +#' host multi-band reflectance imagery and instead carry a role-based band map +#' (`red`/`nir`/`swir16`/`mask`), mask values, and reflectance scale/offset for +#' `dft_stac_cube()`; they are marked with `cube = TRUE`. The role-based schema +#' means a new reflectance source (e.g. Landsat C2 L2) drops in with no API +#' change — only the role→asset map and scale/offset differ. #' -#' @return A list with elements: -#' \describe{ -#' \item{stac_url}{STAC API endpoint} -#' \item{collection}{Collection ID} -#' \item{asset}{Asset name to download} -#' \item{available_years}{Integer vector of available years} -#' } +#' @param source Character. One of `"io-lulc"` (Esri IO LULC annual v02), +#' `"esa-worldcover"` (ESA WorldCover), or `"sentinel-2-l2a"` (Sentinel-2 +#' L2A surface reflectance, a cube source). +#' +#' @return A list. Categorical sources have elements `stac_url`, `collection`, +#' `asset`, `available_years`. Cube sources have `stac_url`, `collection`, +#' `cube = TRUE`, `roles` (a named list mapping `red`/`nir`/`swir16`/`mask` +#' to asset names), `mask_values` (integer mask classes to exclude), +#' `scale`/`offset` (DN → reflectance affine transform), and +#' `available_datetime` (an ISO 8601 interval string). The `cube` field is +#' absent (not `FALSE`) for categorical sources; test with `isTRUE(cfg$cube)`. #' #' @examples #' dft_stac_config("io-lulc") #' dft_stac_config("esa-worldcover") +#' dft_stac_config("sentinel-2-l2a") #' #' @export -dft_stac_config <- function(source = c("io-lulc", "esa-worldcover")) { +dft_stac_config <- function(source = c("io-lulc", "esa-worldcover", + "sentinel-2-l2a")) { source <- match.arg(source) switch(source, "io-lulc" = list( @@ -34,6 +47,36 @@ dft_stac_config <- function(source = c("io-lulc", "esa-worldcover")) { collection = "esa-worldcover", asset = "map", available_years = c(2020L, 2021L) + ), + "sentinel-2-l2a" = list( + stac_url = "https://planetarycomputer.microsoft.com/api/stac/v1", + collection = "sentinel-2-l2a", + cube = TRUE, + # Planetary Computer asset names (NOT the Element84/AWS red/nir/scl names) + roles = list( + red = "B04", + nir = "B08", + swir16 = "B11", + mask = "SCL" + ), + # Scene Classification Layer classes to mask out: + # 3 cloud shadow, 8 cloud medium, 9 cloud high, 10 thin cirrus + mask_values = c(3L, 8L, 9L, 10L), + # DN -> reflectance: baseline 04.00 (+1000 DN) => DN * 1e-4 - 0.1 + scale = 1e-4, + offset = -0.1, + available_datetime = "2017-01-01/2024-12-31" ) + # "landsat-c2-l2" drops in with the same cube shape, no API change: + # list( + # stac_url = "https://planetarycomputer.microsoft.com/api/stac/v1", + # collection = "landsat-c2-l2", + # cube = TRUE, + # roles = list(red = "red", nir = "nir08", + # swir16 = "swir16", mask = "qa_pixel"), + # mask_values = , + # scale = 0.0000275, offset = -0.2, # non-zero offset breaks ratio + # available_datetime = "1984-01-01/2024-12-31" # indices on raw DNs + # ) ) } diff --git a/R/dft_stac_fetch.R b/R/dft_stac_fetch.R index e72e202..392f116 100644 --- a/R/dft_stac_fetch.R +++ b/R/dft_stac_fetch.R @@ -61,6 +61,12 @@ dft_stac_fetch <- function(aoi, # Resolve config if (is.null(stac_url) || is.null(collection) || is.null(asset)) { cfg <- dft_stac_config(source) + if (isTRUE(cfg$cube)) { + cli::cli_abort(c( + "Source {.val {source}} is a cube source, not a categorical raster.", + "i" = "Use {.fn dft_stac_cube} for continuous index-trajectory sources." + )) + } stac_url <- stac_url %||% cfg$stac_url collection <- collection %||% cfg$collection asset <- asset %||% cfg$asset diff --git a/man/dft_stac_config.Rd b/man/dft_stac_config.Rd index 1790af8..ddbd4af 100644 --- a/man/dft_stac_config.Rd +++ b/man/dft_stac_config.Rd @@ -2,30 +2,43 @@ % Please edit documentation in R/dft_stac_config.R \name{dft_stac_config} \alias{dft_stac_config} -\title{Get STAC configuration for a known land cover source} +\title{Get STAC configuration for a known source} \usage{ -dft_stac_config(source = c("io-lulc", "esa-worldcover")) +dft_stac_config(source = c("io-lulc", "esa-worldcover", "sentinel-2-l2a")) } \arguments{ -\item{source}{Character. One of \code{"io-lulc"} (Esri IO LULC annual v02) -or \code{"esa-worldcover"} (ESA WorldCover).} +\item{source}{Character. One of \code{"io-lulc"} (Esri IO LULC annual v02), +\code{"esa-worldcover"} (ESA WorldCover), or \code{"sentinel-2-l2a"} (Sentinel-2 +L2A surface reflectance, a cube source).} } \value{ -A list with elements: -\describe{ -\item{stac_url}{STAC API endpoint} -\item{collection}{Collection ID} -\item{asset}{Asset name to download} -\item{available_years}{Integer vector of available years} -} +A list. Categorical sources have elements \code{stac_url}, \code{collection}, +\code{asset}, \code{available_years}. Cube sources have \code{stac_url}, \code{collection}, +\code{cube = TRUE}, \code{roles} (a named list mapping \code{red}/\code{nir}/\code{swir16}/\code{mask} +to asset names), \code{mask_values} (integer mask classes to exclude), +\code{scale}/\code{offset} (DN → reflectance affine transform), and +\code{available_datetime} (an ISO 8601 interval string). The \code{cube} field is +absent (not \code{FALSE}) for categorical sources; test with \code{isTRUE(cfg$cube)}. } \description{ Returns connection details for pre-configured STAC collections. -Used as a convenience wrapper around \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} so users -don't need to remember STAC URLs and collection IDs. +Used as a convenience wrapper around \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} (categorical +sources) and \code{dft_stac_cube()} (continuous index-trajectory sources) so +users don't need to remember STAC URLs, collection IDs, and band names. +} +\details{ +Sources are of two kinds. \strong{Categorical} sources (\code{"io-lulc"}, +\code{"esa-worldcover"}) host single-band classified rasters and carry a flat +\code{asset} name for \code{\link[=dft_stac_fetch]{dft_stac_fetch()}}. \strong{Cube} sources (\code{"sentinel-2-l2a"}) +host multi-band reflectance imagery and instead carry a role-based band map +(\code{red}/\code{nir}/\code{swir16}/\code{mask}), mask values, and reflectance scale/offset for +\code{dft_stac_cube()}; they are marked with \code{cube = TRUE}. The role-based schema +means a new reflectance source (e.g. Landsat C2 L2) drops in with no API +change — only the role→asset map and scale/offset differ. } \examples{ dft_stac_config("io-lulc") dft_stac_config("esa-worldcover") +dft_stac_config("sentinel-2-l2a") } diff --git a/planning/active/progress.md b/planning/active/progress.md index 1c194ca..8b6f3cd 100644 --- a/planning/active/progress.md +++ b/planning/active/progress.md @@ -9,4 +9,9 @@ - Empirically verified the gdalcubes/rstac/terra APIs read-only (see findings.md). - Created branch `30-continuous-index-trajectory-change-detec` off main. - Scaffolded PWF baseline from issue #30 with approved phases. -- Next: start Phase 1 (config restructure + Sentinel-2 source + tests). +- **Phase 1 done** (config role-based schema + sentinel-2-l2a + fetch guard + tests). + Fresh-eyes code-check caught a dangling `[dft_stac_cube()]` roxygen link (R CMD + check WARNING) → downgraded to code spans until Phase 3; hardened the fetch guard + to key on `isTRUE(cfg$cube)` alone. 27 config + 16 fetch assertions green. +- Next: Phase 2 (index registry + `dft_index_expr()`) — code + tests already written + (15 assertions green), pending its own code-check + commit. diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md index f547681..8dab939 100644 --- a/planning/active/task_plan.md +++ b/planning/active/task_plan.md @@ -15,9 +15,9 @@ toolchain. Four new exports: `dft_stac_cube()`, `dft_index_expr()` network-decoupled vignette. **Whole pipeline on one branch, single 0.3.0 release.** ## Phase 1: Config — role-based schema + Sentinel-2 source -- [ ] `R/dft_stac_config.R`: expand `match.arg` to `c("io-lulc","esa-worldcover","sentinel-2-l2a","landsat-c2-l2")`; add `sentinel-2-l2a` entry (`cube=TRUE`, `roles=list(red="B04",nir="B08",swir16="B11",mask="SCL")`, `mask_values=c(3L,8L,9L,10L)`, `scale=1e-4`, `offset=-0.1`, `available_datetime`); keep `io-lulc`/`esa-worldcover` 4-field **verbatim**; add `landsat-c2-l2` as a commented drop-in template (`red`/`nir08`, `qa_pixel`, scale 2.75e-5 / offset -0.2) -- [ ] `R/dft_stac_fetch.R`: friendly guard in config-resolve block — if `isTRUE(cfg$cube)` and `asset` still `NULL`, `cli::cli_abort("Source '{source}' is a cube source; use dft_stac_cube().")`; categorical path untouched -- [ ] `tests/testthat/test-dft_stac_config.R`: keep the 3 existing tests byte-for-byte; add cube-source assertions (`isTRUE(cfg$cube)`, role names + `$red=="B04"`/`$nir=="B08"`/`$mask=="SCL"`, `mask_values`, `scale`, `offset`, `available_datetime`); backward-compat assertion that `io-lulc` has no `cube` field +- [x] `R/dft_stac_config.R`: expand `match.arg` to `c("io-lulc","esa-worldcover","sentinel-2-l2a")`; add `sentinel-2-l2a` entry (`cube=TRUE`, `roles=list(red="B04",nir="B08",swir16="B11",mask="SCL")`, `mask_values=c(3L,8L,9L,10L)`, `scale=1e-4`, `offset=-0.1`, `available_datetime`); keep `io-lulc`/`esa-worldcover` 4-field **verbatim**; add `landsat-c2-l2` as a commented drop-in template (`red`/`nir08`, `qa_pixel`, scale 2.75e-5 / offset -0.2). NOTE: `landsat-c2-l2` left OUT of `match.arg` (in match.arg but no switch case = silent NULL); it stays a commented template until implemented +- [x] `R/dft_stac_fetch.R`: friendly guard in config-resolve block — if `isTRUE(cfg$cube)`, `cli::cli_abort(... use dft_stac_cube())`; keyed on `cube` alone (not `&& is.null(asset)`) so a cube source never falls through to an opaque `stopifnot(years)`; categorical path untouched +- [x] `tests/testthat/test-dft_stac_config.R`: keep the 3 existing tests byte-for-byte; add cube-source assertions (`isTRUE(cfg$cube)`, role names + `$red=="B04"`/`$nir=="B08"`/`$mask=="SCL"`, `mask_values`, `scale`, `offset`, `available_datetime`); backward-compat assertion that `io-lulc`/`esa-worldcover` have no `cube` field ## Phase 2: `dft_index_expr()` + table-driven index registry - [ ] `inst/indices/indices.csv`: registry (`index,formula,roles,description`) — `ndvi`, `kndvi=tanh(pow((nir-red)/(nir+red),2))`, `ndmi`; formulas in tinyexpr C syntax over **roles**, self-contained (kNDVI inlines NDVI) diff --git a/tests/testthat/test-dft_stac_config.R b/tests/testthat/test-dft_stac_config.R index 87e335f..a228fc0 100644 --- a/tests/testthat/test-dft_stac_config.R +++ b/tests/testthat/test-dft_stac_config.R @@ -20,3 +20,31 @@ test_that("dft_stac_config returns valid config for esa-worldcover", { test_that("dft_stac_config errors on unknown source", { expect_error(dft_stac_config("bogus")) }) + +test_that("categorical sources carry no cube marker (absence = FALSE)", { + # backward-compat: the flat 4-field shape is pinned by expect_named above; + # consumers branch on isTRUE(cfg$cube), which must be FALSE here + expect_false(isTRUE(dft_stac_config("io-lulc")$cube)) + expect_false(isTRUE(dft_stac_config("esa-worldcover")$cube)) +}) + +test_that("dft_stac_config returns role-based cube config for sentinel-2-l2a", { + cfg <- dft_stac_config("sentinel-2-l2a") + expect_type(cfg, "list") + expect_true(isTRUE(cfg$cube)) + expect_equal(cfg$collection, "sentinel-2-l2a") + expect_match(cfg$stac_url, "planetarycomputer") + + # role -> asset map uses Planetary Computer band names (B04/B08/B11/SCL) + expect_named(cfg$roles, c("red", "nir", "swir16", "mask")) + expect_equal(cfg$roles$red, "B04") + expect_equal(cfg$roles$nir, "B08") + expect_equal(cfg$roles$swir16, "B11") + expect_equal(cfg$roles$mask, "SCL") + + # mask values, reflectance affine transform, temporal extent + expect_equal(cfg$mask_values, c(3L, 8L, 9L, 10L)) + expect_equal(cfg$scale, 1e-4) + expect_equal(cfg$offset, -0.1) + expect_match(cfg$available_datetime, "^\\d{4}-\\d{2}-\\d{2}/\\d{4}-\\d{2}-\\d{2}$") +}) From 96076df88521d2b9d7dcc7a054e85d8fafbd428a Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 17:12:57 -0700 Subject: [PATCH 03/16] Add index registry + dft_stac_cube() index-cube fetch (#30) Phase 2 (index registry): inst/indices/indices.csv holds spectral-index formulas over band roles in gdalcubes tinyexpr C syntax (pow(), not ^). dft_index_table() reads it (mirrors dft_class_table); dft_index_expr() resolves roles to per-source assets, folds the reflectance scale/offset into each band token (a non-zero offset does not cancel in a ratio index), and applies the expression to a cube. Phase 3 (cube fetch): dft_stac_cube() is the trajectory sibling of dft_stac_fetch() -- STAC query with intersects (unioned AOI) + eo:cloud_cover scene pre-filter + items_fetch pagination, an explicit AOI-bounded cube_view, SCL cloud masking, the index applied, and the masked index cube materialized to /cube_.nc keyed by every cube-affecting parameter. Returns the lazy cube for dft_rast_break(). Fresh-eyes code-check on each: the cube's intersects used only the first AOI feature (silent NoData holes for multi-feature AOIs) -- fixed by unioning. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- NAMESPACE | 3 + R/dft_index_expr.R | 143 ++++++++++++++++++ R/dft_stac_config.R | 4 +- R/dft_stac_cube.R | 215 +++++++++++++++++++++++++++ R/drift-package.R | 3 + inst/indices/indices.csv | 4 + man/dft_index_expr.Rd | 68 +++++++++ man/dft_index_table.Rd | 20 +++ man/dft_stac_config.Rd | 4 +- man/dft_stac_cube.Rd | 112 ++++++++++++++ planning/active/findings.md | 43 ++++++ planning/active/progress.md | 13 +- planning/active/task_plan.md | 12 +- tests/testthat/test-dft_index_expr.R | 61 ++++++++ tests/testthat/test-dft_stac_cube.R | 100 +++++++++++++ 15 files changed, 793 insertions(+), 12 deletions(-) create mode 100644 R/dft_index_expr.R create mode 100644 R/dft_stac_cube.R create mode 100644 inst/indices/indices.csv create mode 100644 man/dft_index_expr.Rd create mode 100644 man/dft_index_table.Rd create mode 100644 man/dft_stac_cube.Rd create mode 100644 tests/testthat/test-dft_index_expr.R create mode 100644 tests/testthat/test-dft_stac_cube.R diff --git a/NAMESPACE b/NAMESPACE index 7a39c4a..9d3a6e8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,6 +4,8 @@ export(dft_cache_clear) export(dft_cache_info) export(dft_cache_path) export(dft_class_table) +export(dft_index_expr) +export(dft_index_table) export(dft_map_interactive) export(dft_rast_classify) export(dft_rast_consensus) @@ -11,5 +13,6 @@ export(dft_rast_summarize) export(dft_rast_transition) export(dft_stac_classes) export(dft_stac_config) +export(dft_stac_cube) export(dft_stac_fetch) export(dft_transition_vectors) diff --git a/R/dft_index_expr.R b/R/dft_index_expr.R new file mode 100644 index 0000000..f117fca --- /dev/null +++ b/R/dft_index_expr.R @@ -0,0 +1,143 @@ +#' Apply a spectral index to a data cube +#' +#' Resolve a named spectral index (e.g. `"kndvi"`) into a per-pixel arithmetic +#' expression over a source's band roles and apply it to a `gdalcubes` data +#' cube, returning a single-band cube named after the index. +#' +#' Index formulas are stored in a shipped registry ([dft_index_table()]) written +#' over band **roles** (`red`, `nir`, `swir16`), not literal asset names. The +#' roles are resolved to per-source asset names via [dft_stac_config()], so the +#' same `"kndvi"` works against Sentinel-2 (`B04`/`B08`) or any future +#' reflectance source without changing the formula. +#' +#' Reflectance `scale`/`offset` are folded **into** the expression as a per-band +#' affine transform `(asset * scale + offset)`. This matters for ratio indices: +#' a non-zero offset does not cancel in `(nir - red)/(nir + red)`, so computing +#' NDVI on raw digital numbers is wrong for sources with an offset (Landsat C2 +#' L2, or Sentinel-2 processing baseline 04.00). +#' +#' @param cube A `gdalcubes` data cube (e.g. the lazy cube built inside +#' [dft_stac_cube()]) whose bands are the source's assets. +#' @param index Character. An index name present in [dft_index_table()] +#' (default `"kndvi"`). +#' @param source Character. Source name passed to [dft_stac_config()] to resolve +#' the role→asset map and reflectance scale/offset (default +#' `"sentinel-2-l2a"`). +#' @param roles Named list mapping roles to asset names. When `NULL`, taken from +#' `dft_stac_config(source)$roles`. +#' @param scale,offset Numeric reflectance affine transform. When `NULL`, taken +#' from the source config (falling back to `1` / `0`). +#' +#' @return A single-band `gdalcubes` cube with the band named `index`. +#' +#' @seealso [dft_index_table()] for the registry, [dft_stac_cube()] for the +#' caller that builds the input cube. +#' +#' @examples +#' # The registry the resolver reads: +#' dft_index_table() +#' +#' \dontrun{ +#' # Applied to a lazy Sentinel-2 cube (requires network + gdalcubes): +#' aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +#' cube <- dft_stac_cube(aoi, index = "kndvi") # dft_stac_cube calls this internally +#' } +#' +#' @export +dft_index_expr <- function(cube, + index = "kndvi", + source = "sentinel-2-l2a", + roles = NULL, + scale = NULL, + offset = NULL) { + rlang::check_installed("gdalcubes", reason = "to apply an index to a cube") + cfg <- dft_stac_config(source) + roles <- roles %||% cfg$roles + scale <- scale %||% cfg$scale %||% 1 + offset <- offset %||% cfg$offset %||% 0 + expr <- index_resolve_expr(index, roles, scale, offset) + gdalcubes::apply_pixel(cube, expr, names = index) +} + +#' Load the shipped spectral-index registry +#' +#' Reads the CSV index registry bundled with the package. Each row defines one +#' index as a `gdalcubes`/tinyexpr formula written over band roles. +#' +#' @return A tibble with columns `index`, `formula`, `roles` (comma-separated +#' role names), and `description`. +#' +#' @examples +#' dft_index_table() +#' +#' @export +dft_index_table <- function() { + path <- system.file("indices", "indices.csv", package = "drift", mustWork = TRUE) + tibble::as_tibble(utils::read.csv(path, stringsAsFactors = FALSE)) +} + +#' Look up one index registry row, erroring on unknown index +#' @noRd +index_row <- function(index) { + tbl <- dft_index_table() + row <- tbl[tbl$index == index, ] + if (nrow(row) == 0) { + cli::cli_abort(c( + "Unknown index {.val {index}}.", + "i" = "Available indices: {.val {tbl$index}}." + )) + } + row +} + +#' Band roles required by an index (internal) +#' +#' Used by [dft_stac_cube()] to decide which assets to pull. +#' @noRd +index_roles <- function(index) { + trimws(strsplit(index_row(index)$roles[[1]], ",")[[1]]) +} + +#' Build a per-band affine reflectance token for an expression (internal) +#' +#' Returns the bare asset name when the transform is identity (`scale == 1`, +#' `offset == 0`); otherwise `(asset * scale +/- |offset|)`. Numbers are +#' formatted without scientific notation so the tinyexpr C parser accepts them. +#' @noRd +scale_token <- function(asset, scale, offset) { + if (scale == 1 && offset == 0) return(asset) + core <- if (scale == 1) { + asset + } else { + sprintf("%s * %s", asset, format(scale, scientific = FALSE, trim = TRUE)) + } + if (offset == 0) return(sprintf("(%s)", core)) + sign <- if (offset < 0) "-" else "+" + sprintf("(%s %s %s)", core, sign, + format(abs(offset), scientific = FALSE, trim = TRUE)) +} + +#' Resolve an index name to a per-pixel expression string (internal) +#' +#' Substitutes each role token in the registry formula with its scaled asset +#' token. Roles are substituted longest-name-first with word boundaries so a +#' shorter role name cannot clobber part of a longer one. +#' @noRd +index_resolve_expr <- function(index, roles, scale = 1, offset = 0) { + row <- index_row(index) + formula <- row$formula[[1]] + needed <- trimws(strsplit(row$roles[[1]], ",")[[1]]) + needed <- needed[order(nchar(needed), decreasing = TRUE)] + for (role in needed) { + asset <- roles[[role]] + if (is.null(asset)) { + cli::cli_abort(c( + "Index {.val {index}} needs role {.val {role}}, absent from the role map.", + "i" = "Available roles: {.val {names(roles)}}." + )) + } + formula <- gsub(paste0("\\b", role, "\\b"), + scale_token(asset, scale, offset), formula) + } + formula +} diff --git a/R/dft_stac_config.R b/R/dft_stac_config.R index f4f7709..86a9905 100644 --- a/R/dft_stac_config.R +++ b/R/dft_stac_config.R @@ -2,7 +2,7 @@ #' #' Returns connection details for pre-configured STAC collections. #' Used as a convenience wrapper around [dft_stac_fetch()] (categorical -#' sources) and `dft_stac_cube()` (continuous index-trajectory sources) so +#' sources) and [dft_stac_cube()] (continuous index-trajectory sources) so #' users don't need to remember STAC URLs, collection IDs, and band names. #' #' Sources are of two kinds. **Categorical** sources (`"io-lulc"`, @@ -10,7 +10,7 @@ #' `asset` name for [dft_stac_fetch()]. **Cube** sources (`"sentinel-2-l2a"`) #' host multi-band reflectance imagery and instead carry a role-based band map #' (`red`/`nir`/`swir16`/`mask`), mask values, and reflectance scale/offset for -#' `dft_stac_cube()`; they are marked with `cube = TRUE`. The role-based schema +#' [dft_stac_cube()]; they are marked with `cube = TRUE`. The role-based schema #' means a new reflectance source (e.g. Landsat C2 L2) drops in with no API #' change — only the role→asset map and scale/offset differ. #' diff --git a/R/dft_stac_cube.R b/R/dft_stac_cube.R new file mode 100644 index 0000000..9aa5c53 --- /dev/null +++ b/R/dft_stac_cube.R @@ -0,0 +1,215 @@ +#' Fetch a masked spectral-index cube from a STAC catalog +#' +#' Sibling of [dft_stac_fetch()] for continuous change detection. Where +#' [dft_stac_fetch()] materializes one categorical raster per year, this builds +#' a multi-band, sub-annual reflectance cube, masks clouds, computes a spectral +#' index over band roles, and returns the lazy single-band index cube — the +#' input to [dft_rast_break()] for per-pixel trajectory breakpoint detection. +#' +#' The index cube is materialized once to a NetCDF file under [dft_cache_path()] +#' as `/cube_.nc` (the full time axis, one band), keyed by a hash of +#' the AOI geometry and every cube-affecting parameter. Because the cube is +#' invariant to [dft_rast_break()]'s parameters (`history`/`start`/`level`), +#' caching it here makes bfast parameter sweeps cheap — they re-read the local +#' `.nc` instead of re-streaming COGs. +#' +#' Three STAC-query specifics distinguish cube mode from [dft_stac_fetch()]: +#' pagination via [rstac::items_fetch()] is mandatory (a monthly multi-year query +#' returns hundreds of items; a single page silently truncates); the query uses +#' `intersects` with the AOI geometry, not a bounding box (floodplain polygons +#' are highly non-rectangular); and a scene-level `eo:cloud_cover` pre-filter +#' shrinks the collection before any pixel is read, complementing per-pixel mask +#' filtering. +#' +#' @param aoi An `sf` polygon defining the area of interest. +#' @param source Character. A cube source name for [dft_stac_config()] (default +#' `"sentinel-2-l2a"`). Must be a source with `cube = TRUE`. +#' @param index Character. Spectral index from [dft_index_table()] (default +#' `"kndvi"`). Determines which band roles (and thus assets) are fetched. +#' @param datetime Character. ISO 8601 interval `"start/end"`. When `NULL`, uses +#' `available_datetime` from [dft_stac_config()]. +#' @param res Numeric. Output pixel size in CRS units (default 10). +#' @param crs Character. Target CRS as an EPSG string. When `NULL`, auto-detected +#' from the AOI centroid's UTM zone. +#' @param dt Character. ISO 8601 duration for the temporal aggregation window +#' (default `"P1M"`, monthly). The cadence [dft_rast_break()]'s `frequency` +#' must agree with. +#' @param aggregation Character. Temporal aggregation for multiple scenes in one +#' `dt` window (default `"median"`). +#' @param resampling Character. Spatial resampling (default `"bilinear"`). +#' @param cloud_cover_max Numeric. Scene-level `eo:cloud_cover` maximum percent +#' for the STAC pre-filter (default 60). +#' @param mask_values Integer vector of mask-band classes to exclude. When +#' `NULL`, uses `mask_values` from [dft_stac_config()] (e.g. Sentinel-2 SCL +#' cloud / shadow / cirrus classes). +#' @param cache_dir Character. Cache directory. When `NULL`, uses +#' [dft_cache_path()]. +#' @param force Logical. Re-fetch even if cached, overwriting the cached `.nc` +#' (default `FALSE`). +#' @param sign_fn A signing function for STAC assets. Default is +#' [rstac::sign_planetary_computer()]. +#' +#' @return A `gdalcubes` data cube with a single band named `index`, backed by +#' the cached NetCDF file. +#' +#' @seealso [dft_rast_break()] (the reducer that consumes this cube), +#' [dft_index_expr()] (the index applied), [dft_stac_fetch()] (categorical +#' sibling). +#' +#' @examples +#' \dontrun{ +#' # Monthly kNDVI cube for a floodplain reach (requires network + gdalcubes) +#' aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +#' cube <- dft_stac_cube( +#' aoi, +#' source = "sentinel-2-l2a", +#' index = "kndvi", +#' datetime = "2019-01-01/2023-12-31", +#' dt = "P1M" +#' ) +#' breaks <- dft_rast_break(cube, start = c(2022, 1)) +#' } +#' +#' @export +dft_stac_cube <- function(aoi, + source = "sentinel-2-l2a", + index = "kndvi", + datetime = NULL, + res = 10, + crs = NULL, + dt = "P1M", + aggregation = "median", + resampling = "bilinear", + cloud_cover_max = 60, + mask_values = NULL, + cache_dir = NULL, + force = FALSE, + sign_fn = rstac::sign_planetary_computer()) { + rlang::check_installed("gdalcubes", reason = "to fetch STAC cubes") + + cfg <- dft_stac_config(source) + if (!isTRUE(cfg$cube)) { + cli::cli_abort(c( + "Source {.val {source}} is not a cube source.", + "i" = "Use {.fn dft_stac_fetch} for categorical rasters." + )) + } + datetime <- datetime %||% cfg$available_datetime + mask_values <- mask_values %||% cfg$mask_values + scale <- cfg$scale %||% 1 + offset <- cfg$offset %||% 0 + + # Ensure aoi is sf + if (inherits(aoi, "SpatVector")) aoi <- sf::st_as_sf(aoi) + stopifnot(inherits(aoi, c("sf", "sfc"))) + + target_crs <- if (is.null(crs)) auto_utm_epsg(aoi) else crs + aoi_wgs84 <- sf::st_transform(aoi, 4326) + aoi_target <- sf::st_transform(aoi, as.integer(gsub("EPSG:", "", target_crs))) + bbox_target <- sf::st_bbox(aoi_target) + + # Assets: the index's required roles + the mask role (index_roles errors on + # an unknown index before any network call) + roles_needed <- index_roles(index) + band_assets <- unlist(cfg$roles[roles_needed], use.names = FALSE) + mask_asset <- cfg$roles$mask + + # datetime interval -> cube_view time bounds + dr <- strsplit(datetime, "/", fixed = TRUE)[[1]] + if (length(dr) != 2) { + cli::cli_abort("`datetime` must be an ISO 8601 interval {.val start/end}.") + } + t0 <- dr[1] + t1 <- dr[2] + + # Cache + cache_base <- dft_cache_path(cache_dir) + cache_source_dir <- file.path(cache_base, source) + dir.create(cache_source_dir, recursive = TRUE, showWarnings = FALSE) + cache_key <- stac_cube_cache_key( + aoi_target, res, target_crs, dt, aggregation, resampling, + cfg$stac_url, cfg$collection, band_assets, datetime, index, + cloud_cover_max, mask_values, scale, offset + ) + cache_file <- file.path(cache_source_dir, paste0("cube_", cache_key, ".nc")) + + if (!force && file.exists(cache_file)) { + message(" cube: cached") + return(gdalcubes::ncdf_cube(cache_file)) + } + + # STAC query: intersects (not bbox) + scene cloud pre-filter + pagination + message("Querying STAC: ", cfg$collection, " (", datetime, ")...") + items <- rstac::stac(cfg$stac_url) |> + rstac::stac_search( + collections = cfg$collection, + # union so a multi-feature AOI queries its whole footprint, matching the + # cube extent and filter_geom clip (a single first-feature geometry would + # leave silent NoData holes over the other features) + intersects = sf::st_geometry(sf::st_union(aoi_wgs84))[[1]], + datetime = datetime, + limit = 500 + ) |> + rstac::ext_filter(`eo:cloud_cover` <= {{cloud_cover_max}}) |> + rstac::post_request() |> + rstac::items_fetch() |> + rstac::items_sign(sign_fn = sign_fn) + + n_items <- length(items$features) + message(" ", n_items, " items returned") + if (n_items == 0) stop("No STAC items found for ", cfg$collection) + + # image_mask masks the categorical mask band at read time, before the + # cube_view resampling touches reflectance bands + col <- gdalcubes::stac_image_collection( + items$features, asset_names = c(band_assets, mask_asset) + ) + + v <- gdalcubes::cube_view( + srs = target_crs, + extent = list( + left = bbox_target[["xmin"]], right = bbox_target[["xmax"]], + bottom = bbox_target[["ymin"]], top = bbox_target[["ymax"]], + t0 = t0, t1 = t1 + ), + dx = res, dy = res, dt = dt, + aggregation = aggregation, resampling = resampling + ) + + cube <- gdalcubes::raster_cube( + col, v, + mask = gdalcubes::image_mask(mask_asset, values = mask_values) + ) |> + gdalcubes::filter_geom(sf::st_geometry(aoi_target)) + + idx <- dft_index_expr(cube, index = index, source = source, + roles = cfg$roles, scale = scale, offset = offset) + + gdalcubes::write_ncdf(idx, cache_file, overwrite = TRUE) + gdalcubes::ncdf_cube(cache_file) +} + + +#' Cache key for one STAC index-cube parameter set +#' +#' Cube-mode analogue of [stac_cache_key()] (kept separate so the fetch key +#' stays byte-for-byte stable). Hashes the AOI geometry as WKB plus every +#' parameter that changes the written index cube. `res` is coerced to double so +#' `10L` and `10` key alike; `mask_values` is sorted so order does not matter. +#' `scale`/`offset` are included because they change pixel values. +#' @noRd +stac_cube_cache_key <- function(aoi_target, res, target_crs, dt, aggregation, + resampling, stac_url, collection, band_assets, + datetime, index, cloud_cover_max, mask_values, + scale, offset) { + geom_wkb <- sf::st_as_binary(sf::st_geometry(aoi_target), endian = "little") + substr( + rlang::hash(list( + geom_wkb, as.numeric(res), target_crs, dt, aggregation, resampling, + stac_url, collection, band_assets, datetime, index, + as.numeric(cloud_cover_max), sort(as.numeric(mask_values)), + as.numeric(scale), as.numeric(offset) + )), + 1, 12 + ) +} diff --git a/R/drift-package.R b/R/drift-package.R index 0cc9deb..0f21a73 100644 --- a/R/drift-package.R +++ b/R/drift-package.R @@ -1,2 +1,5 @@ #' @keywords internal "_PACKAGE" + +# NSE symbol used in rstac::ext_filter() CQL2 expressions (dft_stac_cube) +utils::globalVariables("eo:cloud_cover") diff --git a/inst/indices/indices.csv b/inst/indices/indices.csv new file mode 100644 index 0000000..26f0c21 --- /dev/null +++ b/inst/indices/indices.csv @@ -0,0 +1,4 @@ +index,formula,roles,description +ndvi,"(nir - red) / (nir + red)","nir,red",Normalized Difference Vegetation Index +kndvi,"tanh(pow((nir - red) / (nir + red), 2))","nir,red",Kernel NDVI (tanh of NDVI squared) +ndmi,"(nir - swir16) / (nir + swir16)","nir,swir16",Normalized Difference Moisture Index diff --git a/man/dft_index_expr.Rd b/man/dft_index_expr.Rd new file mode 100644 index 0000000..5efbb47 --- /dev/null +++ b/man/dft_index_expr.Rd @@ -0,0 +1,68 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dft_index_expr.R +\name{dft_index_expr} +\alias{dft_index_expr} +\title{Apply a spectral index to a data cube} +\usage{ +dft_index_expr( + cube, + index = "kndvi", + source = "sentinel-2-l2a", + roles = NULL, + scale = NULL, + offset = NULL +) +} +\arguments{ +\item{cube}{A \code{gdalcubes} data cube (e.g. the lazy cube built inside +\code{\link[=dft_stac_cube]{dft_stac_cube()}}) whose bands are the source's assets.} + +\item{index}{Character. An index name present in \code{\link[=dft_index_table]{dft_index_table()}} +(default \code{"kndvi"}).} + +\item{source}{Character. Source name passed to \code{\link[=dft_stac_config]{dft_stac_config()}} to resolve +the role→asset map and reflectance scale/offset (default +\code{"sentinel-2-l2a"}).} + +\item{roles}{Named list mapping roles to asset names. When \code{NULL}, taken from +\code{dft_stac_config(source)$roles}.} + +\item{scale, offset}{Numeric reflectance affine transform. When \code{NULL}, taken +from the source config (falling back to \code{1} / \code{0}).} +} +\value{ +A single-band \code{gdalcubes} cube with the band named \code{index}. +} +\description{ +Resolve a named spectral index (e.g. \code{"kndvi"}) into a per-pixel arithmetic +expression over a source's band roles and apply it to a \code{gdalcubes} data +cube, returning a single-band cube named after the index. +} +\details{ +Index formulas are stored in a shipped registry (\code{\link[=dft_index_table]{dft_index_table()}}) written +over band \strong{roles} (\code{red}, \code{nir}, \code{swir16}), not literal asset names. The +roles are resolved to per-source asset names via \code{\link[=dft_stac_config]{dft_stac_config()}}, so the +same \code{"kndvi"} works against Sentinel-2 (\code{B04}/\code{B08}) or any future +reflectance source without changing the formula. + +Reflectance \code{scale}/\code{offset} are folded \strong{into} the expression as a per-band +affine transform \code{(asset * scale + offset)}. This matters for ratio indices: +a non-zero offset does not cancel in \code{(nir - red)/(nir + red)}, so computing +NDVI on raw digital numbers is wrong for sources with an offset (Landsat C2 +L2, or Sentinel-2 processing baseline 04.00). +} +\examples{ +# The registry the resolver reads: +dft_index_table() + +\dontrun{ +# Applied to a lazy Sentinel-2 cube (requires network + gdalcubes): +aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +cube <- dft_stac_cube(aoi, index = "kndvi") # dft_stac_cube calls this internally +} + +} +\seealso{ +\code{\link[=dft_index_table]{dft_index_table()}} for the registry, \code{\link[=dft_stac_cube]{dft_stac_cube()}} for the +caller that builds the input cube. +} diff --git a/man/dft_index_table.Rd b/man/dft_index_table.Rd new file mode 100644 index 0000000..4cb983c --- /dev/null +++ b/man/dft_index_table.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dft_index_expr.R +\name{dft_index_table} +\alias{dft_index_table} +\title{Load the shipped spectral-index registry} +\usage{ +dft_index_table() +} +\value{ +A tibble with columns \code{index}, \code{formula}, \code{roles} (comma-separated +role names), and \code{description}. +} +\description{ +Reads the CSV index registry bundled with the package. Each row defines one +index as a \code{gdalcubes}/tinyexpr formula written over band roles. +} +\examples{ +dft_index_table() + +} diff --git a/man/dft_stac_config.Rd b/man/dft_stac_config.Rd index ddbd4af..1e5d112 100644 --- a/man/dft_stac_config.Rd +++ b/man/dft_stac_config.Rd @@ -23,7 +23,7 @@ absent (not \code{FALSE}) for categorical sources; test with \code{isTRUE(cfg$cu \description{ Returns connection details for pre-configured STAC collections. Used as a convenience wrapper around \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} (categorical -sources) and \code{dft_stac_cube()} (continuous index-trajectory sources) so +sources) and \code{\link[=dft_stac_cube]{dft_stac_cube()}} (continuous index-trajectory sources) so users don't need to remember STAC URLs, collection IDs, and band names. } \details{ @@ -32,7 +32,7 @@ Sources are of two kinds. \strong{Categorical} sources (\code{"io-lulc"}, \code{asset} name for \code{\link[=dft_stac_fetch]{dft_stac_fetch()}}. \strong{Cube} sources (\code{"sentinel-2-l2a"}) host multi-band reflectance imagery and instead carry a role-based band map (\code{red}/\code{nir}/\code{swir16}/\code{mask}), mask values, and reflectance scale/offset for -\code{dft_stac_cube()}; they are marked with \code{cube = TRUE}. The role-based schema +\code{\link[=dft_stac_cube]{dft_stac_cube()}}; they are marked with \code{cube = TRUE}. The role-based schema means a new reflectance source (e.g. Landsat C2 L2) drops in with no API change — only the role→asset map and scale/offset differ. } diff --git a/man/dft_stac_cube.Rd b/man/dft_stac_cube.Rd new file mode 100644 index 0000000..18c3481 --- /dev/null +++ b/man/dft_stac_cube.Rd @@ -0,0 +1,112 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dft_stac_cube.R +\name{dft_stac_cube} +\alias{dft_stac_cube} +\title{Fetch a masked spectral-index cube from a STAC catalog} +\usage{ +dft_stac_cube( + aoi, + source = "sentinel-2-l2a", + index = "kndvi", + datetime = NULL, + res = 10, + crs = NULL, + dt = "P1M", + aggregation = "median", + resampling = "bilinear", + cloud_cover_max = 60, + mask_values = NULL, + cache_dir = NULL, + force = FALSE, + sign_fn = rstac::sign_planetary_computer() +) +} +\arguments{ +\item{aoi}{An \code{sf} polygon defining the area of interest.} + +\item{source}{Character. A cube source name for \code{\link[=dft_stac_config]{dft_stac_config()}} (default +\code{"sentinel-2-l2a"}). Must be a source with \code{cube = TRUE}.} + +\item{index}{Character. Spectral index from \code{\link[=dft_index_table]{dft_index_table()}} (default +\code{"kndvi"}). Determines which band roles (and thus assets) are fetched.} + +\item{datetime}{Character. ISO 8601 interval \code{"start/end"}. When \code{NULL}, uses +\code{available_datetime} from \code{\link[=dft_stac_config]{dft_stac_config()}}.} + +\item{res}{Numeric. Output pixel size in CRS units (default 10).} + +\item{crs}{Character. Target CRS as an EPSG string. When \code{NULL}, auto-detected +from the AOI centroid's UTM zone.} + +\item{dt}{Character. ISO 8601 duration for the temporal aggregation window +(default \code{"P1M"}, monthly). The cadence \code{\link[=dft_rast_break]{dft_rast_break()}}'s \code{frequency} +must agree with.} + +\item{aggregation}{Character. Temporal aggregation for multiple scenes in one +\code{dt} window (default \code{"median"}).} + +\item{resampling}{Character. Spatial resampling (default \code{"bilinear"}).} + +\item{cloud_cover_max}{Numeric. Scene-level \code{eo:cloud_cover} maximum percent +for the STAC pre-filter (default 60).} + +\item{mask_values}{Integer vector of mask-band classes to exclude. When +\code{NULL}, uses \code{mask_values} from \code{\link[=dft_stac_config]{dft_stac_config()}} (e.g. Sentinel-2 SCL +cloud / shadow / cirrus classes).} + +\item{cache_dir}{Character. Cache directory. When \code{NULL}, uses +\code{\link[=dft_cache_path]{dft_cache_path()}}.} + +\item{force}{Logical. Re-fetch even if cached, overwriting the cached \code{.nc} +(default \code{FALSE}).} + +\item{sign_fn}{A signing function for STAC assets. Default is +\code{\link[rstac:items_sign_planetary_computer]{rstac::sign_planetary_computer()}}.} +} +\value{ +A \code{gdalcubes} data cube with a single band named \code{index}, backed by +the cached NetCDF file. +} +\description{ +Sibling of \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} for continuous change detection. Where +\code{\link[=dft_stac_fetch]{dft_stac_fetch()}} materializes one categorical raster per year, this builds +a multi-band, sub-annual reflectance cube, masks clouds, computes a spectral +index over band roles, and returns the lazy single-band index cube — the +input to \code{\link[=dft_rast_break]{dft_rast_break()}} for per-pixel trajectory breakpoint detection. +} +\details{ +The index cube is materialized once to a NetCDF file under \code{\link[=dft_cache_path]{dft_cache_path()}} +as \verb{/cube_.nc} (the full time axis, one band), keyed by a hash of +the AOI geometry and every cube-affecting parameter. Because the cube is +invariant to \code{\link[=dft_rast_break]{dft_rast_break()}}'s parameters (\code{history}/\code{start}/\code{level}), +caching it here makes bfast parameter sweeps cheap — they re-read the local +\code{.nc} instead of re-streaming COGs. + +Three STAC-query specifics distinguish cube mode from \code{\link[=dft_stac_fetch]{dft_stac_fetch()}}: +pagination via \code{\link[rstac:items_functions]{rstac::items_fetch()}} is mandatory (a monthly multi-year query +returns hundreds of items; a single page silently truncates); the query uses +\code{intersects} with the AOI geometry, not a bounding box (floodplain polygons +are highly non-rectangular); and a scene-level \code{eo:cloud_cover} pre-filter +shrinks the collection before any pixel is read, complementing per-pixel mask +filtering. +} +\examples{ +\dontrun{ +# Monthly kNDVI cube for a floodplain reach (requires network + gdalcubes) +aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +cube <- dft_stac_cube( + aoi, + source = "sentinel-2-l2a", + index = "kndvi", + datetime = "2019-01-01/2023-12-31", + dt = "P1M" +) +breaks <- dft_rast_break(cube, start = c(2022, 1)) +} + +} +\seealso{ +\code{\link[=dft_rast_break]{dft_rast_break()}} (the reducer that consumes this cube), +\code{\link[=dft_index_expr]{dft_index_expr()}} (the index applied), \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} (categorical +sibling). +} diff --git a/planning/active/findings.md b/planning/active/findings.md index 943c63d..3e04e96 100644 --- a/planning/active/findings.md +++ b/planning/active/findings.md @@ -75,6 +75,49 @@ rstac 1.0.1, terra 1.9.11, zoo 1.8.15. `bfast` / `strucchangeRcpp` NOT installed residual is a small additive bias near the 2022 boundary). Ship documented accepted-bias; per-scene harmonization from item metadata is a follow-up. +## Phase 4 empirical gate result (2026-07-08) — DESIGN CHANGE + +Ran the smoke test after installing bfast 1.7.2 / strucchangeRcpp 1.5.4. Findings: +- **Leg (a) passed exactly**: a 72-pt monthly series with a −0.3 step at 2022-06 + → `bfastmonitor(start=c(2022,1), level=0.01)` returns `breakpoint=2022.417`, + `magnitude=-0.300`. Scalar `level` is accepted (default is length-2). Stable + series → `breakpoint=NA` (no error). So `history="all"` + scalar `level` work. +- **CLOSURE CAPTURE DOES NOT WORK.** The reducer FUN runs in a spawned worker for + the R-callback path **at every `parallel` setting, including `parallel=1`**. + A closure (even a `force()`d factory frame) fails in the worker with + `object 'band' not found` — gdalcubes does not restore the closure environment. + Both the issue-#30 sketch and the original agent-B design assumed closures work; + they don't. This is the gate's headline catch. +- **SELF-CONTAINED FUN WORKS** at `parallel` 1 and 2. Two variants proven: + inline scalar literals via `substitute()`, or embed the per-pixel helper as a + literal function OBJECT (env detached to `baseenv()`) + inline scalar params. + Chosen: **embed-the-helper-object** — keeps a single logic source + (`.dft_break_pixel()`, directly unit-testable) AND a worker-safe FUN. + Result on the 2×2 synthetic cube: `nlyr=2`, names `break_date`/`break_mag`, + drop pixel `date=2022.42 mag=-0.3`, stable pixels `NA/0`, all-NA pixel `NA/NA`. +- `load_pkgs = "bfast"` is required so the worker can resolve `bfast::bfastmonitor`. +- Degenerate paths (`all(is.na(v))` / `sum(!is.na(v)) < min_obs`) return `c(NA,NA)` + **before any `bfast::` symbol** → unit-testable with no bfast dependency. + +Design for `R/dft_rast_break.R` (proven): +```r +.dft_break_pixel <- function(v, ts_start, frequency, start, history, level, min_obs) { + if (all(is.na(v)) || sum(!is.na(v)) < min_obs) return(c(NA_real_, NA_real_)) + ts_v <- stats::ts(v, start = ts_start, frequency = frequency) + tryCatch({ + m <- bfast::bfastmonitor(ts_v, start = start, history = history, level = level) + c(m$breakpoint, m$magnitude) + }, error = function(e) c(NA_real_, NA_real_)) +} +# reducer FUN: embed .dft_break_pixel as a literal object (env=baseenv) + inline +# band/ts_start/frequency/start/history/level/min_obs via substitute(); no free +# vars; reduce_time(..., load_pkgs = "bfast"). +``` +`bfastmonitor` return: `$breakpoint` = decimal year or NA; `$magnitude` = median +monitoring-period residual (neg = index drop = scour; ~0 = stable). `ts_start` +and `frequency` come from `gdalcubes::dimension_values(cube, "M")$t` in the main +process; `stop()` if a caller `frequency` disagrees with the dt cadence. + ## Reuse map - `stac_cache_key()` / `auto_utm_epsg()` / sf-coercion / `write_ncdf→terra→mask` idiom in `R/dft_stac_fetch.R` — reuse for `dft_stac_cube()`. diff --git a/planning/active/progress.md b/planning/active/progress.md index 8b6f3cd..a009473 100644 --- a/planning/active/progress.md +++ b/planning/active/progress.md @@ -13,5 +13,14 @@ Fresh-eyes code-check caught a dangling `[dft_stac_cube()]` roxygen link (R CMD check WARNING) → downgraded to code spans until Phase 3; hardened the fetch guard to key on `isTRUE(cfg$cube)` alone. 27 config + 16 fetch assertions green. -- Next: Phase 2 (index registry + `dft_index_expr()`) — code + tests already written - (15 assertions green), pending its own code-check + commit. +- **Phase 2 done** (index registry `inst/indices/indices.csv` + `dft_index_expr()` + + `dft_index_table()` + resolver internals; 15 assertions green; code-check Clean). +- **Phase 3 done** (`dft_stac_cube()` + `stac_cube_cache_key()`; 20 assertions green). + Code-check caught a multi-feature-AOI `intersects` bug (silent NoData holes) → + fixed by unioning the AOI. `eo:cloud_cover` NSE declared in globalVariables. +- Committed Phases 2+3 together (tightly-coupled acquisition pipeline; roxygen + cross-refs only resolve once both exist). Full suite: 261 pass / 0 fail / 3 skip. +- **Phase 4 empirical gate RUN** (bfast 1.7.2 installed): proved closure-capture in + the reduce_time R-callback DOES NOT WORK (worker process, any parallel setting) — + the design must build a self-contained FUN. Embed-helper-object approach works at + parallel 1 and 2. See findings.md. Next: write `R/dft_rast_break.R` per proven design. diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md index 8dab939..2661e06 100644 --- a/planning/active/task_plan.md +++ b/planning/active/task_plan.md @@ -20,14 +20,14 @@ network-decoupled vignette. **Whole pipeline on one branch, single 0.3.0 release - [x] `tests/testthat/test-dft_stac_config.R`: keep the 3 existing tests byte-for-byte; add cube-source assertions (`isTRUE(cfg$cube)`, role names + `$red=="B04"`/`$nir=="B08"`/`$mask=="SCL"`, `mask_values`, `scale`, `offset`, `available_datetime`); backward-compat assertion that `io-lulc`/`esa-worldcover` have no `cube` field ## Phase 2: `dft_index_expr()` + table-driven index registry -- [ ] `inst/indices/indices.csv`: registry (`index,formula,roles,description`) — `ndvi`, `kndvi=tanh(pow((nir-red)/(nir+red),2))`, `ndmi`; formulas in tinyexpr C syntax over **roles**, self-contained (kNDVI inlines NDVI) -- [ ] `R/dft_index_expr.R`: `dft_index_table()` (reads the CSV, mirrors `dft_class_table()`); internal `index_resolve_expr(index, roles, scale, offset)` — pure string builder, per-role scaled token `(asset*scale+offset)` when scale≠1 or offset≠0 else bare asset, longest-role-first word-boundary substitution, unknown-index `cli_abort` listing available; internal `index_roles(index)`; exported `dft_index_expr(cube, index="kndvi", source="sentinel-2-l2a", roles=NULL, scale=NULL, offset=NULL)` → `gdalcubes::apply_pixel(cube, expr, names = index)` -- [ ] `tests/testthat/test-dft_index_expr.R` (pure local, no gdalcubes/network): kNDVI-over-S2 resolves to the exact scaled string; scale/offset on (Landsat) vs off (bare) tokens; `ndmi` resolves `swir16→B11`; unknown-index error +- [x] `inst/indices/indices.csv`: registry (`index,formula,roles,description`) — `ndvi`, `kndvi=tanh(pow((nir-red)/(nir+red),2))`, `ndmi`; formulas in tinyexpr C syntax over **roles**, self-contained (kNDVI inlines NDVI). Uses `pow()` not `^` (gdalcubes tinyexpr) +- [x] `R/dft_index_expr.R`: `dft_index_table()` (reads the CSV, mirrors `dft_class_table()`); internal `index_resolve_expr(index, roles, scale, offset)` — pure string builder, per-role scaled token `(asset*scale±|offset|)` when scale≠1 or offset≠0 else bare asset, longest-role-first word-boundary substitution, unknown-index/absent-role `cli_abort`; internals `index_row()`/`index_roles()`/`scale_token()`; exported `dft_index_expr(cube, index="kndvi", source="sentinel-2-l2a", roles=NULL, scale=NULL, offset=NULL)` → `gdalcubes::apply_pixel(cube, expr, names = index)` +- [x] `tests/testthat/test-dft_index_expr.R` (pure local, no gdalcubes/network): kNDVI-over-S2 resolves to the exact scaled string; scale/offset on (Landsat) vs off (bare) tokens; `ndmi` resolves `swir16→B11`; unknown-index + absent-role errors. 15 assertions green. **Code-check Clean.** ## Phase 3: `dft_stac_cube()` -- [ ] `R/dft_stac_cube.R`: signature `dft_stac_cube(aoi, source="sentinel-2-l2a", index="kndvi", datetime=NULL, res=10, crs=NULL, dt="P1M", aggregation="median", resampling="bilinear", cloud_cover_max=60, mask_values=NULL, cache_dir=NULL, force=FALSE, sign_fn=rstac::sign_planetary_computer())`; `check_installed("gdalcubes")`; `stopifnot(isTRUE(cfg$cube))`; reuse `auto_utm_epsg`/sf-coercion/`dft_cache_path`; derive assets from `index_roles(index)` ∩ `cfg$roles` + `cfg$roles$mask`; STAC query with **`intersects`** (not bbox) + `ext_filter({{cloud_cover_max}})` + `post_request() |> items_fetch() |> items_sign()`; **explicit** `cube_view` extent from `bbox_target` + parsed `t0`/`t1`; `raster_cube(col, v, mask=image_mask(mask_asset, values=mask_values)) |> filter_geom(...)`; `dft_index_expr(...)`; materialize to `/cube_.nc` via `write_ncdf(overwrite=TRUE)`, return `gdalcubes::ncdf_cube(f)`; `force` overwrites -- [ ] Internal `stac_cube_cache_key()` (leave `stac_cache_key()` byte-for-byte unchanged — pinned by fetch tests): hash AOI-WKB + `res`/`target_crs`/`dt`/`aggregation`/`resampling`/`stac_url`/`collection` + cube additions `band_assets`/`datetime`/`index`/`cloud_cover_max`/`sort(mask_values)`/`scale`/`offset` -- [ ] `tests/testthat/test-dft_stac_cube.R`: local gdalcubes-missing guard (mirror fetch test); `stac_cube_cache_key` unit tests (determinism/12-hex, per-param sensitivity incl. index/datetime/cloud_cover_max/mask_values/scale/offset, sf-attrs ignored, 10L≡10); network E2E `skip_on_cran()` + `skip_if_offline()` + `skip_if_not_installed("gdalcubes")` (short window on `example_aoi.gpkg`, assert single `kndvi` band + cache-hit on second call) +- [x] `R/dft_stac_cube.R`: full signature per plan; `check_installed("gdalcubes")`; `!isTRUE(cfg$cube)` reject; reuse `auto_utm_epsg`/sf-coercion/`dft_cache_path`; derive assets from `index_roles(index)` + `cfg$roles$mask`; STAC query with **`intersects` (unioned AOI)** + `ext_filter(\`eo:cloud_cover\` <= {{cloud_cover_max}})` + `post_request() |> items_fetch() |> items_sign()`; **explicit** `cube_view` extent from `bbox_target` + parsed `t0`/`t1`; `raster_cube(col, v, mask=image_mask(mask_asset, values=mask_values)) |> filter_geom(...)`; `dft_index_expr(...)`; materialize to `/cube_.nc` via `write_ncdf(overwrite=TRUE)`, return `gdalcubes::ncdf_cube(f)`; `force` overwrites. **Code-check fixed a multi-feature-AOI bug: union AOI before `intersects` (else silent NoData holes — the #25 class).** `eo:cloud_cover` NSE declared in `utils::globalVariables()` +- [x] Internal `stac_cube_cache_key()` (leaves `stac_cache_key()` byte-for-byte unchanged): hash AOI-WKB + `res`/`target_crs`/`dt`/`aggregation`/`resampling`/`stac_url`/`collection` + cube additions `band_assets`/`datetime`/`index`/`cloud_cover_max`/`sort(mask_values)`/`scale`/`offset` +- [x] `tests/testthat/test-dft_stac_cube.R`: local gdalcubes-missing guard; categorical-source reject; `stac_cube_cache_key` unit tests (determinism/12-hex, per-param sensitivity, mask-order + res-type normalization, sf-attrs ignored); network E2E gated behind `DRIFT_TEST_NETWORK=true` env var (keeps `devtools::test()` network-free per repo convention). 20 assertions green ## Phase 4: `dft_rast_break()` — bfast reducer (empirical gate FIRST) - [ ] **Smoke-test gate before writing the function**: install `bfast` (+ `strucchangeRcpp`); run the 4-leg smoke script (scratchpad/data-raw): (a) synthetic 24-pt monthly series w/ injected step drop → `bfastmonitor(start=c(2022,1))` gives finite `$breakpoint` ≈ injected date, `$magnitude<0` (pins ts/frequency/`c(year,period)` semantics); (b) synthetic local cube (`create_image_collection(date_time=, band_names="kndvi")`→`cube_view(dt="P1M")`→`raster_cube`) through `reduce_time(names=c("break_date","break_mag"), load_pkgs="bfast", FUN=…)`→`write_ncdf`→`terra::rast` gives `nlyr==2`, correct names, `x["kndvi",]` resolves in FUN, break pixel finite / all-NA pixel NA; (c) `dt="P1M"` + `frequency=1` yields garbage (motivates the guard); (d) tiny real-S2 leg via `dft_stac_cube()` returns a 2-band raster. Proceed only if all pass diff --git a/tests/testthat/test-dft_index_expr.R b/tests/testthat/test-dft_index_expr.R new file mode 100644 index 0000000..26f3cd5 --- /dev/null +++ b/tests/testthat/test-dft_index_expr.R @@ -0,0 +1,61 @@ +# S2 role map (Planetary Computer band names) used across the resolver tests +s2_roles <- list(red = "B04", nir = "B08", swir16 = "B11", mask = "SCL") + +test_that("dft_index_table ships ndvi, kndvi, ndmi with formulas over roles", { + tbl <- dft_index_table() + expect_s3_class(tbl, "tbl_df") + expect_named(tbl, c("index", "formula", "roles", "description")) + expect_true(all(c("ndvi", "kndvi", "ndmi") %in% tbl$index)) + # kNDVI must use tinyexpr pow(), never R's ^ (which gdalcubes cannot parse) + kndvi <- tbl$formula[tbl$index == "kndvi"] + expect_match(kndvi, "pow\\(") + expect_false(grepl("\\^", kndvi)) +}) + +test_that("kNDVI resolves over S2 roles with scale/offset folded in", { + expr <- drift:::index_resolve_expr("kndvi", s2_roles, scale = 1e-4, offset = -0.1) + expect_equal( + expr, + paste0( + "tanh(pow(((B08 * 0.0001 - 0.1) - (B04 * 0.0001 - 0.1)) / ", + "((B08 * 0.0001 - 0.1) + (B04 * 0.0001 - 0.1)), 2))" + ) + ) +}) + +test_that("identity scale/offset yields bare asset tokens (no affine)", { + expr <- drift:::index_resolve_expr("ndvi", s2_roles, scale = 1, offset = 0) + expect_equal(expr, "(B08 - B04) / (B08 + B04)") +}) + +test_that("non-zero offset appears in the expression (ratio-on-DN guard)", { + # Landsat C2 L2 affine: scale 2.75e-5, offset -0.2 must not cancel in a ratio + landsat_roles <- list(red = "red", nir = "nir08", swir16 = "swir16") + expr <- drift:::index_resolve_expr("ndvi", landsat_roles, + scale = 2.75e-5, offset = -0.2) + expect_match(expr, "red * 0.0000275 - 0.2", fixed = TRUE) + expect_match(expr, "nir08 * 0.0000275 - 0.2", fixed = TRUE) +}) + +test_that("ndmi resolves swir16 -> its asset", { + expr <- drift:::index_resolve_expr("ndmi", s2_roles, scale = 1, offset = 0) + expect_equal(expr, "(B08 - B11) / (B08 + B11)") +}) + +test_that("index_roles reports the roles an index needs", { + expect_setequal(drift:::index_roles("kndvi"), c("nir", "red")) + expect_setequal(drift:::index_roles("ndmi"), c("nir", "swir16")) +}) + +test_that("unknown index errors with the available set", { + expect_error(drift:::index_resolve_expr("bogus", s2_roles), "Unknown index") + expect_error(drift:::index_roles("bogus"), "Unknown index") +}) + +test_that("an index needing an absent role errors", { + # ndmi needs swir16; a role map without it must fail loudly + expect_error( + drift:::index_resolve_expr("ndmi", list(red = "B04", nir = "B08")), + "role" + ) +}) diff --git a/tests/testthat/test-dft_stac_cube.R b/tests/testthat/test-dft_stac_cube.R new file mode 100644 index 0000000..63185b8 --- /dev/null +++ b/tests/testthat/test-dft_stac_cube.R @@ -0,0 +1,100 @@ +test_that("dft_stac_cube requires gdalcubes", { + skip_if(requireNamespace("gdalcubes", quietly = TRUE), + "gdalcubes is installed, can't test missing-package path") + aoi <- sf::st_read( + system.file("extdata", "example_aoi.gpkg", package = "drift"), + quiet = TRUE + ) + expect_error(dft_stac_cube(aoi), "gdalcubes") +}) + +test_that("dft_stac_cube rejects categorical sources", { + skip_if_not_installed("gdalcubes") + aoi <- sf::st_read( + system.file("extdata", "example_aoi.gpkg", package = "drift"), + quiet = TRUE + ) + expect_error(dft_stac_cube(aoi, source = "io-lulc"), "not a cube source") +}) + +# helpers for stac_cube_cache_key tests: a unit-square polygon (optionally +# shifted) and a key call with fixed defaults so each test varies one input +square_aoi <- function(dx = 0) { + sf::st_sfc( + sf::st_polygon(list(rbind( + c(0 + dx, 0), c(1 + dx, 0), c(1 + dx, 1), c(0 + dx, 1), c(0 + dx, 0) + ))), + crs = 32609 + ) +} + +cube_key <- function(aoi = square_aoi(), res = 10, target_crs = "EPSG:32609", + dt = "P1M", aggregation = "median", resampling = "bilinear", + stac_url = "https://example.com/stac", + collection = "sentinel-2-l2a", + band_assets = c("B08", "B04"), + datetime = "2019-01-01/2023-12-31", index = "kndvi", + cloud_cover_max = 60, mask_values = c(3, 8, 9, 10), + scale = 1e-4, offset = -0.1) { + drift:::stac_cube_cache_key( + aoi, res, target_crs, dt, aggregation, resampling, stac_url, collection, + band_assets, datetime, index, cloud_cover_max, mask_values, scale, offset + ) +} + +test_that("stac_cube_cache_key is deterministic and 12-char hex", { + expect_equal(cube_key(), cube_key()) + expect_match(cube_key(), "^[0-9a-f]{12}$") +}) + +test_that("stac_cube_cache_key changes with each cube-affecting parameter", { + base <- cube_key() + expect_false(cube_key(aoi = square_aoi(dx = 0.5)) == base) + expect_false(cube_key(res = 20) == base) + expect_false(cube_key(target_crs = "EPSG:32610") == base) + expect_false(cube_key(dt = "P1Y") == base) + expect_false(cube_key(aggregation = "mean") == base) + expect_false(cube_key(resampling = "near") == base) + expect_false(cube_key(collection = "landsat-c2-l2") == base) + expect_false(cube_key(band_assets = c("B08", "B11")) == base) + expect_false(cube_key(datetime = "2020-01-01/2020-12-31") == base) + expect_false(cube_key(index = "ndvi") == base) + expect_false(cube_key(cloud_cover_max = 20) == base) + expect_false(cube_key(mask_values = c(8, 9)) == base) + expect_false(cube_key(scale = 2.75e-5) == base) + expect_false(cube_key(offset = -0.2) == base) +}) + +test_that("stac_cube_cache_key normalizes mask_values order and res type", { + expect_equal(cube_key(mask_values = c(3, 8, 9, 10)), + cube_key(mask_values = c(10, 9, 8, 3))) + expect_equal(cube_key(res = 10L), cube_key(res = 10)) +}) + +test_that("stac_cube_cache_key ignores sf attribute columns", { + bare <- square_aoi() + with_attrs <- sf::st_sf(name = "a", area = 1.5, geometry = bare) + expect_equal(cube_key(with_attrs), cube_key(bare)) +}) + +# Network end-to-end against the Planetary Computer. Opt-in only (env var), so +# the default `devtools::test()` stays network-free per the repo convention. +test_that("dft_stac_cube fetches a single-band index cube end-to-end", { + skip_if(Sys.getenv("DRIFT_TEST_NETWORK") != "true", + "network test — set DRIFT_TEST_NETWORK=true to run") + skip_if_not_installed("gdalcubes") + aoi <- sf::st_read( + system.file("extdata", "example_aoi.gpkg", package = "drift"), + quiet = TRUE + ) + cache <- tempfile("drift_cube_") + dir.create(cache) + cube <- dft_stac_cube(aoi, index = "kndvi", + datetime = "2021-06-01/2021-08-31", dt = "P1M", + cache_dir = cache) + expect_s3_class(cube, "cube") + expect_equal(gdalcubes::bands(cube)$name, "kndvi") + # second call hits the cache (one cube_.nc under the source dir) + expect_length(list.files(file.path(cache, "sentinel-2-l2a"), + pattern = "^cube_.*\\.nc$"), 1) +}) From 18e999520b614e9b2d82a507c15cb7308a7ab53a Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 17:37:06 -0700 Subject: [PATCH 04/16] Add dft_rast_break() per-pixel trajectory breakpoint reducer (#30) Reduces an index cube over time with bfast::bfastmonitor(), returning a two-band SpatRaster of break date (decimal year) and magnitude (negative = index drop = scour / veg loss). Empirical gate (findings.md): the gdalcubes reduce_time R callback runs in a spawned worker at EVERY parallel setting, and a closure over enclosing locals fails there ("object 'band' not found"). The reducer is therefore built self-contained -- build_break_reducer() embeds the testable .dft_break_pixel() helper as a literal object (env detached to baseenv) and inlines every scalar parameter via substitute(), with load_pkgs="bfast" -- proven correct at parallel 1 and 2. ts start / seasonal frequency are derived from the cube time axis in the main process; a caller frequency that disagrees with the cadence errors. Degenerate pixels (all-NA / < min_obs) short-circuit before any bfast call, so those paths test with no bfast dependency. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- DESCRIPTION | 1 + NAMESPACE | 1 + R/dft_rast_break.R | 189 +++++++++++++++++++++++++++ man/dft_rast_break.Rd | 84 ++++++++++++ planning/active/progress.md | 12 +- planning/active/task_plan.md | 8 +- tests/testthat/helper-break.R | 39 ++++++ tests/testthat/test-dft_rast_break.R | 123 +++++++++++++++++ 8 files changed, 452 insertions(+), 5 deletions(-) create mode 100644 R/dft_rast_break.R create mode 100644 man/dft_rast_break.Rd create mode 100644 tests/testthat/helper-break.R create mode 100644 tests/testthat/test-dft_rast_break.R diff --git a/DESCRIPTION b/DESCRIPTION index 449a7da..f735498 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -31,6 +31,7 @@ Imports: terra (>= 1.8-10), tibble Suggests: + bfast, bookdown, DT, flooded, diff --git a/NAMESPACE b/NAMESPACE index 9d3a6e8..5e0b03d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,6 +7,7 @@ export(dft_class_table) export(dft_index_expr) export(dft_index_table) export(dft_map_interactive) +export(dft_rast_break) export(dft_rast_classify) export(dft_rast_consensus) export(dft_rast_summarize) diff --git a/R/dft_rast_break.R b/R/dft_rast_break.R new file mode 100644 index 0000000..5a18d72 --- /dev/null +++ b/R/dft_rast_break.R @@ -0,0 +1,189 @@ +#' Detect per-pixel index-trajectory breakpoints +#' +#' Reduce an index cube (from [dft_stac_cube()]) over time with +#' [bfast::bfastmonitor()], returning a two-band `SpatRaster` of the break date +#' and magnitude for every pixel. Where categorical differencing compares two +#' land-cover labels, this asks a stronger question of a continuous index +#' trajectory: *when* did the pixel's spectral history break, and by how much? +#' +#' `bfastmonitor` fits a season-trend model to a stable *history* period, then +#' watches the *monitoring* period (from `start` onward) for a structural break. +#' The returned `break_mag` is the median monitoring-period residual: **negative +#' means the index dropped** (e.g. vegetation loss / channel scour), positive +#' means it rose (establishment). `break_date` is a decimal year (e.g. +#' `2022.42`) or `NA` where no break was detected. +#' +#' The reducer runs as a per-pixel R callback. gdalcubes evaluates that callback +#' in spawned worker processes, so the function is built self-contained (its +#' parameters are baked in as literals) and `bfast` is loaded in each worker — +#' making the result independent of the `gdalcubes::gdalcubes_options(parallel=)` +#' setting. +#' +#' @param cube A `gdalcubes` index cube whose single band is a spectral index +#' time series (the return value of [dft_stac_cube()]). +#' @param band Character. Band to reduce. When `NULL`, the cube's first band. +#' @param history Character. `bfastmonitor` history-selection method: `"all"` +#' (default), `"ROC"`, or `"BP"`. +#' @param start Numeric `c(year, period)`. Start of the monitoring period, in the +#' cube's temporal frequency (e.g. `c(2022, 1)` = Jan 2022 for a monthly cube). +#' Everything before it is the stable history. +#' @param frequency Numeric or `NULL`. Seasonal frequency of the time series +#' (12 for monthly, 1 for annual). When `NULL`, derived from the cube's +#' temporal cadence; when supplied, it must agree with that cadence or the +#' call errors. +#' @param level Numeric. Significance level passed to [bfast::bfastmonitor()] +#' (default 0.01). +#' @param min_obs Integer. Minimum non-`NA` observations required to attempt a +#' fit; pixels with fewer return `NA` (default 6). +#' @param cache_dir Character. Cache directory. When `NULL`, [dft_cache_path()]. +#' @param force Logical. Recompute even if cached (default `FALSE`). +#' +#' @return A two-band [terra::SpatRaster] with layers `break_date` (decimal year +#' or `NA`) and `break_mag` (signed index change; negative = index drop). +#' +#' @seealso [dft_stac_cube()] (builds the input cube), [dft_index_expr()]. +#' +#' @examples +#' \dontrun{ +#' # Requires network + gdalcubes + bfast +#' aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +#' cube <- dft_stac_cube(aoi, index = "kndvi", datetime = "2019-01-01/2023-12-31") +#' breaks <- dft_rast_break(cube, start = c(2022, 1)) +#' terra::plot(breaks[["break_mag"]]) # negative (blue) = scour / veg loss +#' } +#' +#' @export +dft_rast_break <- function(cube, + band = NULL, + history = "all", + start = c(2022, 1), + frequency = NULL, + level = 0.01, + min_obs = 6, + cache_dir = NULL, + force = FALSE) { + rlang::check_installed("gdalcubes", reason = "to reduce a data cube over time") + rlang::check_installed("bfast", reason = "for trajectory breakpoint detection") + + band <- band %||% gdalcubes::bands(cube)$name[[1]] + + # derive the ts() start and seasonal frequency from the cube time axis, in the + # main process (the reducer callback cannot see the cube) + tdim <- gdalcubes::dimensions(cube)$t + cadence_freq <- cadence_frequency(tdim$pixel_size) + if (is.na(cadence_freq)) { + cli::cli_abort(c( + "Unsupported cube temporal cadence {.val {tdim$pixel_size}}.", + "i" = "Use a monthly (`P1M`) or annual (`P1Y`) cube." + )) + } + if (is.null(frequency)) { + frequency <- cadence_freq + } else if (!isTRUE(all.equal(as.numeric(frequency), as.numeric(cadence_freq)))) { + cli::cli_abort(c( + "`frequency` ({frequency}) disagrees with the cube cadence \\ + {.val {tdim$pixel_size}} (= {cadence_freq}).", + "i" = "Leave `frequency = NULL` to derive it from the cube." + )) + } + t0_date <- as.Date(tdim$low) + yr <- as.integer(format(t0_date, "%Y")) + mo <- as.integer(format(t0_date, "%m")) + period <- floor((mo - 1) / (12 / frequency)) + 1 + ts_start <- c(yr, period) + + # cache keyed by the full cube definition + reducer parameters + cache_base <- dft_cache_path(cache_dir) + cache_break_dir <- file.path(cache_base, "break") + dir.create(cache_break_dir, recursive = TRUE, showWarnings = FALSE) + key <- break_cache_key(cube, band, history, start, frequency, level, min_obs) + cache_file <- file.path(cache_break_dir, paste0("break_", key, ".nc")) + + if (!force && file.exists(cache_file)) { + message(" break: cached") + return(terra::rast(cache_file)) + } + + fun <- build_break_reducer(band, ts_start, frequency, start, history, level, + min_obs) + reduced <- gdalcubes::reduce_time( + cube, names = c("break_date", "break_mag"), + load_pkgs = "bfast", FUN = fun + ) + gdalcubes::write_ncdf(reduced, cache_file, overwrite = TRUE) + terra::rast(cache_file) +} + + +#' Per-pixel breakpoint reducer logic (internal, unit-testable) +#' +#' Canonical logic shared by the exported reducer. The degenerate branches +#' (all-`NA` or fewer than `min_obs` observations) return `c(NA, NA)` before any +#' `bfast` symbol is touched, so they are testable without bfast installed. +#' @noRd +.dft_break_pixel <- function(v, ts_start, frequency, start, history, level, + min_obs) { + if (all(is.na(v)) || sum(!is.na(v)) < min_obs) return(c(NA_real_, NA_real_)) + ts_v <- stats::ts(v, start = ts_start, frequency = frequency) + tryCatch({ + m <- bfast::bfastmonitor(ts_v, start = start, history = history, + level = level) + c(m$breakpoint, m$magnitude) + }, error = function(e) c(NA_real_, NA_real_)) +} + + +#' Seasonal frequency implied by a gdalcubes ISO cadence (internal) +#' +#' `P1M` -> 12, `P3M` -> 4, `P1Y` -> 1. Returns `NA` for unsupported cadences. +#' @noRd +cadence_frequency <- function(dt_iso) { + m <- regmatches(dt_iso, regexec("^P(\\d+)([MY])$", dt_iso))[[1]] + if (length(m) != 3) return(NA_real_) + n <- as.numeric(m[2]) + switch(m[3], + "M" = 12 / n, + "Y" = 1 / n, + NA_real_ + ) +} + + +#' Build a self-contained per-pixel reducer for reduce_time (internal) +#' +#' gdalcubes serializes the callback into worker processes and does not restore +#' the calling environment, so a closure over enclosing locals fails there. This +#' embeds [.dft_break_pixel()] as a literal function object (detached to +#' `baseenv()`) and inlines every parameter as a literal, yielding a callback +#' with no free variables that runs correctly under any `parallel` setting. +#' @noRd +build_break_reducer <- function(band, ts_start, frequency, start, history, + level, min_obs) { + pixfun <- .dft_break_pixel + environment(pixfun) <- baseenv() + f <- function(x) NULL + body(f) <- substitute( + PIX(x[BAND, ], TS, FRQ, ST, HI, LV, MN), + list(PIX = pixfun, BAND = band, TS = ts_start, FRQ = frequency, + ST = start, HI = history, LV = level, MN = min_obs) + ) + environment(f) <- baseenv() + f +} + + +#' Cache key for one break reduction (internal) +#' +#' Hashes the full cube definition (`gdalcubes::as_json`, which captures the +#' source items, view, mask, and index) plus every reducer parameter. +#' @noRd +break_cache_key <- function(cube, band, history, start, frequency, level, + min_obs) { + substr( + rlang::hash(list( + gdalcubes::as_json(cube), band, history, as.numeric(start), + as.numeric(frequency), as.numeric(level), as.numeric(min_obs) + )), + 1, 12 + ) +} diff --git a/man/dft_rast_break.Rd b/man/dft_rast_break.Rd new file mode 100644 index 0000000..5db69e5 --- /dev/null +++ b/man/dft_rast_break.Rd @@ -0,0 +1,84 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dft_rast_break.R +\name{dft_rast_break} +\alias{dft_rast_break} +\title{Detect per-pixel index-trajectory breakpoints} +\usage{ +dft_rast_break( + cube, + band = NULL, + history = "all", + start = c(2022, 1), + frequency = NULL, + level = 0.01, + min_obs = 6, + cache_dir = NULL, + force = FALSE +) +} +\arguments{ +\item{cube}{A \code{gdalcubes} index cube whose single band is a spectral index +time series (the return value of \code{\link[=dft_stac_cube]{dft_stac_cube()}}).} + +\item{band}{Character. Band to reduce. When \code{NULL}, the cube's first band.} + +\item{history}{Character. \code{bfastmonitor} history-selection method: \code{"all"} +(default), \code{"ROC"}, or \code{"BP"}.} + +\item{start}{Numeric \code{c(year, period)}. Start of the monitoring period, in the +cube's temporal frequency (e.g. \code{c(2022, 1)} = Jan 2022 for a monthly cube). +Everything before it is the stable history.} + +\item{frequency}{Numeric or \code{NULL}. Seasonal frequency of the time series +(12 for monthly, 1 for annual). When \code{NULL}, derived from the cube's +temporal cadence; when supplied, it must agree with that cadence or the +call errors.} + +\item{level}{Numeric. Significance level passed to \code{\link[bfast:bfastmonitor]{bfast::bfastmonitor()}} +(default 0.01).} + +\item{min_obs}{Integer. Minimum non-\code{NA} observations required to attempt a +fit; pixels with fewer return \code{NA} (default 6).} + +\item{cache_dir}{Character. Cache directory. When \code{NULL}, \code{\link[=dft_cache_path]{dft_cache_path()}}.} + +\item{force}{Logical. Recompute even if cached (default \code{FALSE}).} +} +\value{ +A two-band \link[terra:SpatRaster-class]{terra::SpatRaster} with layers \code{break_date} (decimal year +or \code{NA}) and \code{break_mag} (signed index change; negative = index drop). +} +\description{ +Reduce an index cube (from \code{\link[=dft_stac_cube]{dft_stac_cube()}}) over time with +\code{\link[bfast:bfastmonitor]{bfast::bfastmonitor()}}, returning a two-band \code{SpatRaster} of the break date +and magnitude for every pixel. Where categorical differencing compares two +land-cover labels, this asks a stronger question of a continuous index +trajectory: \emph{when} did the pixel's spectral history break, and by how much? +} +\details{ +\code{bfastmonitor} fits a season-trend model to a stable \emph{history} period, then +watches the \emph{monitoring} period (from \code{start} onward) for a structural break. +The returned \code{break_mag} is the median monitoring-period residual: \strong{negative +means the index dropped} (e.g. vegetation loss / channel scour), positive +means it rose (establishment). \code{break_date} is a decimal year (e.g. +\code{2022.42}) or \code{NA} where no break was detected. + +The reducer runs as a per-pixel R callback. gdalcubes evaluates that callback +in spawned worker processes, so the function is built self-contained (its +parameters are baked in as literals) and \code{bfast} is loaded in each worker — +making the result independent of the \code{gdalcubes::gdalcubes_options(parallel=)} +setting. +} +\examples{ +\dontrun{ +# Requires network + gdalcubes + bfast +aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +cube <- dft_stac_cube(aoi, index = "kndvi", datetime = "2019-01-01/2023-12-31") +breaks <- dft_rast_break(cube, start = c(2022, 1)) +terra::plot(breaks[["break_mag"]]) # negative (blue) = scour / veg loss +} + +} +\seealso{ +\code{\link[=dft_stac_cube]{dft_stac_cube()}} (builds the input cube), \code{\link[=dft_index_expr]{dft_index_expr()}}. +} diff --git a/planning/active/progress.md b/planning/active/progress.md index a009473..75eb7b0 100644 --- a/planning/active/progress.md +++ b/planning/active/progress.md @@ -23,4 +23,14 @@ - **Phase 4 empirical gate RUN** (bfast 1.7.2 installed): proved closure-capture in the reduce_time R-callback DOES NOT WORK (worker process, any parallel setting) — the design must build a self-contained FUN. Embed-helper-object approach works at - parallel 1 and 2. See findings.md. Next: write `R/dft_rast_break.R` per proven design. + parallel 1 and 2. See findings.md. +- **Phase 4 done** (`dft_rast_break()` + `.dft_break_pixel`/`cadence_frequency`/ + `build_break_reducer`/`break_cache_key`; bfast→Suggests). 30 assertions green; + full suite 291 pass. Code-check Clean. +- **Real-S2 E2E surfaced a `dft_stac_cube` bug**: `gdalcubes::filter_geom()` yields + an all-NA cube (and intermittently segfaults the compute worker) on this gdalcubes + build. Proven sibling `dft_stac_fetch` works on the same AOI, and the cube stages + work WITHOUT filter_geom (valid kNDVI 0..1). Fix: drop filter_geom; the cube spans + the AOI bbox (cloud-masked), callers clip the reduced raster with terra::mask() + (matches how dft_stac_fetch masks). Verifying the fixed E2E, then committing the + fix + Phase 5 (vignette + NEWS + 0.3.0). diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md index 2661e06..17eaa72 100644 --- a/planning/active/task_plan.md +++ b/planning/active/task_plan.md @@ -30,10 +30,10 @@ network-decoupled vignette. **Whole pipeline on one branch, single 0.3.0 release - [x] `tests/testthat/test-dft_stac_cube.R`: local gdalcubes-missing guard; categorical-source reject; `stac_cube_cache_key` unit tests (determinism/12-hex, per-param sensitivity, mask-order + res-type normalization, sf-attrs ignored); network E2E gated behind `DRIFT_TEST_NETWORK=true` env var (keeps `devtools::test()` network-free per repo convention). 20 assertions green ## Phase 4: `dft_rast_break()` — bfast reducer (empirical gate FIRST) -- [ ] **Smoke-test gate before writing the function**: install `bfast` (+ `strucchangeRcpp`); run the 4-leg smoke script (scratchpad/data-raw): (a) synthetic 24-pt monthly series w/ injected step drop → `bfastmonitor(start=c(2022,1))` gives finite `$breakpoint` ≈ injected date, `$magnitude<0` (pins ts/frequency/`c(year,period)` semantics); (b) synthetic local cube (`create_image_collection(date_time=, band_names="kndvi")`→`cube_view(dt="P1M")`→`raster_cube`) through `reduce_time(names=c("break_date","break_mag"), load_pkgs="bfast", FUN=…)`→`write_ncdf`→`terra::rast` gives `nlyr==2`, correct names, `x["kndvi",]` resolves in FUN, break pixel finite / all-NA pixel NA; (c) `dt="P1M"` + `frequency=1` yields garbage (motivates the guard); (d) tiny real-S2 leg via `dft_stac_cube()` returns a 2-band raster. Proceed only if all pass -- [ ] `R/dft_rast_break.R`: internal `.dft_break_pixel(v, start, frequency, ts_start, history, level, min_obs)` — degenerate all-NA / ` frequency (pure) ------------------------------------------- + +test_that("cadence_frequency maps ISO durations to seasonal frequency", { + expect_equal(drift:::cadence_frequency("P1M"), 12) + expect_equal(drift:::cadence_frequency("P3M"), 4) + expect_equal(drift:::cadence_frequency("P1Y"), 1) + expect_true(is.na(drift:::cadence_frequency("P16D"))) + expect_true(is.na(drift:::cadence_frequency("garbage"))) +}) + +# ---- .dft_break_pixel degenerate paths (no bfast needed) ------------------- + +test_that(".dft_break_pixel returns c(NA, NA) on all-NA input", { + expect_equal( + drift:::.dft_break_pixel(rep(NA_real_, 24), c(2018, 1), 12, c(2022, 1), + "all", 0.01, 6), + c(NA_real_, NA_real_) + ) +}) + +test_that(".dft_break_pixel returns c(NA, NA) when fewer than min_obs", { + v <- c(0.5, 0.6, NA, NA, 0.55, rep(NA_real_, 19)) # 3 non-NA < min_obs 6 + expect_equal( + drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 0.01, 6), + c(NA_real_, NA_real_) + ) +}) + +# ---- reducer is self-contained (worker-safe) ------------------------------- + +test_that("build_break_reducer yields a callback with no free variables", { + f <- drift:::build_break_reducer("kndvi", c(2018, 1), 12, c(2022, 1), + "all", 0.01, 6) + # environment detached to baseenv and all params inlined as literals, so the + # only globals referenced are base/pkg functions -> safe to serialize to a + # gdalcubes worker (see findings.md: closures fail in workers) + expect_identical(environment(f), baseenv()) + globals <- codetools::findGlobals(f, merge = FALSE)$variables + expect_false("band" %in% globals) + expect_false("ts_start" %in% globals) +}) + +# ---- break cache key ------------------------------------------------------- + +test_that("break_cache_key changes with each reducer parameter", { + skip_if_not_installed("gdalcubes") + skip_if_not_installed("bfast") + cube <- synthetic_break_cube()$cube + base <- drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 12, 0.01, 6) + expect_match(base, "^[0-9a-f]{12}$") + expect_false(drift:::break_cache_key(cube, "kndvi", "ROC", c(2022, 1), 12, 0.01, 6) == base) + expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2021, 1), 12, 0.01, 6) == base) + expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 1, 0.01, 6) == base) + expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 12, 0.05, 6) == base) + expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 12, 0.01, 8) == base) +}) + +# ---- bfast-gated per-pixel behavior ---------------------------------------- + +test_that(".dft_break_pixel detects an injected step drop (negative magnitude)", { + skip_if_not_installed("bfast") + tt <- seq_len(72) + v <- 0.6 + 0.15 * sin(2 * pi * tt / 12) + v[54:72] <- v[54:72] - 0.3 # step drop at 2022-06 (monitoring period) + out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 0.01, 6) + expect_true(is.finite(out[1])) + expect_gt(out[1], 2022) + expect_lt(out[1], 2022.9) + expect_lt(out[2], 0) +}) + +test_that(".dft_break_pixel returns NA break on a stable series (non-error)", { + skip_if_not_installed("bfast") + tt <- seq_len(72) + v <- 0.6 + 0.15 * sin(2 * pi * tt / 12) + out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 0.01, 6) + expect_true(is.na(out[1])) +}) + +# ---- gdalcubes + bfast integration (synthetic cube, no network) ------------ + +test_that("dft_rast_break reduces a synthetic cube to a 2-band raster", { + skip_if_not_installed("gdalcubes") + skip_if_not_installed("bfast") + sc <- synthetic_break_cube() + breaks <- dft_rast_break(sc$cube, start = c(2022, 1), + cache_dir = sc$cache_dir) + + expect_s4_class(breaks, "SpatRaster") + expect_equal(terra::nlyr(breaks), 2) + expect_equal(names(breaks), c("break_date", "break_mag")) + expect_equal(terra::crs(breaks, describe = TRUE)$code, "32609") + + bd <- terra::values(breaks[["break_date"]])[, 1] + bm <- terra::values(breaks[["break_mag"]])[, 1] + # the engineered drop pixel yields a finite break with negative magnitude; + # the all-NA pixel yields NA + expect_true(any(is.finite(bd))) + expect_true(any(is.na(bd))) + expect_true(any(is.finite(bd) & bm < 0)) + + # second call hits the cache (one break_.nc) + breaks2 <- dft_rast_break(sc$cube, start = c(2022, 1), + cache_dir = sc$cache_dir) + expect_equal(terra::values(breaks2[["break_date"]])[, 1], bd) + expect_length(list.files(file.path(sc$cache_dir, "break"), + pattern = "^break_.*\\.nc$"), 1) +}) From c6953a0c92e99f73f2341b62ee88971ebb771576 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 18:08:39 -0700 Subject: [PATCH 05/16] Fix dft_stac_cube() all-NA cube from filter_geom (#30) The real-Sentinel-2 end-to-end run produced an entirely-NA kNDVI cube (and intermittently segfaulted the gdalcubes compute worker). Isolated to gdalcubes::filter_geom(): stepping through the pipeline, the raw bands, SCL masking, and the kNDVI index all yield valid data (kNDVI in 0..1) WITHOUT filter_geom, and the proven sibling dft_stac_fetch() works on the same AOI. Drop the in-pipeline polygon clip. The cube now spans the AOI bounding box (cloud-masked); callers clip the reduced raster with terra::mask(), matching how dft_stac_fetch() masks after reading. Verified end-to-end: 48-month cube has valid kNDVI throughout and dft_rast_break() reduces it (no segfault) to a plausible break raster (dates in the 2022-2023 monitoring window). Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- R/dft_stac_cube.R | 12 +++++++++--- man/dft_stac_cube.Rd | 4 +++- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/R/dft_stac_cube.R b/R/dft_stac_cube.R index 9aa5c53..60daa03 100644 --- a/R/dft_stac_cube.R +++ b/R/dft_stac_cube.R @@ -50,7 +50,9 @@ #' [rstac::sign_planetary_computer()]. #' #' @return A `gdalcubes` data cube with a single band named `index`, backed by -#' the cached NetCDF file. +#' the cached NetCDF file. The cube spans the AOI **bounding box** (cloud-masked +#' but not clipped to the AOI polygon); clip the reduced raster from +#' [dft_rast_break()] with `terra::mask()` if a tight AOI is needed. #' #' @seealso [dft_rast_break()] (the reducer that consumes this cube), #' [dft_index_expr()] (the index applied), [dft_stac_fetch()] (categorical @@ -176,11 +178,15 @@ dft_stac_cube <- function(aoi, aggregation = aggregation, resampling = resampling ) + # The cube spans the AOI bounding box. Clipping to the AOI polygon with + # gdalcubes::filter_geom() inside the pipeline yields an all-NA cube (and can + # crash the compute worker) on the pinned gdalcubes build, so we mask clouds + # here and leave polygon clipping to the caller (terra::mask() on the reduced + # raster), matching how the categorical sibling dft_stac_fetch() masks. cube <- gdalcubes::raster_cube( col, v, mask = gdalcubes::image_mask(mask_asset, values = mask_values) - ) |> - gdalcubes::filter_geom(sf::st_geometry(aoi_target)) + ) idx <- dft_index_expr(cube, index = index, source = source, roles = cfg$roles, scale = scale, offset = offset) diff --git a/man/dft_stac_cube.Rd b/man/dft_stac_cube.Rd index 18c3481..5e8c44c 100644 --- a/man/dft_stac_cube.Rd +++ b/man/dft_stac_cube.Rd @@ -65,7 +65,9 @@ cloud / shadow / cirrus classes).} } \value{ A \code{gdalcubes} data cube with a single band named \code{index}, backed by -the cached NetCDF file. +the cached NetCDF file. The cube spans the AOI \strong{bounding box} (cloud-masked +but not clipped to the AOI polygon); clip the reduced raster from +\code{\link[=dft_rast_break]{dft_rast_break()}} with \code{terra::mask()} if a tight AOI is needed. } \description{ Sibling of \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} for continuous change detection. Where From 61eaa5d67d175a59d741d16f093188b55e31766e Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 19:06:38 -0700 Subject: [PATCH 06/16] Add growing-season tuning: months filter, snow mask, order, /vsicurl config (#30) For high-latitude floodplains the all-months fetch was slow (~30 min for 100 ha -- the Sentinel-2 streaming, not the compute) and injected winter snow/low-sun noise that has no vegetation signal. Focus the analysis on the growing season: - dft_stac_cube() gains `months` (calendar-month filter, default NULL/all). Fetching only e.g. June-September streams ~3x fewer scenes AND sharpens the signal; empty months become NA so the monthly series stays regular at frequency 12 for bfast. - Snow/ice (SCL 11) added to the default Sentinel-2 mask. - A GDAL /vsicurl config block (DISABLE_READDIR_ON_OPEN etc.), restored on exit, trims per-COG open latency. - dft_rast_break() gains `order` (bfastmonitor harmonic order); lower it for a growing-season-only series that samples a narrow part of the annual cycle. Both cache keys extended with the new params. Growing-season E2E (months=6:9, 2018-2023): 12 min vs 30, 24 clean summer observations per pixel, breaks dated to summer months. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- R/dft_rast_break.R | 30 +++++++++++------- R/dft_stac_config.R | 6 ++-- R/dft_stac_cube.R | 44 +++++++++++++++++++++++++-- man/dft_rast_break.Rd | 7 +++++ man/dft_stac_cube.Rd | 10 ++++++ planning/active/progress.md | 13 ++++++-- tests/testthat/test-dft_rast_break.R | 28 ++++++++++------- tests/testthat/test-dft_stac_config.R | 4 +-- tests/testthat/test-dft_stac_cube.R | 12 ++++++-- 9 files changed, 120 insertions(+), 34 deletions(-) diff --git a/R/dft_rast_break.R b/R/dft_rast_break.R index 5a18d72..7c1a76c 100644 --- a/R/dft_rast_break.R +++ b/R/dft_rast_break.R @@ -31,6 +31,11 @@ #' (12 for monthly, 1 for annual). When `NULL`, derived from the cube's #' temporal cadence; when supplied, it must agree with that cadence or the #' call errors. +#' @param order Integer. Harmonic order of the season-trend model passed to +#' [bfast::bfastmonitor()] (default 3). Lower it (1-2) when the series samples +#' only part of the year (e.g. a growing-season-only cube from +#' [dft_stac_cube()] `months`), where a high order overfits sparse seasonal +#' coverage. #' @param level Numeric. Significance level passed to [bfast::bfastmonitor()] #' (default 0.01). #' @param min_obs Integer. Minimum non-`NA` observations required to attempt a @@ -58,6 +63,7 @@ dft_rast_break <- function(cube, history = "all", start = c(2022, 1), frequency = NULL, + order = 3, level = 0.01, min_obs = 6, cache_dir = NULL, @@ -96,7 +102,8 @@ dft_rast_break <- function(cube, cache_base <- dft_cache_path(cache_dir) cache_break_dir <- file.path(cache_base, "break") dir.create(cache_break_dir, recursive = TRUE, showWarnings = FALSE) - key <- break_cache_key(cube, band, history, start, frequency, level, min_obs) + key <- break_cache_key(cube, band, history, start, frequency, order, level, + min_obs) cache_file <- file.path(cache_break_dir, paste0("break_", key, ".nc")) if (!force && file.exists(cache_file)) { @@ -104,8 +111,8 @@ dft_rast_break <- function(cube, return(terra::rast(cache_file)) } - fun <- build_break_reducer(band, ts_start, frequency, start, history, level, - min_obs) + fun <- build_break_reducer(band, ts_start, frequency, start, history, order, + level, min_obs) reduced <- gdalcubes::reduce_time( cube, names = c("break_date", "break_mag"), load_pkgs = "bfast", FUN = fun @@ -121,13 +128,13 @@ dft_rast_break <- function(cube, #' (all-`NA` or fewer than `min_obs` observations) return `c(NA, NA)` before any #' `bfast` symbol is touched, so they are testable without bfast installed. #' @noRd -.dft_break_pixel <- function(v, ts_start, frequency, start, history, level, - min_obs) { +.dft_break_pixel <- function(v, ts_start, frequency, start, history, order, + level, min_obs) { if (all(is.na(v)) || sum(!is.na(v)) < min_obs) return(c(NA_real_, NA_real_)) ts_v <- stats::ts(v, start = ts_start, frequency = frequency) tryCatch({ m <- bfast::bfastmonitor(ts_v, start = start, history = history, - level = level) + order = order, level = level) c(m$breakpoint, m$magnitude) }, error = function(e) c(NA_real_, NA_real_)) } @@ -158,14 +165,14 @@ cadence_frequency <- function(dt_iso) { #' with no free variables that runs correctly under any `parallel` setting. #' @noRd build_break_reducer <- function(band, ts_start, frequency, start, history, - level, min_obs) { + order, level, min_obs) { pixfun <- .dft_break_pixel environment(pixfun) <- baseenv() f <- function(x) NULL body(f) <- substitute( - PIX(x[BAND, ], TS, FRQ, ST, HI, LV, MN), + PIX(x[BAND, ], TS, FRQ, ST, HI, OR, LV, MN), list(PIX = pixfun, BAND = band, TS = ts_start, FRQ = frequency, - ST = start, HI = history, LV = level, MN = min_obs) + ST = start, HI = history, OR = order, LV = level, MN = min_obs) ) environment(f) <- baseenv() f @@ -177,12 +184,13 @@ build_break_reducer <- function(band, ts_start, frequency, start, history, #' Hashes the full cube definition (`gdalcubes::as_json`, which captures the #' source items, view, mask, and index) plus every reducer parameter. #' @noRd -break_cache_key <- function(cube, band, history, start, frequency, level, +break_cache_key <- function(cube, band, history, start, frequency, order, level, min_obs) { substr( rlang::hash(list( gdalcubes::as_json(cube), band, history, as.numeric(start), - as.numeric(frequency), as.numeric(level), as.numeric(min_obs) + as.numeric(frequency), as.numeric(order), as.numeric(level), + as.numeric(min_obs) )), 1, 12 ) diff --git a/R/dft_stac_config.R b/R/dft_stac_config.R index 86a9905..ed9e34d 100644 --- a/R/dft_stac_config.R +++ b/R/dft_stac_config.R @@ -60,8 +60,10 @@ dft_stac_config <- function(source = c("io-lulc", "esa-worldcover", mask = "SCL" ), # Scene Classification Layer classes to mask out: - # 3 cloud shadow, 8 cloud medium, 9 cloud high, 10 thin cirrus - mask_values = c(3L, 8L, 9L, 10L), + # 3 cloud shadow, 8 cloud medium, 9 cloud high, 10 thin cirrus, 11 snow/ice + # (snow is masked by default: it is never a vegetation signal and otherwise + # dominates the winter trajectory at high latitudes) + mask_values = c(3L, 8L, 9L, 10L, 11L), # DN -> reflectance: baseline 04.00 (+1000 DN) => DN * 1e-4 - 0.1 scale = 1e-4, offset = -0.1, diff --git a/R/dft_stac_cube.R b/R/dft_stac_cube.R index 60daa03..4cdff07 100644 --- a/R/dft_stac_cube.R +++ b/R/dft_stac_cube.R @@ -39,6 +39,14 @@ #' @param resampling Character. Spatial resampling (default `"bilinear"`). #' @param cloud_cover_max Numeric. Scene-level `eo:cloud_cover` maximum percent #' for the STAC pre-filter (default 60). +#' @param months Integer vector of calendar months (1-12) to keep, or `NULL` +#' (default) for all. Restricting to the growing season (e.g. `6:9`) both +#' sharpens the vegetation signal — snow and low-sun winter scenes carry no +#' vegetation information — and cuts the number of scenes streamed. Months with +#' no retained scenes become `NA` in the monthly cube, so the per-pixel series +#' stays regular at `frequency = 12` for [dft_rast_break()]. Prefer a longer +#' `datetime` window when using this, so enough growing-season history remains +#' to fit a stable BFAST baseline. #' @param mask_values Integer vector of mask-band classes to exclude. When #' `NULL`, uses `mask_values` from [dft_stac_config()] (e.g. Sentinel-2 SCL #' cloud / shadow / cirrus classes). @@ -83,12 +91,32 @@ dft_stac_cube <- function(aoi, aggregation = "median", resampling = "bilinear", cloud_cover_max = 60, + months = NULL, mask_values = NULL, cache_dir = NULL, force = FALSE, sign_fn = rstac::sign_planetary_computer()) { rlang::check_installed("gdalcubes", reason = "to fetch STAC cubes") + # GDAL cloud-read tuning for /vsicurl COG streaming (biggest win: + # DISABLE_READDIR_ON_OPEN avoids a remote directory listing on every open). + # Restored on exit so we don't mutate the caller's session. + gdal_cfg <- c( + GDAL_DISABLE_READDIR_ON_OPEN = "EMPTY_DIR", + GDAL_HTTP_MULTIPLEX = "YES", + GDAL_HTTP_VERSION = "2", + VSI_CACHE = "TRUE", + CPL_VSIL_CURL_ALLOWED_EXTENSIONS = ".tif" + ) + old_cfg <- Sys.getenv(names(gdal_cfg), unset = NA) + do.call(Sys.setenv, as.list(gdal_cfg)) + on.exit({ + set_again <- old_cfg[!is.na(old_cfg)] + if (length(set_again)) do.call(Sys.setenv, as.list(set_again)) + unset <- names(old_cfg)[is.na(old_cfg)] + if (length(unset)) Sys.unsetenv(unset) + }, add = TRUE) + cfg <- dft_stac_config(source) if (!isTRUE(cfg$cube)) { cli::cli_abort(c( @@ -131,7 +159,7 @@ dft_stac_cube <- function(aoi, cache_key <- stac_cube_cache_key( aoi_target, res, target_crs, dt, aggregation, resampling, cfg$stac_url, cfg$collection, band_assets, datetime, index, - cloud_cover_max, mask_values, scale, offset + cloud_cover_max, mask_values, scale, offset, months ) cache_file <- file.path(cache_source_dir, paste0("cube_", cache_key, ".nc")) @@ -157,6 +185,16 @@ dft_stac_cube <- function(aoi, rstac::items_fetch() |> rstac::items_sign(sign_fn = sign_fn) + # Restrict to growing-season (or any) calendar months. Fetching fewer, better + # months both sharpens the vegetation signal (drops snow/low-sun winter noise) + # and cuts the number of scenes streamed. Months with no scenes become NA in + # the monthly cube, so the ts() stays regular at frequency 12. + if (!is.null(months)) { + item_dt <- vapply(items$features, function(f) f$properties$datetime %||% NA_character_, "") + item_mo <- as.integer(format(as.Date(substr(item_dt, 1, 10)), "%m")) + items$features <- items$features[!is.na(item_mo) & item_mo %in% months] + } + n_items <- length(items$features) message(" ", n_items, " items returned") if (n_items == 0) stop("No STAC items found for ", cfg$collection) @@ -207,14 +245,14 @@ dft_stac_cube <- function(aoi, stac_cube_cache_key <- function(aoi_target, res, target_crs, dt, aggregation, resampling, stac_url, collection, band_assets, datetime, index, cloud_cover_max, mask_values, - scale, offset) { + scale, offset, months = NULL) { geom_wkb <- sf::st_as_binary(sf::st_geometry(aoi_target), endian = "little") substr( rlang::hash(list( geom_wkb, as.numeric(res), target_crs, dt, aggregation, resampling, stac_url, collection, band_assets, datetime, index, as.numeric(cloud_cover_max), sort(as.numeric(mask_values)), - as.numeric(scale), as.numeric(offset) + as.numeric(scale), as.numeric(offset), sort(as.numeric(months)) )), 1, 12 ) diff --git a/man/dft_rast_break.Rd b/man/dft_rast_break.Rd index 5db69e5..918f342 100644 --- a/man/dft_rast_break.Rd +++ b/man/dft_rast_break.Rd @@ -10,6 +10,7 @@ dft_rast_break( history = "all", start = c(2022, 1), frequency = NULL, + order = 3, level = 0.01, min_obs = 6, cache_dir = NULL, @@ -34,6 +35,12 @@ Everything before it is the stable history.} temporal cadence; when supplied, it must agree with that cadence or the call errors.} +\item{order}{Integer. Harmonic order of the season-trend model passed to +\code{\link[bfast:bfastmonitor]{bfast::bfastmonitor()}} (default 3). Lower it (1-2) when the series samples +only part of the year (e.g. a growing-season-only cube from +\code{\link[=dft_stac_cube]{dft_stac_cube()}} \code{months}), where a high order overfits sparse seasonal +coverage.} + \item{level}{Numeric. Significance level passed to \code{\link[bfast:bfastmonitor]{bfast::bfastmonitor()}} (default 0.01).} diff --git a/man/dft_stac_cube.Rd b/man/dft_stac_cube.Rd index 5e8c44c..8d20922 100644 --- a/man/dft_stac_cube.Rd +++ b/man/dft_stac_cube.Rd @@ -15,6 +15,7 @@ dft_stac_cube( aggregation = "median", resampling = "bilinear", cloud_cover_max = 60, + months = NULL, mask_values = NULL, cache_dir = NULL, force = FALSE, @@ -50,6 +51,15 @@ must agree with.} \item{cloud_cover_max}{Numeric. Scene-level \code{eo:cloud_cover} maximum percent for the STAC pre-filter (default 60).} +\item{months}{Integer vector of calendar months (1-12) to keep, or \code{NULL} +(default) for all. Restricting to the growing season (e.g. \code{6:9}) both +sharpens the vegetation signal — snow and low-sun winter scenes carry no +vegetation information — and cuts the number of scenes streamed. Months with +no retained scenes become \code{NA} in the monthly cube, so the per-pixel series +stays regular at \code{frequency = 12} for \code{\link[=dft_rast_break]{dft_rast_break()}}. Prefer a longer +\code{datetime} window when using this, so enough growing-season history remains +to fit a stable BFAST baseline.} + \item{mask_values}{Integer vector of mask-band classes to exclude. When \code{NULL}, uses \code{mask_values} from \code{\link[=dft_stac_config]{dft_stac_config()}} (e.g. Sentinel-2 SCL cloud / shadow / cirrus classes).} diff --git a/planning/active/progress.md b/planning/active/progress.md index 75eb7b0..747831e 100644 --- a/planning/active/progress.md +++ b/planning/active/progress.md @@ -32,5 +32,14 @@ build. Proven sibling `dft_stac_fetch` works on the same AOI, and the cube stages work WITHOUT filter_geom (valid kNDVI 0..1). Fix: drop filter_geom; the cube spans the AOI bbox (cloud-masked), callers clip the reduced raster with terra::mask() - (matches how dft_stac_fetch masks). Verifying the fixed E2E, then committing the - fix + Phase 5 (vignette + NEWS + 0.3.0). + (matches how dft_stac_fetch masks). Fixed E2E verified (48-mo cube valid, break + reduce 25 s, no segfault); committed (c6953a0). +- **User steer — growing-season tuning.** All-months fetch was ~30 min for 100 ha + (the fetch, not the compute). User asked whether restricting to the growing season + sharpens the veg signal AND cuts data — yes on both. Added `dft_stac_cube(months=)` + calendar-month filter (default NULL); snow (SCL 11) now in the default S2 mask; GDAL + /vsicurl config (on.exit restore); `dft_rast_break(order=)` harmonic knob. Growing- + season E2E (months=6:9, 2018-2023): cube in 12 min (vs 30), 24 clean summer obs/pixel, + breaks dated to summer months (2022.42-2023.67). 294 unit tests pass, lint clean. +- Next: commit growing-season enhancement + regenerate artifact + Phase 5 (vignette + render + NEWS + 0.3.0 release + archive + PR). diff --git a/tests/testthat/test-dft_rast_break.R b/tests/testthat/test-dft_rast_break.R index a5fde24..5d073cb 100644 --- a/tests/testthat/test-dft_rast_break.R +++ b/tests/testthat/test-dft_rast_break.R @@ -28,7 +28,7 @@ test_that("cadence_frequency maps ISO durations to seasonal frequency", { test_that(".dft_break_pixel returns c(NA, NA) on all-NA input", { expect_equal( drift:::.dft_break_pixel(rep(NA_real_, 24), c(2018, 1), 12, c(2022, 1), - "all", 0.01, 6), + "all", 3, 0.01, 6), c(NA_real_, NA_real_) ) }) @@ -36,7 +36,7 @@ test_that(".dft_break_pixel returns c(NA, NA) on all-NA input", { test_that(".dft_break_pixel returns c(NA, NA) when fewer than min_obs", { v <- c(0.5, 0.6, NA, NA, 0.55, rep(NA_real_, 19)) # 3 non-NA < min_obs 6 expect_equal( - drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 0.01, 6), + drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 3, 0.01, 6), c(NA_real_, NA_real_) ) }) @@ -45,7 +45,7 @@ test_that(".dft_break_pixel returns c(NA, NA) when fewer than min_obs", { test_that("build_break_reducer yields a callback with no free variables", { f <- drift:::build_break_reducer("kndvi", c(2018, 1), 12, c(2022, 1), - "all", 0.01, 6) + "all", 3, 0.01, 6) # environment detached to baseenv and all params inlined as literals, so the # only globals referenced are base/pkg functions -> safe to serialize to a # gdalcubes worker (see findings.md: closures fail in workers) @@ -61,13 +61,19 @@ test_that("break_cache_key changes with each reducer parameter", { skip_if_not_installed("gdalcubes") skip_if_not_installed("bfast") cube <- synthetic_break_cube()$cube - base <- drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 12, 0.01, 6) + k <- function(history = "all", start = c(2022, 1), frequency = 12, order = 3, + level = 0.01, min_obs = 6) { + drift:::break_cache_key(cube, "kndvi", history, start, frequency, order, + level, min_obs) + } + base <- k() expect_match(base, "^[0-9a-f]{12}$") - expect_false(drift:::break_cache_key(cube, "kndvi", "ROC", c(2022, 1), 12, 0.01, 6) == base) - expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2021, 1), 12, 0.01, 6) == base) - expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 1, 0.01, 6) == base) - expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 12, 0.05, 6) == base) - expect_false(drift:::break_cache_key(cube, "kndvi", "all", c(2022, 1), 12, 0.01, 8) == base) + expect_false(k(history = "ROC") == base) + expect_false(k(start = c(2021, 1)) == base) + expect_false(k(frequency = 1) == base) + expect_false(k(order = 1) == base) + expect_false(k(level = 0.05) == base) + expect_false(k(min_obs = 8) == base) }) # ---- bfast-gated per-pixel behavior ---------------------------------------- @@ -77,7 +83,7 @@ test_that(".dft_break_pixel detects an injected step drop (negative magnitude)", tt <- seq_len(72) v <- 0.6 + 0.15 * sin(2 * pi * tt / 12) v[54:72] <- v[54:72] - 0.3 # step drop at 2022-06 (monitoring period) - out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 0.01, 6) + out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 3, 0.01, 6) expect_true(is.finite(out[1])) expect_gt(out[1], 2022) expect_lt(out[1], 2022.9) @@ -88,7 +94,7 @@ test_that(".dft_break_pixel returns NA break on a stable series (non-error)", { skip_if_not_installed("bfast") tt <- seq_len(72) v <- 0.6 + 0.15 * sin(2 * pi * tt / 12) - out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 0.01, 6) + out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 3, 0.01, 6) expect_true(is.na(out[1])) }) diff --git a/tests/testthat/test-dft_stac_config.R b/tests/testthat/test-dft_stac_config.R index a228fc0..95567f0 100644 --- a/tests/testthat/test-dft_stac_config.R +++ b/tests/testthat/test-dft_stac_config.R @@ -42,8 +42,8 @@ test_that("dft_stac_config returns role-based cube config for sentinel-2-l2a", { expect_equal(cfg$roles$swir16, "B11") expect_equal(cfg$roles$mask, "SCL") - # mask values, reflectance affine transform, temporal extent - expect_equal(cfg$mask_values, c(3L, 8L, 9L, 10L)) + # mask values (incl. 11 snow/ice), reflectance affine transform, temporal extent + expect_equal(cfg$mask_values, c(3L, 8L, 9L, 10L, 11L)) expect_equal(cfg$scale, 1e-4) expect_equal(cfg$offset, -0.1) expect_match(cfg$available_datetime, "^\\d{4}-\\d{2}-\\d{2}/\\d{4}-\\d{2}-\\d{2}$") diff --git a/tests/testthat/test-dft_stac_cube.R b/tests/testthat/test-dft_stac_cube.R index 63185b8..cda9b07 100644 --- a/tests/testthat/test-dft_stac_cube.R +++ b/tests/testthat/test-dft_stac_cube.R @@ -34,11 +34,12 @@ cube_key <- function(aoi = square_aoi(), res = 10, target_crs = "EPSG:32609", collection = "sentinel-2-l2a", band_assets = c("B08", "B04"), datetime = "2019-01-01/2023-12-31", index = "kndvi", - cloud_cover_max = 60, mask_values = c(3, 8, 9, 10), - scale = 1e-4, offset = -0.1) { + cloud_cover_max = 60, mask_values = c(3, 8, 9, 10, 11), + scale = 1e-4, offset = -0.1, months = NULL) { drift:::stac_cube_cache_key( aoi, res, target_crs, dt, aggregation, resampling, stac_url, collection, - band_assets, datetime, index, cloud_cover_max, mask_values, scale, offset + band_assets, datetime, index, cloud_cover_max, mask_values, scale, offset, + months ) } @@ -63,6 +64,11 @@ test_that("stac_cube_cache_key changes with each cube-affecting parameter", { expect_false(cube_key(mask_values = c(8, 9)) == base) expect_false(cube_key(scale = 2.75e-5) == base) expect_false(cube_key(offset = -0.2) == base) + expect_false(cube_key(months = 6:9) == base) +}) + +test_that("stac_cube_cache_key normalizes months order", { + expect_equal(cube_key(months = c(6, 7, 8, 9)), cube_key(months = c(9, 8, 7, 6))) }) test_that("stac_cube_cache_key normalizes mask_values order and res type", { From 34ecbd42846540756902f8799ec61f079c8615d8 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 21:04:55 -0700 Subject: [PATCH 07/16] Split Sentinel-2 at the baseline offset boundary; reduce via terra (#30) The Planetary Computer +1000 DN reflectance offset only applies from processing baseline 04.00 (2022-01-25 on); earlier scenes have no offset. A uniform offset was therefore wrong for pre-2022 scenes and produced a false whole-AOI index step at the boundary (validated: 99% of pixels showed a negative "break" dated to exactly the boundary -- an artifact, not vegetation). Fix (baseline-conditional split): - dft_stac_config() sentinel-2-l2a gains offset_boundary = "2022-01-25" and offset_before = 0. - dft_stac_cube() splits items at the boundary, corrects each side with its own offset, and coalesces onto the shared monthly grid. It now returns a terra SpatRaster stack (materialized GeoTIFF, time per layer) instead of a gdalcubes cube -- gdalcubes cannot read a terra-written NetCDF, so the coalesce lives in terra. - dft_rast_break() reduces the stack per pixel via parallel::mclapply (forked workers inherit the namespace and closures, so the whole gdalcubes reduce_time worker-serialization machinery -- build_break_reducer, load_pkgs -- is gone; .dft_break_pixel is unchanged). ts start / frequency derive from the layer times. Validated end-to-end on 2018-2023 growing-season Sentinel-2: split fired 75 pre / 85 post, per-year kNDVI now aligns across 2022 (2021=0.52, 2022=0.53), and breaks fell from 99% to 25% of usable pixels -- real change, not the boundary artifact. Addresses the offset half of #32. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- NEWS.md | 8 ++ R/dft_rast_break.R | 180 ++++++++++---------------- R/dft_stac_config.R | 7 +- R/dft_stac_cube.R | 121 +++++++++++------ man/dft_rast_break.Rd | 36 ++---- man/dft_stac_cube.Rd | 31 +++-- planning/active/findings.md | 32 +++++ planning/active/progress.md | 20 ++- tests/testthat/helper-break.R | 48 +++---- tests/testthat/test-dft_rast_break.R | 98 +++++--------- tests/testthat/test-dft_stac_config.R | 5 + tests/testthat/test-dft_stac_cube.R | 17 ++- 12 files changed, 311 insertions(+), 292 deletions(-) diff --git a/NEWS.md b/NEWS.md index a07e71d..1760fe1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,11 @@ +# drift 0.3.0 + +- Continuous index-trajectory change detection for floodplain reaches (#30). A new fetch-and-reduce pipeline complements the categorical `dft_stac_fetch()` path. `dft_stac_cube()` builds a cloud-masked monthly spectral-index stack from Sentinel-2 (via a new `"sentinel-2-l2a"` source), and `dft_rast_break()` reduces it per pixel with `bfast::bfastmonitor()` into a two-band raster of break date (decimal year) and magnitude (negative = index drop, e.g. scour or vegetation loss). Where categorical differencing compares two land-cover labels, this detects *when* a pixel's spectral history breaks and by how much. See the "Continuous Index-Trajectory Change Detection" vignette. +- `dft_index_expr()` and `dft_index_table()` add a table-driven spectral-index registry (NDVI, kNDVI, NDMI) whose formulas are written over band *roles*, so one index resolves against any reflectance source; the reflectance scale/offset is folded into each expression. +- Sentinel-2 handling is correctness-focused: `dft_stac_cube()` masks cloud/shadow/cirrus/snow, restricts to caller-chosen calendar `months` (e.g. the growing season) to sharpen the signal and cut scenes streamed, and — because the +1000 DN reflectance offset only applies from processing baseline 04.00 (2022-01-25) — splits items at that boundary and corrects each side, so a multi-year series carries no artificial index step at 2022. +- `dft_stac_config()` gains a role-based schema for reflectance cube sources (band roles, mask classes, scale/offset, offset boundary), leaving the categorical `io-lulc`/`esa-worldcover` sources unchanged. `bfast` added to Suggests. +- Known limitation tracked as a follow-up: the cube spans the AOI bounding box rather than the polygon (a gdalcubes `filter_geom` limitation, #32); labelling breaks with from/to land-cover classes is #31. + # drift 0.2.4 - `dft_transition_vectors()` no longer exhausts memory on large-extent rasters (#27). The per-class loop allocated full-grid vectors per class and per patch — ncell × n_patches churn that OOM-killed a 102.6M-cell, 56-class floodplain. Replaced by a single `terra::patches(values = TRUE)` pass plus a sparse patch-to-label map. Output is identical (verified patch-by-patch against the old implementation); only `patch_id` numbering / row order changes, to raster scan order. Benchmark at 24M cells: 1.9 s for a 4,799-patch raster; the old code took 122 s on a milder 1,232-patch raster of the same size. diff --git a/R/dft_rast_break.R b/R/dft_rast_break.R index 7c1a76c..530cd02 100644 --- a/R/dft_rast_break.R +++ b/R/dft_rast_break.R @@ -1,6 +1,6 @@ #' Detect per-pixel index-trajectory breakpoints #' -#' Reduce an index cube (from [dft_stac_cube()]) over time with +#' Reduce a monthly index stack (from [dft_stac_cube()]) over time with #' [bfast::bfastmonitor()], returning a two-band `SpatRaster` of the break date #' and magnitude for every pixel. Where categorical differencing compares two #' land-cover labels, this asks a stronger question of a continuous index @@ -13,24 +13,20 @@ #' means it rose (establishment). `break_date` is a decimal year (e.g. #' `2022.42`) or `NA` where no break was detected. #' -#' The reducer runs as a per-pixel R callback. gdalcubes evaluates that callback -#' in spawned worker processes, so the function is built self-contained (its -#' parameters are baked in as literals) and `bfast` is loaded in each worker — -#' making the result independent of the `gdalcubes::gdalcubes_options(parallel=)` -#' setting. +#' Pixels are reduced in parallel with `parallel::mclapply()` (forked workers, so +#' the per-pixel logic and its parameters are inherited directly). Pixels with +#' fewer than `min_obs` valid observations short-circuit to `NA`. #' -#' @param cube A `gdalcubes` index cube whose single band is a spectral index -#' time series (the return value of [dft_stac_cube()]). -#' @param band Character. Band to reduce. When `NULL`, the cube's first band. +#' @param cube A monthly index `SpatRaster` (the return value of +#' [dft_stac_cube()]): one layer per time step, with a time value per layer. #' @param history Character. `bfastmonitor` history-selection method: `"all"` #' (default), `"ROC"`, or `"BP"`. #' @param start Numeric `c(year, period)`. Start of the monitoring period, in the -#' cube's temporal frequency (e.g. `c(2022, 1)` = Jan 2022 for a monthly cube). -#' Everything before it is the stable history. +#' stack's temporal frequency (e.g. `c(2022, 1)` = Jan 2022 for a monthly +#' stack). Everything before it is the stable history. #' @param frequency Numeric or `NULL`. Seasonal frequency of the time series -#' (12 for monthly, 1 for annual). When `NULL`, derived from the cube's -#' temporal cadence; when supplied, it must agree with that cadence or the -#' call errors. +#' (12 for monthly, 1 for annual). When `NULL`, derived from the layer time +#' spacing; when supplied, it must agree with that spacing or the call errors. #' @param order Integer. Harmonic order of the season-trend model passed to #' [bfast::bfastmonitor()] (default 3). Lower it (1-2) when the series samples #' only part of the year (e.g. a growing-season-only cube from @@ -40,13 +36,13 @@ #' (default 0.01). #' @param min_obs Integer. Minimum non-`NA` observations required to attempt a #' fit; pixels with fewer return `NA` (default 6). -#' @param cache_dir Character. Cache directory. When `NULL`, [dft_cache_path()]. -#' @param force Logical. Recompute even if cached (default `FALSE`). +#' @param cores Integer or `NULL`. Forked workers for the per-pixel reduction. +#' When `NULL`, uses one fewer than the detected cores. #' #' @return A two-band [terra::SpatRaster] with layers `break_date` (decimal year #' or `NA`) and `break_mag` (signed index change; negative = index drop). #' -#' @seealso [dft_stac_cube()] (builds the input cube), [dft_index_expr()]. +#' @seealso [dft_stac_cube()] (builds the input stack), [dft_index_expr()]. #' #' @examples #' \dontrun{ @@ -59,74 +55,75 @@ #' #' @export dft_rast_break <- function(cube, - band = NULL, history = "all", start = c(2022, 1), frequency = NULL, order = 3, level = 0.01, min_obs = 6, - cache_dir = NULL, - force = FALSE) { - rlang::check_installed("gdalcubes", reason = "to reduce a data cube over time") + cores = NULL) { rlang::check_installed("bfast", reason = "for trajectory breakpoint detection") - - band <- band %||% gdalcubes::bands(cube)$name[[1]] - - # derive the ts() start and seasonal frequency from the cube time axis, in the - # main process (the reducer callback cannot see the cube) - tdim <- gdalcubes::dimensions(cube)$t - cadence_freq <- cadence_frequency(tdim$pixel_size) - if (is.na(cadence_freq)) { + if (!inherits(cube, "SpatRaster")) { + cli::cli_abort("`cube` must be a SpatRaster time stack from {.fn dft_stac_cube}.") + } + tm <- terra::time(cube) + if (length(tm) != terra::nlyr(cube) || anyNA(tm)) { cli::cli_abort(c( - "Unsupported cube temporal cadence {.val {tdim$pixel_size}}.", - "i" = "Use a monthly (`P1M`) or annual (`P1Y`) cube." + "`cube` must carry a time value for every layer.", + "i" = "Pass the stack returned by {.fn dft_stac_cube}." )) } + + # derive the ts() start and seasonal frequency from the layer times + cadence_freq <- cadence_frequency(tm) + if (is.na(cadence_freq)) { + cli::cli_abort("Unsupported layer cadence; use a monthly or annual stack.") + } if (is.null(frequency)) { frequency <- cadence_freq } else if (!isTRUE(all.equal(as.numeric(frequency), as.numeric(cadence_freq)))) { cli::cli_abort(c( - "`frequency` ({frequency}) disagrees with the cube cadence \\ - {.val {tdim$pixel_size}} (= {cadence_freq}).", - "i" = "Leave `frequency = NULL` to derive it from the cube." + "`frequency` ({frequency}) disagrees with the layer cadence (= {cadence_freq}).", + "i" = "Leave `frequency = NULL` to derive it from the stack." )) } - t0_date <- as.Date(tdim$low) - yr <- as.integer(format(t0_date, "%Y")) - mo <- as.integer(format(t0_date, "%m")) - period <- floor((mo - 1) / (12 / frequency)) + 1 - ts_start <- c(yr, period) + t0 <- as.Date(tm[1]) + yr <- as.integer(format(t0, "%Y")) + mo <- as.integer(format(t0, "%m")) + ts_start <- c(yr, floor((mo - 1) / (12 / frequency)) + 1) - # cache keyed by the full cube definition + reducer parameters - cache_base <- dft_cache_path(cache_dir) - cache_break_dir <- file.path(cache_base, "break") - dir.create(cache_break_dir, recursive = TRUE, showWarnings = FALSE) - key <- break_cache_key(cube, band, history, start, frequency, order, level, - min_obs) - cache_file <- file.path(cache_break_dir, paste0("break_", key, ".nc")) + if (is.null(cores)) { + dc <- parallel::detectCores() + cores <- if (is.na(dc)) 2L else max(1L, dc - 1L) + } - if (!force && file.exists(cache_file)) { - message(" break: cached") - return(terra::rast(cache_file)) + # reduce only pixels with a usable series; the rest stay NA + vals <- terra::values(cube) + usable <- which(rowSums(!is.na(vals)) >= min_obs) + res <- matrix(NA_real_, nrow(vals), 2) + if (length(usable)) { + chunks <- split(usable, (seq_along(usable) - 1) %% cores) + parts <- parallel::mclapply(chunks, function(ii) { + t(vapply(ii, function(i) { + .dft_break_pixel(vals[i, ], ts_start, frequency, start, history, order, + level, min_obs) + }, numeric(2))) + }, mc.cores = cores) + for (k in seq_along(chunks)) res[chunks[[k]], ] <- parts[[k]] } - fun <- build_break_reducer(band, ts_start, frequency, start, history, order, - level, min_obs) - reduced <- gdalcubes::reduce_time( - cube, names = c("break_date", "break_mag"), - load_pkgs = "bfast", FUN = fun - ) - gdalcubes::write_ncdf(reduced, cache_file, overwrite = TRUE) - terra::rast(cache_file) + out <- cube[[1:2]] + terra::values(out) <- res + names(out) <- c("break_date", "break_mag") + out } #' Per-pixel breakpoint reducer logic (internal, unit-testable) #' -#' Canonical logic shared by the exported reducer. The degenerate branches -#' (all-`NA` or fewer than `min_obs` observations) return `c(NA, NA)` before any -#' `bfast` symbol is touched, so they are testable without bfast installed. +#' The degenerate branches (all-`NA` or fewer than `min_obs` observations) return +#' `c(NA, NA)` before any `bfast` symbol is touched, so they are testable without +#' bfast installed. #' @noRd .dft_break_pixel <- function(v, ts_start, frequency, start, history, order, level, min_obs) { @@ -140,58 +137,17 @@ dft_rast_break <- function(cube, } -#' Seasonal frequency implied by a gdalcubes ISO cadence (internal) -#' -#' `P1M` -> 12, `P3M` -> 4, `P1Y` -> 1. Returns `NA` for unsupported cadences. -#' @noRd -cadence_frequency <- function(dt_iso) { - m <- regmatches(dt_iso, regexec("^P(\\d+)([MY])$", dt_iso))[[1]] - if (length(m) != 3) return(NA_real_) - n <- as.numeric(m[2]) - switch(m[3], - "M" = 12 / n, - "Y" = 1 / n, - NA_real_ - ) -} - - -#' Build a self-contained per-pixel reducer for reduce_time (internal) -#' -#' gdalcubes serializes the callback into worker processes and does not restore -#' the calling environment, so a closure over enclosing locals fails there. This -#' embeds [.dft_break_pixel()] as a literal function object (detached to -#' `baseenv()`) and inlines every parameter as a literal, yielding a callback -#' with no free variables that runs correctly under any `parallel` setting. -#' @noRd -build_break_reducer <- function(band, ts_start, frequency, start, history, - order, level, min_obs) { - pixfun <- .dft_break_pixel - environment(pixfun) <- baseenv() - f <- function(x) NULL - body(f) <- substitute( - PIX(x[BAND, ], TS, FRQ, ST, HI, OR, LV, MN), - list(PIX = pixfun, BAND = band, TS = ts_start, FRQ = frequency, - ST = start, HI = history, OR = order, LV = level, MN = min_obs) - ) - environment(f) <- baseenv() - f -} - - -#' Cache key for one break reduction (internal) +#' Seasonal frequency implied by a stack's layer times (internal) #' -#' Hashes the full cube definition (`gdalcubes::as_json`, which captures the -#' source items, view, mask, and index) plus every reducer parameter. +#' Monthly spacing -> 12, quarterly -> 4, annual -> 1. Returns `NA` for +#' unsupported cadences. #' @noRd -break_cache_key <- function(cube, band, history, start, frequency, order, level, - min_obs) { - substr( - rlang::hash(list( - gdalcubes::as_json(cube), band, history, as.numeric(start), - as.numeric(frequency), as.numeric(order), as.numeric(level), - as.numeric(min_obs) - )), - 1, 12 - ) +cadence_frequency <- function(tm) { + if (length(tm) < 2) return(NA_real_) + d <- stats::median(as.numeric(diff(as.Date(tm)))) + if (is.na(d)) return(NA_real_) + if (d >= 26 && d <= 32) return(12) + if (d >= 85 && d <= 95) return(4) + if (d >= 360 && d <= 370) return(1) + NA_real_ } diff --git a/R/dft_stac_config.R b/R/dft_stac_config.R index ed9e34d..a63f4ec 100644 --- a/R/dft_stac_config.R +++ b/R/dft_stac_config.R @@ -64,9 +64,14 @@ dft_stac_config <- function(source = c("io-lulc", "esa-worldcover", # (snow is masked by default: it is never a vegetation signal and otherwise # dominates the winter trajectory at high latitudes) mask_values = c(3L, 8L, 9L, 10L, 11L), - # DN -> reflectance: baseline 04.00 (+1000 DN) => DN * 1e-4 - 0.1 + # DN -> reflectance. The +1000 DN offset only applies from processing + # baseline 04.00 (2022-01-25 on); earlier scenes have no offset. dft_stac_cube + # splits items at the boundary and applies `offset` after it, `offset_before` + # before it, so a multi-year series has no artificial step at 2022. scale = 1e-4, offset = -0.1, + offset_boundary = "2022-01-25", + offset_before = 0, available_datetime = "2017-01-01/2024-12-31" ) # "landsat-c2-l2" drops in with the same cube shape, no API change: diff --git a/R/dft_stac_cube.R b/R/dft_stac_cube.R index 4cdff07..711ebc0 100644 --- a/R/dft_stac_cube.R +++ b/R/dft_stac_cube.R @@ -2,16 +2,16 @@ #' #' Sibling of [dft_stac_fetch()] for continuous change detection. Where #' [dft_stac_fetch()] materializes one categorical raster per year, this builds -#' a multi-band, sub-annual reflectance cube, masks clouds, computes a spectral -#' index over band roles, and returns the lazy single-band index cube — the -#' input to [dft_rast_break()] for per-pixel trajectory breakpoint detection. +#' a sub-annual reflectance cube, masks clouds, computes a spectral index over +#' band roles, and returns the index time series as a `SpatRaster` (one layer +#' per time step) — the input to [dft_rast_break()] for per-pixel trajectory +#' breakpoint detection. #' -#' The index cube is materialized once to a NetCDF file under [dft_cache_path()] -#' as `/cube_.nc` (the full time axis, one band), keyed by a hash of -#' the AOI geometry and every cube-affecting parameter. Because the cube is -#' invariant to [dft_rast_break()]'s parameters (`history`/`start`/`level`), -#' caching it here makes bfast parameter sweeps cheap — they re-read the local -#' `.nc` instead of re-streaming COGs. +#' The index stack is materialized once to a GeoTIFF under [dft_cache_path()] +#' as `/cube_.tif`, keyed by a hash of the AOI geometry and every +#' cube-affecting parameter. Because it is invariant to [dft_rast_break()]'s +#' parameters, caching it here makes bfast parameter sweeps cheap — they re-read +#' the local raster instead of re-streaming COGs. #' #' Three STAC-query specifics distinguish cube mode from [dft_stac_fetch()]: #' pagination via [rstac::items_fetch()] is mandatory (a monthly multi-year query @@ -52,15 +52,18 @@ #' cloud / shadow / cirrus classes). #' @param cache_dir Character. Cache directory. When `NULL`, uses #' [dft_cache_path()]. -#' @param force Logical. Re-fetch even if cached, overwriting the cached `.nc` +#' @param force Logical. Re-fetch even if cached, overwriting the cached raster #' (default `FALSE`). #' @param sign_fn A signing function for STAC assets. Default is #' [rstac::sign_planetary_computer()]. #' -#' @return A `gdalcubes` data cube with a single band named `index`, backed by -#' the cached NetCDF file. The cube spans the AOI **bounding box** (cloud-masked -#' but not clipped to the AOI polygon); clip the reduced raster from -#' [dft_rast_break()] with `terra::mask()` if a tight AOI is needed. +#' @return A [terra::SpatRaster] index stack — one layer per time step, with a +#' time value per layer — cached as a GeoTIFF. The stack spans the AOI +#' **bounding box** (cloud-masked but not clipped to the AOI polygon); clip the +#' reduced raster from [dft_rast_break()] with `terra::mask()` if a tight AOI is +#' needed. For sources with a reflectance-offset baseline boundary (Sentinel-2), +#' items are split at the boundary and offset-corrected per side, so a series +#' crossing it carries no artificial index step. #' #' @seealso [dft_rast_break()] (the reducer that consumes this cube), #' [dft_index_expr()] (the index applied), [dft_stac_fetch()] (categorical @@ -128,6 +131,12 @@ dft_stac_cube <- function(aoi, mask_values <- mask_values %||% cfg$mask_values scale <- cfg$scale %||% 1 offset <- cfg$offset %||% 0 + # sources whose reflectance offset changes at a processing-baseline boundary + # (e.g. Sentinel-2 +1000 DN from 2022-01-25) carry the boundary date and the + # pre-boundary offset; the fetch splits items at the boundary and corrects + # each side, so a series crossing it has no artificial index step. + offset_boundary <- cfg$offset_boundary + offset_before <- cfg$offset_before %||% 0 # Ensure aoi is sf if (inherits(aoi, "SpatVector")) aoi <- sf::st_as_sf(aoi) @@ -159,13 +168,20 @@ dft_stac_cube <- function(aoi, cache_key <- stac_cube_cache_key( aoi_target, res, target_crs, dt, aggregation, resampling, cfg$stac_url, cfg$collection, band_assets, datetime, index, - cloud_cover_max, mask_values, scale, offset, months + cloud_cover_max, mask_values, scale, offset, months, offset_before ) - cache_file <- file.path(cache_source_dir, paste0("cube_", cache_key, ".nc")) + cache_file <- file.path(cache_source_dir, paste0("cube_", cache_key, ".tif")) + + # monthly layer times, derived from the datetime window start + month_times <- function(n) { + seq(as.Date(paste0(substr(dr[1], 1, 7), "-01")), by = "month", length.out = n) + } if (!force && file.exists(cache_file)) { message(" cube: cached") - return(gdalcubes::ncdf_cube(cache_file)) + r <- terra::rast(cache_file) + terra::time(r) <- month_times(terra::nlyr(r)) + return(r) } # STAC query: intersects (not bbox) + scene cloud pre-filter + pagination @@ -199,12 +215,6 @@ dft_stac_cube <- function(aoi, message(" ", n_items, " items returned") if (n_items == 0) stop("No STAC items found for ", cfg$collection) - # image_mask masks the categorical mask band at read time, before the - # cube_view resampling touches reflectance bands - col <- gdalcubes::stac_image_collection( - items$features, asset_names = c(band_assets, mask_asset) - ) - v <- gdalcubes::cube_view( srs = target_crs, extent = list( @@ -216,27 +226,58 @@ dft_stac_cube <- function(aoi, aggregation = aggregation, resampling = resampling ) - # The cube spans the AOI bounding box. Clipping to the AOI polygon with - # gdalcubes::filter_geom() inside the pipeline yields an all-NA cube (and can - # crash the compute worker) on the pinned gdalcubes build, so we mask clouds - # here and leave polygon clipping to the caller (terra::mask() on the reduced - # raster), matching how the categorical sibling dft_stac_fetch() masks. - cube <- gdalcubes::raster_cube( - col, v, - mask = gdalcubes::image_mask(mask_asset, values = mask_values) - ) + # Build the index cube for one item subset with one offset, materialize it, and + # read it back as a terra stack. The cube spans the AOI bounding box: + # gdalcubes::filter_geom() to clip to the polygon yields an all-NA cube (and can + # crash the compute worker) on the pinned build, so we mask clouds here and + # leave polygon clipping to the caller, as the sibling dft_stac_fetch() does. + build_index_stack <- function(features, offset_use) { + img_col <- gdalcubes::stac_image_collection( + features, asset_names = c(band_assets, mask_asset) + ) + cube <- gdalcubes::raster_cube( + img_col, v, mask = gdalcubes::image_mask(mask_asset, values = mask_values) + ) + idx <- dft_index_expr(cube, index = index, source = source, + roles = cfg$roles, scale = scale, offset = offset_use) + tmp <- tempfile(fileext = ".nc") + gdalcubes::write_ncdf(idx, tmp, overwrite = TRUE) + terra::rast(tmp) + } - idx <- dft_index_expr(cube, index = index, source = source, - roles = cfg$roles, scale = scale, offset = offset) + # Baseline-conditional offset: split items at the boundary and correct each + # side with its own offset, then coalesce onto the shared monthly grid. Both + # subcubes are built over the full view so their layers align for terra::cover. + is_pre <- rep(FALSE, length(items$features)) + if (!is.null(offset_boundary)) { + item_date <- as.Date(substr( + vapply(items$features, function(f) f$properties$datetime %||% NA_character_, ""), + 1, 10 + )) + is_pre <- !is.na(item_date) & item_date < as.Date(offset_boundary) + } + + if (any(is_pre) && !all(is_pre)) { + message(" offset split at ", offset_boundary, ": ", + sum(is_pre), " pre / ", sum(!is_pre), " post") + stk <- terra::cover( + build_index_stack(items$features[is_pre], offset_before), + build_index_stack(items$features[!is_pre], offset) + ) + } else { + stk <- build_index_stack(items$features, if (all(is_pre)) offset_before else offset) + } - gdalcubes::write_ncdf(idx, cache_file, overwrite = TRUE) - gdalcubes::ncdf_cube(cache_file) + terra::time(stk) <- month_times(terra::nlyr(stk)) + names(stk) <- rep(index, terra::nlyr(stk)) + terra::writeRaster(stk, cache_file, overwrite = TRUE) + stk } #' Cache key for one STAC index-cube parameter set #' -#' Cube-mode analogue of [stac_cache_key()] (kept separate so the fetch key +#' Cube-mode analogue of `stac_cache_key()` (kept separate so the fetch key #' stays byte-for-byte stable). Hashes the AOI geometry as WKB plus every #' parameter that changes the written index cube. `res` is coerced to double so #' `10L` and `10` key alike; `mask_values` is sorted so order does not matter. @@ -245,14 +286,16 @@ dft_stac_cube <- function(aoi, stac_cube_cache_key <- function(aoi_target, res, target_crs, dt, aggregation, resampling, stac_url, collection, band_assets, datetime, index, cloud_cover_max, mask_values, - scale, offset, months = NULL) { + scale, offset, months = NULL, + offset_before = 0) { geom_wkb <- sf::st_as_binary(sf::st_geometry(aoi_target), endian = "little") substr( rlang::hash(list( geom_wkb, as.numeric(res), target_crs, dt, aggregation, resampling, stac_url, collection, band_assets, datetime, index, as.numeric(cloud_cover_max), sort(as.numeric(mask_values)), - as.numeric(scale), as.numeric(offset), sort(as.numeric(months)) + as.numeric(scale), as.numeric(offset), sort(as.numeric(months)), + as.numeric(offset_before) )), 1, 12 ) diff --git a/man/dft_rast_break.Rd b/man/dft_rast_break.Rd index 918f342..6e4b152 100644 --- a/man/dft_rast_break.Rd +++ b/man/dft_rast_break.Rd @@ -6,34 +6,29 @@ \usage{ dft_rast_break( cube, - band = NULL, history = "all", start = c(2022, 1), frequency = NULL, order = 3, level = 0.01, min_obs = 6, - cache_dir = NULL, - force = FALSE + cores = NULL ) } \arguments{ -\item{cube}{A \code{gdalcubes} index cube whose single band is a spectral index -time series (the return value of \code{\link[=dft_stac_cube]{dft_stac_cube()}}).} - -\item{band}{Character. Band to reduce. When \code{NULL}, the cube's first band.} +\item{cube}{A monthly index \code{SpatRaster} (the return value of +\code{\link[=dft_stac_cube]{dft_stac_cube()}}): one layer per time step, with a time value per layer.} \item{history}{Character. \code{bfastmonitor} history-selection method: \code{"all"} (default), \code{"ROC"}, or \code{"BP"}.} \item{start}{Numeric \code{c(year, period)}. Start of the monitoring period, in the -cube's temporal frequency (e.g. \code{c(2022, 1)} = Jan 2022 for a monthly cube). -Everything before it is the stable history.} +stack's temporal frequency (e.g. \code{c(2022, 1)} = Jan 2022 for a monthly +stack). Everything before it is the stable history.} \item{frequency}{Numeric or \code{NULL}. Seasonal frequency of the time series -(12 for monthly, 1 for annual). When \code{NULL}, derived from the cube's -temporal cadence; when supplied, it must agree with that cadence or the -call errors.} +(12 for monthly, 1 for annual). When \code{NULL}, derived from the layer time +spacing; when supplied, it must agree with that spacing or the call errors.} \item{order}{Integer. Harmonic order of the season-trend model passed to \code{\link[bfast:bfastmonitor]{bfast::bfastmonitor()}} (default 3). Lower it (1-2) when the series samples @@ -47,16 +42,15 @@ coverage.} \item{min_obs}{Integer. Minimum non-\code{NA} observations required to attempt a fit; pixels with fewer return \code{NA} (default 6).} -\item{cache_dir}{Character. Cache directory. When \code{NULL}, \code{\link[=dft_cache_path]{dft_cache_path()}}.} - -\item{force}{Logical. Recompute even if cached (default \code{FALSE}).} +\item{cores}{Integer or \code{NULL}. Forked workers for the per-pixel reduction. +When \code{NULL}, uses one fewer than the detected cores.} } \value{ A two-band \link[terra:SpatRaster-class]{terra::SpatRaster} with layers \code{break_date} (decimal year or \code{NA}) and \code{break_mag} (signed index change; negative = index drop). } \description{ -Reduce an index cube (from \code{\link[=dft_stac_cube]{dft_stac_cube()}}) over time with +Reduce a monthly index stack (from \code{\link[=dft_stac_cube]{dft_stac_cube()}}) over time with \code{\link[bfast:bfastmonitor]{bfast::bfastmonitor()}}, returning a two-band \code{SpatRaster} of the break date and magnitude for every pixel. Where categorical differencing compares two land-cover labels, this asks a stronger question of a continuous index @@ -70,11 +64,9 @@ means the index dropped} (e.g. vegetation loss / channel scour), positive means it rose (establishment). \code{break_date} is a decimal year (e.g. \code{2022.42}) or \code{NA} where no break was detected. -The reducer runs as a per-pixel R callback. gdalcubes evaluates that callback -in spawned worker processes, so the function is built self-contained (its -parameters are baked in as literals) and \code{bfast} is loaded in each worker — -making the result independent of the \code{gdalcubes::gdalcubes_options(parallel=)} -setting. +Pixels are reduced in parallel with \code{parallel::mclapply()} (forked workers, so +the per-pixel logic and its parameters are inherited directly). Pixels with +fewer than \code{min_obs} valid observations short-circuit to \code{NA}. } \examples{ \dontrun{ @@ -87,5 +79,5 @@ terra::plot(breaks[["break_mag"]]) # negative (blue) = scour / veg loss } \seealso{ -\code{\link[=dft_stac_cube]{dft_stac_cube()}} (builds the input cube), \code{\link[=dft_index_expr]{dft_index_expr()}}. +\code{\link[=dft_stac_cube]{dft_stac_cube()}} (builds the input stack), \code{\link[=dft_index_expr]{dft_index_expr()}}. } diff --git a/man/dft_stac_cube.Rd b/man/dft_stac_cube.Rd index 8d20922..df566fe 100644 --- a/man/dft_stac_cube.Rd +++ b/man/dft_stac_cube.Rd @@ -67,32 +67,35 @@ cloud / shadow / cirrus classes).} \item{cache_dir}{Character. Cache directory. When \code{NULL}, uses \code{\link[=dft_cache_path]{dft_cache_path()}}.} -\item{force}{Logical. Re-fetch even if cached, overwriting the cached \code{.nc} +\item{force}{Logical. Re-fetch even if cached, overwriting the cached raster (default \code{FALSE}).} \item{sign_fn}{A signing function for STAC assets. Default is \code{\link[rstac:items_sign_planetary_computer]{rstac::sign_planetary_computer()}}.} } \value{ -A \code{gdalcubes} data cube with a single band named \code{index}, backed by -the cached NetCDF file. The cube spans the AOI \strong{bounding box} (cloud-masked -but not clipped to the AOI polygon); clip the reduced raster from -\code{\link[=dft_rast_break]{dft_rast_break()}} with \code{terra::mask()} if a tight AOI is needed. +A \link[terra:SpatRaster-class]{terra::SpatRaster} index stack — one layer per time step, with a +time value per layer — cached as a GeoTIFF. The stack spans the AOI +\strong{bounding box} (cloud-masked but not clipped to the AOI polygon); clip the +reduced raster from \code{\link[=dft_rast_break]{dft_rast_break()}} with \code{terra::mask()} if a tight AOI is +needed. For sources with a reflectance-offset baseline boundary (Sentinel-2), +items are split at the boundary and offset-corrected per side, so a series +crossing it carries no artificial index step. } \description{ Sibling of \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} for continuous change detection. Where \code{\link[=dft_stac_fetch]{dft_stac_fetch()}} materializes one categorical raster per year, this builds -a multi-band, sub-annual reflectance cube, masks clouds, computes a spectral -index over band roles, and returns the lazy single-band index cube — the -input to \code{\link[=dft_rast_break]{dft_rast_break()}} for per-pixel trajectory breakpoint detection. +a sub-annual reflectance cube, masks clouds, computes a spectral index over +band roles, and returns the index time series as a \code{SpatRaster} (one layer +per time step) — the input to \code{\link[=dft_rast_break]{dft_rast_break()}} for per-pixel trajectory +breakpoint detection. } \details{ -The index cube is materialized once to a NetCDF file under \code{\link[=dft_cache_path]{dft_cache_path()}} -as \verb{/cube_.nc} (the full time axis, one band), keyed by a hash of -the AOI geometry and every cube-affecting parameter. Because the cube is -invariant to \code{\link[=dft_rast_break]{dft_rast_break()}}'s parameters (\code{history}/\code{start}/\code{level}), -caching it here makes bfast parameter sweeps cheap — they re-read the local -\code{.nc} instead of re-streaming COGs. +The index stack is materialized once to a GeoTIFF under \code{\link[=dft_cache_path]{dft_cache_path()}} +as \verb{/cube_.tif}, keyed by a hash of the AOI geometry and every +cube-affecting parameter. Because it is invariant to \code{\link[=dft_rast_break]{dft_rast_break()}}'s +parameters, caching it here makes bfast parameter sweeps cheap — they re-read +the local raster instead of re-streaming COGs. Three STAC-query specifics distinguish cube mode from \code{\link[=dft_stac_fetch]{dft_stac_fetch()}}: pagination via \code{\link[rstac:items_functions]{rstac::items_fetch()}} is mandatory (a monthly multi-year query diff --git a/planning/active/findings.md b/planning/active/findings.md index 3e04e96..40592ac 100644 --- a/planning/active/findings.md +++ b/planning/active/findings.md @@ -118,6 +118,38 @@ monitoring-period residual (neg = index drop = scour; ~0 = stable). `ts_start` and `frequency` come from `gdalcubes::dimension_values(cube, "M")$t` in the main process; `stop()` if a caller `frequency` disagrees with the dt cadence. +## Sentinel-2 baseline offset — the correctness catch (2026-07-08) + +A peer validation session + my own growing-season E2E stats surfaced that PC's +`sentinel-2-l2a` +1000 DN reflectance offset only applies from processing +baseline 04.00 (2022-01-25 on); earlier scenes have offset 0. A **uniform** +`offset = -0.1` is therefore wrong pre-2022 and creates a false whole-AOI index +step at the boundary. kNDVI's `tanh` hid it (bounded 0-1) so the cube looked +valid, but my E2E had **90903/91467 (99%) negative breaks all at 2022.42** — the +boundary, not vegetation. User chose to fix it properly (baseline-conditional +split), not defer. + +**terra re-architecture (validated):** +- gdalcubes CANNOT read a terra-written NetCDF (dim-name mismatch), so coalescing + pre/post cubes at the gdalcubes level is impossible. Pivoted to terra. +- `dft_stac_cube()` returns a **terra SpatRaster stack** (materialized GeoTIFF, + `terra::time` set). If the source has `offset_boundary` and items straddle it, + it builds a pre cube (offset `offset_before`) and post cube (offset) over the + SAME full `cube_view`, and coalesces with `terra::cover(pre, post)`. +- `dft_rast_break()` reduces the stack via `parallel::mclapply` (fork -> closures + and package internals inherited, no gdalcubes-worker serialization). Validated: + 102400 px in 8.3 s. `.dft_break_pixel` unchanged. Dropped build_break_reducer, + break_cache_key, gdalcubes reduce_time. +- `cadence_frequency()` now derives frequency from the median day-spacing of the + layer times (26-32d -> 12, 85-95d -> 4, 360-370d -> 1). + +**Split validation E2E (2018-2023, months=6:9):** split fired 75 pre / 85 post; +per-year growing-season mean kNDVI now ALIGNS across the boundary +(2021=0.524 -> 2022=0.529, all years 0.41-0.53, no jump); finite breaks dropped +from 99% to **25% of usable pixels**, spread across 2022-2024. Offset artifact +gone. Config: `sentinel-2-l2a` gains `offset_boundary="2022-01-25"`, +`offset_before=0`. + ## Reuse map - `stac_cache_key()` / `auto_utm_epsg()` / sf-coercion / `write_ncdf→terra→mask` idiom in `R/dft_stac_fetch.R` — reuse for `dft_stac_cube()`. diff --git a/planning/active/progress.md b/planning/active/progress.md index 747831e..504f30e 100644 --- a/planning/active/progress.md +++ b/planning/active/progress.md @@ -41,5 +41,21 @@ /vsicurl config (on.exit restore); `dft_rast_break(order=)` harmonic knob. Growing- season E2E (months=6:9, 2018-2023): cube in 12 min (vs 30), 24 clean summer obs/pixel, breaks dated to summer months (2022.42-2023.67). 294 unit tests pass, lint clean. -- Next: commit growing-season enhancement + regenerate artifact + Phase 5 (vignette - render + NEWS + 0.3.0 release + archive + PR). +- Committed growing-season enhancement (61eaa5d); code-check Clean. +- **Peer session + my E2E stats surfaced a real offset bug.** PC's uniform offset=-0.1 + is wrong for pre-2022-01-25 scenes (the +1000 DN baseline offset flips there). A + series crossing the boundary shows a false whole-AOI break: my own growing-season + E2E had 90903/91467 (99%) negative breaks all at 2022.42 — the offset boundary, not + vegetation. User chose to fix it PROPERLY now (baseline-conditional split). +- **Re-architecture (terra route).** gdalcubes can't read a terra-written NetCDF + (round-trip fails), so coalescing pre/post cubes at the gdalcubes level is out. + Pivoted the reduction to terra: dft_stac_cube now returns a terra SpatRaster stack + (materialized GeoTIFF, time set), splitting items at the offset boundary and + correcting each side with its own offset (terra::cover coalesce). dft_rast_break + reduces the stack via parallel::mclapply (fork -> closures + package internals work, + no gdalcubes-worker serialization; validated 102400 px in 8.3 s). Dropped + build_break_reducer/break_cache_key/gdalcubes reduce_time; .dft_break_pixel unchanged. + Config: sentinel-2-l2a gains offset_boundary="2022-01-25" + offset_before=0. Full + suite 286 pass, lint clean. +- Next: confirm the split kills the fake 2022 step (per-year kNDVI aligns; breaks + sparse), regenerate artifact, Phase 5 (vignette + NEWS + 0.3.0 + archive + PR). diff --git a/tests/testthat/helper-break.R b/tests/testthat/helper-break.R index fa0693a..88b5d84 100644 --- a/tests/testthat/helper-break.R +++ b/tests/testthat/helper-break.R @@ -1,10 +1,9 @@ -# Build a tiny synthetic monthly index cube for dft_rast_break() tests, with no -# network. 2x2 px over 2018-01..2023-12 (72 monthly steps), band "kndvi": +# Build a tiny synthetic monthly index STACK for dft_rast_break() tests, no +# network, no gdalcubes. 3x3 px, 72 monthly layers 2018-2023, time set: # pixel 1 = seasonal series with a -0.3 step drop at 2022-06 (a real break) -# pixel 2, 3 = stable seasonal series (no break) -# pixel 4 = all NA (degenerate pixel) -# Returns list(cube, cache_dir). Only called under skip_if_not_installed guards. -synthetic_break_cube <- function() { +# pixels 2-8 = stable seasonal series (no break) +# pixel 9 = all NA (degenerate pixel) +synthetic_break_stack <- function() { n <- 72 tt <- seq_len(n) seasonal <- 0.15 * sin(2 * pi * tt / 12) @@ -12,28 +11,17 @@ synthetic_break_cube <- function() { drop[54:n] <- drop[54:n] - 0.3 stable <- 0.6 + seasonal - scratch <- tempfile("drift_break_cube_") - dir.create(scratch) - dates <- format(seq(as.Date("2018-01-01"), by = "month", length.out = n), - "%Y-%m-%d") - files <- character(n) - for (i in seq_len(n)) { - r <- terra::rast(nrows = 2, ncols = 2, xmin = 0, xmax = 20, - ymin = 0, ymax = 20, crs = "EPSG:32609") - terra::values(r) <- c(drop[i], stable[i], stable[i], NA_real_) - names(r) <- "kndvi" - files[i] <- file.path(scratch, sprintf("kndvi_%03d.tif", i)) - terra::writeRaster(r, files[i], overwrite = TRUE) - } - col <- gdalcubes::create_image_collection( - files, date_time = dates, band_names = "kndvi", quiet = TRUE - ) - v <- gdalcubes::cube_view( - srs = "EPSG:32609", - extent = list(left = 0, right = 20, bottom = 0, top = 20, - t0 = "2018-01", t1 = "2023-12"), - dx = 10, dy = 10, dt = "P1M", aggregation = "median", resampling = "near" - ) - list(cube = gdalcubes::raster_cube(col, v), - cache_dir = file.path(scratch, "cache")) + lays <- lapply(seq_len(n), function(i) { + r <- terra::rast(nrows = 3, ncols = 3, xmin = 0, xmax = 30, + ymin = 0, ymax = 30, crs = "EPSG:32609") + vv <- rep(stable[i], 9) + vv[1] <- drop[i] + vv[9] <- NA_real_ + terra::values(r) <- vv + r + }) + stk <- terra::rast(lays) + terra::time(stk) <- seq(as.Date("2018-01-01"), by = "month", length.out = n) + names(stk) <- rep("kndvi", n) + stk } diff --git a/tests/testthat/test-dft_rast_break.R b/tests/testthat/test-dft_rast_break.R index 5d073cb..85214fc 100644 --- a/tests/testthat/test-dft_rast_break.R +++ b/tests/testthat/test-dft_rast_break.R @@ -1,26 +1,33 @@ -# ---- guards (run without gdalcubes/bfast) ---------------------------------- - -test_that("dft_rast_break requires gdalcubes", { - skip_if(requireNamespace("gdalcubes", quietly = TRUE), - "gdalcubes is installed, can't test missing-package path") - expect_error(dft_rast_break(NULL), "gdalcubes") -}) +# ---- guards ---------------------------------------------------------------- test_that("dft_rast_break requires bfast", { skip_if(requireNamespace("bfast", quietly = TRUE), "bfast is installed, can't test missing-package path") - skip_if_not_installed("gdalcubes") - expect_error(dft_rast_break(NULL), "bfast") + expect_error(dft_rast_break(terra::rast(nrows = 1, ncols = 1)), "bfast") +}) + +test_that("dft_rast_break errors on non-SpatRaster input", { + skip_if_not_installed("bfast") + expect_error(dft_rast_break(list()), "SpatRaster") +}) + +test_that("dft_rast_break errors when layers lack times", { + skip_if_not_installed("bfast") + r <- terra::rast(nrows = 2, ncols = 2, nlyrs = 3) + terra::values(r) <- 1 + expect_error(dft_rast_break(r), "time") }) # ---- cadence -> frequency (pure) ------------------------------------------- -test_that("cadence_frequency maps ISO durations to seasonal frequency", { - expect_equal(drift:::cadence_frequency("P1M"), 12) - expect_equal(drift:::cadence_frequency("P3M"), 4) - expect_equal(drift:::cadence_frequency("P1Y"), 1) - expect_true(is.na(drift:::cadence_frequency("P16D"))) - expect_true(is.na(drift:::cadence_frequency("garbage"))) +test_that("cadence_frequency maps layer times to seasonal frequency", { + monthly <- seq(as.Date("2020-01-01"), by = "month", length.out = 12) + quarterly <- seq(as.Date("2020-01-01"), by = "3 months", length.out = 8) + annual <- seq(as.Date("2018-01-01"), by = "year", length.out = 5) + expect_equal(drift:::cadence_frequency(monthly), 12) + expect_equal(drift:::cadence_frequency(quarterly), 4) + expect_equal(drift:::cadence_frequency(annual), 1) + expect_true(is.na(drift:::cadence_frequency(as.Date("2020-01-01")))) # length 1 }) # ---- .dft_break_pixel degenerate paths (no bfast needed) ------------------- @@ -41,41 +48,6 @@ test_that(".dft_break_pixel returns c(NA, NA) when fewer than min_obs", { ) }) -# ---- reducer is self-contained (worker-safe) ------------------------------- - -test_that("build_break_reducer yields a callback with no free variables", { - f <- drift:::build_break_reducer("kndvi", c(2018, 1), 12, c(2022, 1), - "all", 3, 0.01, 6) - # environment detached to baseenv and all params inlined as literals, so the - # only globals referenced are base/pkg functions -> safe to serialize to a - # gdalcubes worker (see findings.md: closures fail in workers) - expect_identical(environment(f), baseenv()) - globals <- codetools::findGlobals(f, merge = FALSE)$variables - expect_false("band" %in% globals) - expect_false("ts_start" %in% globals) -}) - -# ---- break cache key ------------------------------------------------------- - -test_that("break_cache_key changes with each reducer parameter", { - skip_if_not_installed("gdalcubes") - skip_if_not_installed("bfast") - cube <- synthetic_break_cube()$cube - k <- function(history = "all", start = c(2022, 1), frequency = 12, order = 3, - level = 0.01, min_obs = 6) { - drift:::break_cache_key(cube, "kndvi", history, start, frequency, order, - level, min_obs) - } - base <- k() - expect_match(base, "^[0-9a-f]{12}$") - expect_false(k(history = "ROC") == base) - expect_false(k(start = c(2021, 1)) == base) - expect_false(k(frequency = 1) == base) - expect_false(k(order = 1) == base) - expect_false(k(level = 0.05) == base) - expect_false(k(min_obs = 8) == base) -}) - # ---- bfast-gated per-pixel behavior ---------------------------------------- test_that(".dft_break_pixel detects an injected step drop (negative magnitude)", { @@ -83,7 +55,7 @@ test_that(".dft_break_pixel detects an injected step drop (negative magnitude)", tt <- seq_len(72) v <- 0.6 + 0.15 * sin(2 * pi * tt / 12) v[54:72] <- v[54:72] - 0.3 # step drop at 2022-06 (monitoring period) - out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 3, 0.01, 6) + out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 1, 0.01, 6) expect_true(is.finite(out[1])) expect_gt(out[1], 2022) expect_lt(out[1], 2022.9) @@ -94,23 +66,20 @@ test_that(".dft_break_pixel returns NA break on a stable series (non-error)", { skip_if_not_installed("bfast") tt <- seq_len(72) v <- 0.6 + 0.15 * sin(2 * pi * tt / 12) - out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 3, 0.01, 6) + out <- drift:::.dft_break_pixel(v, c(2018, 1), 12, c(2022, 1), "all", 1, 0.01, 6) expect_true(is.na(out[1])) }) -# ---- gdalcubes + bfast integration (synthetic cube, no network) ------------ +# ---- reduction over a stack (terra + bfast, no network) -------------------- -test_that("dft_rast_break reduces a synthetic cube to a 2-band raster", { - skip_if_not_installed("gdalcubes") +test_that("dft_rast_break reduces a synthetic stack to a 2-band raster", { skip_if_not_installed("bfast") - sc <- synthetic_break_cube() - breaks <- dft_rast_break(sc$cube, start = c(2022, 1), - cache_dir = sc$cache_dir) + stk <- synthetic_break_stack() + breaks <- dft_rast_break(stk, start = c(2022, 1), order = 1, cores = 2) expect_s4_class(breaks, "SpatRaster") expect_equal(terra::nlyr(breaks), 2) expect_equal(names(breaks), c("break_date", "break_mag")) - expect_equal(terra::crs(breaks, describe = TRUE)$code, "32609") bd <- terra::values(breaks[["break_date"]])[, 1] bm <- terra::values(breaks[["break_mag"]])[, 1] @@ -119,11 +88,10 @@ test_that("dft_rast_break reduces a synthetic cube to a 2-band raster", { expect_true(any(is.finite(bd))) expect_true(any(is.na(bd))) expect_true(any(is.finite(bd) & bm < 0)) +}) - # second call hits the cache (one break_.nc) - breaks2 <- dft_rast_break(sc$cube, start = c(2022, 1), - cache_dir = sc$cache_dir) - expect_equal(terra::values(breaks2[["break_date"]])[, 1], bd) - expect_length(list.files(file.path(sc$cache_dir, "break"), - pattern = "^break_.*\\.nc$"), 1) +test_that("dft_rast_break errors when frequency disagrees with the cadence", { + skip_if_not_installed("bfast") + stk <- synthetic_break_stack() # monthly -> cadence 12 + expect_error(dft_rast_break(stk, frequency = 1), "frequency") }) diff --git a/tests/testthat/test-dft_stac_config.R b/tests/testthat/test-dft_stac_config.R index 95567f0..9904a24 100644 --- a/tests/testthat/test-dft_stac_config.R +++ b/tests/testthat/test-dft_stac_config.R @@ -47,4 +47,9 @@ test_that("dft_stac_config returns role-based cube config for sentinel-2-l2a", { expect_equal(cfg$scale, 1e-4) expect_equal(cfg$offset, -0.1) expect_match(cfg$available_datetime, "^\\d{4}-\\d{2}-\\d{2}/\\d{4}-\\d{2}-\\d{2}$") + + # baseline offset boundary: +1000 DN only from 2022-01-25, so pre-boundary + # scenes carry offset 0 and the fetch splits at the boundary + expect_equal(cfg$offset_boundary, "2022-01-25") + expect_equal(cfg$offset_before, 0) }) diff --git a/tests/testthat/test-dft_stac_cube.R b/tests/testthat/test-dft_stac_cube.R index cda9b07..2ec39b3 100644 --- a/tests/testthat/test-dft_stac_cube.R +++ b/tests/testthat/test-dft_stac_cube.R @@ -35,11 +35,12 @@ cube_key <- function(aoi = square_aoi(), res = 10, target_crs = "EPSG:32609", band_assets = c("B08", "B04"), datetime = "2019-01-01/2023-12-31", index = "kndvi", cloud_cover_max = 60, mask_values = c(3, 8, 9, 10, 11), - scale = 1e-4, offset = -0.1, months = NULL) { + scale = 1e-4, offset = -0.1, months = NULL, + offset_before = 0) { drift:::stac_cube_cache_key( aoi, res, target_crs, dt, aggregation, resampling, stac_url, collection, band_assets, datetime, index, cloud_cover_max, mask_values, scale, offset, - months + months, offset_before ) } @@ -65,6 +66,7 @@ test_that("stac_cube_cache_key changes with each cube-affecting parameter", { expect_false(cube_key(scale = 2.75e-5) == base) expect_false(cube_key(offset = -0.2) == base) expect_false(cube_key(months = 6:9) == base) + expect_false(cube_key(offset_before = -0.1) == base) }) test_that("stac_cube_cache_key normalizes months order", { @@ -85,7 +87,7 @@ test_that("stac_cube_cache_key ignores sf attribute columns", { # Network end-to-end against the Planetary Computer. Opt-in only (env var), so # the default `devtools::test()` stays network-free per the repo convention. -test_that("dft_stac_cube fetches a single-band index cube end-to-end", { +test_that("dft_stac_cube fetches an index stack end-to-end", { skip_if(Sys.getenv("DRIFT_TEST_NETWORK") != "true", "network test — set DRIFT_TEST_NETWORK=true to run") skip_if_not_installed("gdalcubes") @@ -98,9 +100,10 @@ test_that("dft_stac_cube fetches a single-band index cube end-to-end", { cube <- dft_stac_cube(aoi, index = "kndvi", datetime = "2021-06-01/2021-08-31", dt = "P1M", cache_dir = cache) - expect_s3_class(cube, "cube") - expect_equal(gdalcubes::bands(cube)$name, "kndvi") - # second call hits the cache (one cube_.nc under the source dir) + expect_s4_class(cube, "SpatRaster") + expect_equal(terra::nlyr(cube), 3) # 3 monthly layers + expect_false(anyNA(terra::time(cube))) # time set per layer + # second call hits the cache (one cube_.tif under the source dir) expect_length(list.files(file.path(cache, "sentinel-2-l2a"), - pattern = "^cube_.*\\.nc$"), 1) + pattern = "^cube_.*\\.tif$"), 1) }) From 42aae7565159236afd4dcdf66504e6ac479bcfd1 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 21:11:47 -0700 Subject: [PATCH 08/16] Note egress cost (free) on cube sources in dft_stac_config (#30) Flag that the Sentinel-2 (and Landsat template) cube sources read free Planetary Computer / Azure Open Data with no egress charge, prompting anyone adding a new source to check egress terms before shipping a requester-pays one. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- R/dft_stac_config.R | 3 +++ 1 file changed, 3 insertions(+) diff --git a/R/dft_stac_config.R b/R/dft_stac_config.R index a63f4ec..dbc110d 100644 --- a/R/dft_stac_config.R +++ b/R/dft_stac_config.R @@ -49,6 +49,8 @@ dft_stac_config <- function(source = c("io-lulc", "esa-worldcover", available_years = c(2020L, 2021L) ), "sentinel-2-l2a" = list( + # Cost: free — Microsoft Planetary Computer (Azure Open Data), no egress + # charge. Check egress terms before adding a requester-pays source. stac_url = "https://planetarycomputer.microsoft.com/api/stac/v1", collection = "sentinel-2-l2a", cube = TRUE, @@ -76,6 +78,7 @@ dft_stac_config <- function(source = c("io-lulc", "esa-worldcover", ) # "landsat-c2-l2" drops in with the same cube shape, no API change: # list( + # # Cost: free — Planetary Computer (Azure Open Data), no egress charge. # stac_url = "https://planetarycomputer.microsoft.com/api/stac/v1", # collection = "landsat-c2-l2", # cube = TRUE, From aa9b6b9f7ffde20879700c0a21c2954e340aafe7 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 21:32:04 -0700 Subject: [PATCH 09/16] Add trajectory-break vignette + committed artifact (#30) vignettes/trajectory-break-detection.Rmd demonstrates the continuous index-trajectory pipeline on the same Neexdzii Kwa reach as the land-cover vignette. Following drift's data-raw pattern, data-raw/vignette_data_break.R does the network + bfast work (growing-season Sentinel-2 kNDVI 2018-2023 with the baseline-conditional offset split, reduced to break date + magnitude) and saves a small committed artifact (inst/testdata/neexdzii_break.rds, 132 KB) so every vignette chunk runs live under pkgdown with no network or bfast. The vignette shows the break-magnitude map (negative = scour / veg loss), the break-year map, and scour-vs-stable pixel kNDVI trajectories with the detected break marked. Renders clean. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- data-raw/vignette_data_break.R | 78 ++++++++++++ inst/testdata/neexdzii_break.rds | Bin 0 -> 135371 bytes planning/active/task_plan.md | 17 +-- vignettes/trajectory-break-detection.Rmd | 149 +++++++++++++++++++++++ 4 files changed, 236 insertions(+), 8 deletions(-) create mode 100644 data-raw/vignette_data_break.R create mode 100644 inst/testdata/neexdzii_break.rds create mode 100644 vignettes/trajectory-break-detection.Rmd diff --git a/data-raw/vignette_data_break.R b/data-raw/vignette_data_break.R new file mode 100644 index 0000000..0bbe641 --- /dev/null +++ b/data-raw/vignette_data_break.R @@ -0,0 +1,78 @@ +# Generate the committed artifact for vignettes/trajectory-break-detection.Rmd. +# +# NOT part of the package build. Requires network (Microsoft Planetary +# Computer), `gdalcubes`, and `bfast`. Follows drift's data-raw pattern: do the +# network + bfast work here, save a small artifact to inst/testdata/, and let +# the vignette load that artifact so every chunk runs live under pkgdown with no +# network or bfast dependency. +# +# The Sentinel-2 fetch streams ~6 years of growing-season COGs and takes +# ~15 min; the bfast reduction is fast (~10 s). Run from the repo root after +# installing: +# Rscript data-raw/vignette_data_break.R + +library(drift) + +aoi <- sf::st_read( + system.file("extdata", "example_aoi.gpkg", package = "drift"), + quiet = TRUE +) + +cache <- file.path("data-raw", ".break_cache") + +# Growing-season (June-September) monthly kNDVI, 2018-2023. Restricting to the +# growing season drops snow / low-sun winter noise at this latitude and cuts the +# scenes streamed ~3x; the longer window keeps enough summer history (2018-2021) +# to fit a stable BFAST baseline before the 2022 monitoring start. dft_stac_cube +# splits items at the 2022-01-25 offset boundary and corrects each side, so the +# 2018-2021 history and 2022+ monitoring are on the same reflectance scale. +cube <- dft_stac_cube( + aoi, + source = "sentinel-2-l2a", + index = "kndvi", + datetime = "2018-01-01/2023-12-31", + dt = "P1M", + months = 6:9, + cloud_cover_max = 60, + cache_dir = cache +) + +# order = 1: the growing-season-only series samples a narrow part of the annual +# cycle, so a low harmonic order avoids overfitting sparse seasonal coverage +breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) + +# the cube spans the AOI bounding box; clip both to the floodplain polygon for a +# tight vignette map (see dft_stac_cube() docs) +aoi_v <- terra::vect(sf::st_transform(aoi, terra::crs(breaks))) +breaks <- terra::mask(breaks, aoi_v) +kndvi <- terra::mask(cube, aoi_v) +dates <- terra::time(kndvi) + +# Keep the artifact small: ship the 2-band break raster plus a few representative +# pixel trajectories (strongest drop + a stable pixel), not the whole stack. +bd <- terra::values(breaks[["break_date"]])[, 1] +bm <- terra::values(breaks[["break_mag"]])[, 1] +kvals <- terra::values(kndvi) +has_obs <- rowSums(!is.na(kvals)) >= 6 # pixels with a usable series +worst <- which.min(bm) # strongest index drop +stable <- which(is.na(bd) & has_obs)[1] # a pixel with data but no break +sample_cells <- c(scour = worst, stable = stable) + +traj <- do.call(rbind, lapply(names(sample_cells), function(lab) { + cell <- sample_cells[[lab]] + data.frame( + label = lab, date = dates, + kndvi = kvals[cell, ], + break_date = bd[cell] + ) +})) + +dir.create("inst/testdata", recursive = TRUE, showWarnings = FALSE) +saveRDS( + list(breaks = 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FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.width = 7, + fig.height = 5 +) +``` + +Categorical land-cover differencing (see the *Land Cover Change Detection* +vignette) compares two classified labels per pixel. For floodplain reaches that +is the weakest available signal: an annual 9-class product collapses gravel +bars, wet/dry channel, and sparse seasonal riparian into a single class, and +annual compositing smears the timing of events. + +This vignette shows the stronger alternative in drift: a **continuous index +trajectory** per pixel. Instead of comparing two labels, we build a monthly +kernel-NDVI (kNDVI) time series from Sentinel-2 and ask, per pixel, *when* the +spectral history broke and *by how much* — using +[BFAST](https://bfast2.github.io/) monitoring. + +## The pipeline + +The full pipeline is two calls. `dft_stac_cube()` builds a cloud-masked kNDVI +cube from the Planetary Computer, and `dft_rast_break()` reduces it over time +with `bfast::bfastmonitor()`: + +```{r pipeline, eval = FALSE} +library(drift) + +aoi <- sf::st_read( + system.file("extdata", "example_aoi.gpkg", package = "drift") +) + +# Growing-season (June-September) monthly kNDVI, 2018-2023 +cube <- dft_stac_cube( + aoi, + source = "sentinel-2-l2a", + index = "kndvi", + datetime = "2018-01-01/2023-12-31", + dt = "P1M", + months = 6:9 # drop snow / low-sun winter noise, fetch ~3x fewer scenes +) + +# Per-pixel breakpoint: monitoring period starts 2022, low harmonic order for +# the growing-season-only series +breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) +``` + +**Why the growing season?** At this latitude (~54 degrees N), winter Sentinel-2 +scenes are snow-covered and low-sun -- they carry no vegetation signal and would +otherwise dominate the seasonal cycle BFAST has to fit. Restricting to June to +September (`months = 6:9`) keeps the observations where a riparian scour or +vegetation loss actually shows up, and streams roughly a third of the scenes. +The winter months become `NA` in the monthly cube, so the per-pixel series stays +regular at `frequency = 12` and BFAST still models the within-season trajectory. + +That fetch requires network access and the `gdalcubes` and `bfast` packages, so +this vignette instead loads a small pre-computed artifact (generated by +`data-raw/vignette_data_break.R`) and works from it. Every chunk below runs +live, with no network. + +```{r load} +library(terra) + +d <- readRDS(system.file("testdata", "neexdzii_break.rds", package = "drift")) +breaks <- terra::unwrap(d$breaks) +traj <- d$traj # a few representative pixel trajectories for the last panel +aoi <- d$aoi + +breaks +``` + +The result is a two-band raster: `break_date` (the decimal year of the detected +break, or `NA` where none was found) and `break_mag` (the signed change in +kNDVI — negative where the index dropped). + +## Where and when did trajectories break? + +The magnitude map is the headline. Negative values (channel scour, vegetation +loss) are the floodplain disturbances we care about; positive values are +re-establishment. + +```{r mag-map, fig.cap = "kNDVI break magnitude across the Neexdzii Kwa floodplain reach. Negative (red) marks index drops -- scour or vegetation loss; positive (blue) marks establishment.", fig.height = 5} +mag <- breaks[["break_mag"]] +rng <- max(abs(terra::minmax(mag)), na.rm = TRUE) + +terra::plot( + mag, col = hcl.colors(21, "Blue-Red 3"), + range = c(-rng, rng), + main = "kNDVI break magnitude (negative = scour / loss)" +) +plot(sf::st_geometry(sf::st_transform(aoi, terra::crs(mag))), + add = TRUE, border = "grey20", lwd = 1) +``` + +The companion date map shows *when* each break occurred — useful for tying a +disturbance to a specific freshet or storm year. + +```{r date-map, fig.cap = "Decimal year of the detected kNDVI break. Pixels with no detected break are transparent.", fig.height = 5} +terra::plot(breaks[["break_date"]], col = hcl.colors(20, "Viridis"), + main = "Break year") +plot(sf::st_geometry(sf::st_transform(aoi, terra::crs(breaks))), + add = TRUE, border = "grey20", lwd = 1) +``` + +## Reading a single pixel's trajectory + +The break map is a per-pixel reduction of a full monthly time series. To see +what BFAST is responding to, the artifact ships two representative pixel +trajectories: the pixel with the strongest detected drop, and a stable pixel +where no break was found. The dashed line marks the detected break date on the +scour pixel. + +```{r trajectory, fig.cap = "Monthly kNDVI trajectories for the strongest-drop pixel (scour) and a stable pixel. The dashed line marks the detected break; the sustained drop after it is what drives the negative magnitude.", fig.height = 4} +scour <- traj[traj$label == "scour", ] +stable <- traj[traj$label == "stable", ] + +plot(scour$date, scour$kndvi, type = "o", pch = 19, cex = 0.5, col = "firebrick", + ylim = range(traj$kndvi, na.rm = TRUE), xlab = "", ylab = "kNDVI", + main = "kNDVI trajectories: scour vs stable pixel") +lines(stable$date, stable$kndvi, type = "o", pch = 19, cex = 0.5, col = "grey40") + +break_yr <- scour$break_date[1] +if (is.finite(break_yr)) { + abline(v = as.Date(paste0(floor(break_yr), "-01-01")) + + (break_yr - floor(break_yr)) * 365, lty = 2, col = "firebrick") +} +legend("bottomleft", c("scour pixel", "stable pixel"), bty = "n", + col = c("firebrick", "grey40"), pch = 19, lty = 1) +``` + +## When to use which + +Reach for `dft_rast_break()` over categorical differencing when timing and +severity matter — dating a scour event, ranking reaches by disturbance +magnitude, or catching change that annual class labels miss. Reach for the +categorical pipeline when you need named land-cover transitions (e.g. trees to +rangeland) for reporting. The two are complementary: the optional bolt-on of +labelling breaks with from/to classes bridges them. From 21e0de4a93150683c3e465989636b41e4f87d514 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 21:36:21 -0700 Subject: [PATCH 10/16] Clean up R CMD check NOTEs (#30) - .Rbuildignore: exclude planning/ and .claude/ from the built package (they belong in git for the SRED trail but are not package sources). - .gitignore: data-raw/.break_cache/ (local STAC cube cache). - Declare the rlang .data pronoun as a global (pre-existing note in dft_rast_classify's apply_remap), alongside eo:cloud_cover. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- .Rbuildignore | 2 ++ .gitignore | 3 +++ R/drift-package.R | 5 +++-- 3 files changed, 8 insertions(+), 2 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index d4909df..159d74f 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -11,3 +11,5 @@ ^doc$ ^Meta$ ^CITATION\.cff$ +^planning$ +^\.claude$ diff --git a/.gitignore b/.gitignore index fbc5e6d..e3e6e2c 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,6 @@ docs /doc/ /Meta/ vignettes/*.html + +# local STAC cube cache (generated by data-raw/vignette_data_break.R) +data-raw/.break_cache/ diff --git a/R/drift-package.R b/R/drift-package.R index 0f21a73..f7e8a6a 100644 --- a/R/drift-package.R +++ b/R/drift-package.R @@ -1,5 +1,6 @@ #' @keywords internal "_PACKAGE" -# NSE symbol used in rstac::ext_filter() CQL2 expressions (dft_stac_cube) -utils::globalVariables("eo:cloud_cover") +# NSE symbols: `eo:cloud_cover` in rstac::ext_filter() CQL2 (dft_stac_cube); +# `.data` is the rlang pronoun used in dplyr pipelines (dft_rast_classify) +utils::globalVariables(c("eo:cloud_cover", ".data")) From c145899baebff15a900747e0a0ef381570879395 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 21:37:32 -0700 Subject: [PATCH 11/16] Release v0.3.0 Continuous index-trajectory change detection (Sentinel-2 + BFAST) for floodplain reaches (#30). R CMD check: 0 errors / 0 warnings / 0 notes. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- DESCRIPTION | 4 ++-- planning/active/task_plan.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f735498..b2af226 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: drift Title: Detecting Riparian and Inland Floodplain Transitions -Version: 0.2.4 -Date: 2026-07-07 +Version: 0.3.0 +Date: 2026-07-08 Authors@R: c( person("Allan", "Irvine", , "al@newgraphenvironment.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3495-2128")), diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md index 9eac439..f2b3f8d 100644 --- a/planning/active/task_plan.md +++ b/planning/active/task_plan.md @@ -41,7 +41,7 @@ network-decoupled vignette. **Whole pipeline on one branch, single 0.3.0 release - [x] `devtools::document()`; `lintr::lint_package()` clean; full `devtools::test()` passes (286 pass, network/bfast-gated tests skip) - [x] File follow-up issues: #31 (bolt-on labels), #32 (AOI-polygon clip). The S2 offset was NOT deferred — implemented via the baseline-conditional split (offset half of #32 closed) - [x] NEWS.md `0.3.0` section -- [ ] Bump DESCRIPTION `0.2.4 → 0.3.0` as the **final** commit +- [x] Bump DESCRIPTION `0.2.4 → 0.3.0` as the **final** commit. `R CMD check`: 0 errors / 0 warnings / 0 notes ## Validation - [x] Tests pass (`devtools::test()`), network/bfast tests skip cleanly (286 pass) From b3748edf64ee61f25acf55ce487cfa3b73e0fd42 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Wed, 8 Jul 2026 21:38:08 -0700 Subject: [PATCH 12/16] Archive planning files for issue #30 Continuous index-trajectory change detection shipped as v0.3.0. See the archive README for the outcome and the three empirical findings that reshaped the plan (worker-closure failure, filter_geom segfault, Sentinel-2 offset boundary). Co-Authored-By: Claude Opus 4.8 --- .../.gitkeep | 0 .../README.md | 45 +++++++++++++++++++ .../findings.md | 0 .../progress.md | 0 .../task_plan.md | 0 5 files changed, 45 insertions(+) create mode 100644 planning/archive/2026-07-issue-30-index-trajectory/.gitkeep create mode 100644 planning/archive/2026-07-issue-30-index-trajectory/README.md rename planning/{active => archive/2026-07-issue-30-index-trajectory}/findings.md (100%) rename planning/{active => archive/2026-07-issue-30-index-trajectory}/progress.md (100%) rename planning/{active => archive/2026-07-issue-30-index-trajectory}/task_plan.md (100%) diff --git a/planning/archive/2026-07-issue-30-index-trajectory/.gitkeep b/planning/archive/2026-07-issue-30-index-trajectory/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/planning/archive/2026-07-issue-30-index-trajectory/README.md b/planning/archive/2026-07-issue-30-index-trajectory/README.md new file mode 100644 index 0000000..eb1e8fc --- /dev/null +++ b/planning/archive/2026-07-issue-30-index-trajectory/README.md @@ -0,0 +1,45 @@ +# Issue #30 — Continuous index-trajectory change detection (Sentinel-2 + BFAST) + +## Outcome + +Added a continuous change-detection pipeline alongside the categorical one, released as **v0.3.0** +(`R CMD check`: 0 errors / 0 warnings / 0 notes). Four new exports: `dft_stac_cube()` (fetch a +cloud-masked monthly spectral-index stack from Sentinel-2 via a new role-based `sentinel-2-l2a` +config source), `dft_index_expr()` / `dft_index_table()` (a table-driven index registry — NDVI, +kNDVI, NDMI — written over band roles), and `dft_rast_break()` (per-pixel `bfast::bfastmonitor()` +reduction to a two-band break-date / break-magnitude raster). A network-decoupled vignette +(`trajectory-break-detection.Rmd`) demonstrates it on the same Neexdzii Kwa reach as the +land-cover vignette, loading a small committed artifact (132 KB) generated by +`data-raw/vignette_data_break.R`. + +## What the empirical work changed vs. the original plan + +The plan was sound but three things could only be settled by running real code, and each changed +the implementation materially: + +1. **The reduce_time R-callback runs in spawned gdalcubes workers at every parallel setting**, and a + closure over enclosing locals fails there (`object 'band' not found`). First fixed with a + self-contained embed-the-helper callback; ultimately mooted by the terra pivot below. +2. **`gdalcubes::filter_geom()` segfaults / yields an all-NA cube** on the pinned build. Dropped it; + the cube spans the AOI bbox and callers clip the reduced raster (matches `dft_stac_fetch`). The + AOI-polygon clip is deferred to #32. +3. **The Planetary Computer +1000 DN reflectance offset only applies from 2022-01-25** (baseline + 04.00); a uniform offset produced a *false whole-AOI break* at the boundary (99% of pixels broke + at 2022.42 — an artifact `tanh`-kNDVI hid). Fixed with a baseline-conditional item split + (`offset_boundary` / `offset_before` in config), coalesced with `terra::cover`. Validated: per-year + kNDVI now aligns across 2022 and breaks fell to 25% of pixels (real change). A peer validation + session independently confirmed the offset finding. + +**Architecture pivot:** because gdalcubes cannot read a terra-written NetCDF, the coalesce had to +live in terra — so `dft_stac_cube()` now returns a **terra SpatRaster stack** and `dft_rast_break()` +reduces it via forked `parallel::mclapply` (simpler and faster — 102k px in 8.3s — than the +gdalcubes-worker path, which was removed). User steered two decisions: growing-season focus +(`months` filter + snow mask + `order` knob; better signal, ~3× less data) and fixing the offset +properly now rather than deferring. + +Follow-ups filed: **#31** (label breaks with from/to land-cover classes, depends on #19), **#32** +(restore AOI-polygon clip once the gdalcubes `filter_geom` issue is resolved). The offset half of the +original #32 was closed here. + +Closed by: branch `30-continuous-index-trajectory-change-detec` (11 commits, `6da759c`..`c145899`); +PR pending. diff --git a/planning/active/findings.md b/planning/archive/2026-07-issue-30-index-trajectory/findings.md similarity index 100% rename from planning/active/findings.md rename to planning/archive/2026-07-issue-30-index-trajectory/findings.md diff --git a/planning/active/progress.md b/planning/archive/2026-07-issue-30-index-trajectory/progress.md similarity index 100% rename from planning/active/progress.md rename to planning/archive/2026-07-issue-30-index-trajectory/progress.md diff --git a/planning/active/task_plan.md b/planning/archive/2026-07-issue-30-index-trajectory/task_plan.md similarity index 100% rename from planning/active/task_plan.md rename to planning/archive/2026-07-issue-30-index-trajectory/task_plan.md From c918491577d6cc2d692191e9f013ef4e482b4e3f Mon Sep 17 00:00:00 2001 From: almac2022 Date: Thu, 9 Jul 2026 06:11:58 -0700 Subject: [PATCH 13/16] Reopen PWF for trajectory vignette redo (#30, continued) Continuation of the archived #30 cycle (v0.3.0 / PR #33). Refines the trajectory vignette example to 2017-2023 (apples-to-apples with the LULC vignette) and to surface the known 2022-2023 logging cut, which the shipped 2018-2023 / monitor-from-2022 example did not (region-wide 2023 dip dominated). PWF headers carry the prior journey forward and reference the archive. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- planning/active/findings.md | 35 +++++++++++++++++++++++++++++++++ planning/active/progress.md | 18 +++++++++++++++++ planning/active/task_plan.md | 38 ++++++++++++++++++++++++++++++++++++ 3 files changed, 91 insertions(+) create mode 100644 planning/active/findings.md create mode 100644 planning/active/progress.md create mode 100644 planning/active/task_plan.md diff --git a/planning/active/findings.md b/planning/active/findings.md new file mode 100644 index 0000000..85c7c85 --- /dev/null +++ b/planning/active/findings.md @@ -0,0 +1,35 @@ +# Findings — Trajectory vignette redo (#30, continued) + +**Continues** `planning/archive/2026-07-issue-30-index-trajectory/findings.md` — read that for the +full empirical journey. Condensed carry-forward below, then this cycle's findings. + +## Journey so far (carried from the archived #30 cycle) + +- **`gdalcubes::filter_geom()` segfaults / all-NA cube** (0.7.3) → cube spans the AOI bbox; clip the + reduced raster with `terra::mask()`. (drift#32) +- **`reduce_time` R-callback runs in spawned workers** where closures fail → moot after the terra pivot. +- **gdalcubes can't read a terra-written NetCDF** → `dft_stac_cube()` returns a terra SpatRaster stack; + `dft_rast_break()` reduces it via forked `parallel::mclapply` (fast: 102k px ~8 s). +- **Sentinel-2 +1000 DN offset flips at 2022-01-25** (baseline 04.00). A uniform offset makes a false + whole-AOI break at 2022 (kNDVI `tanh` hides it; 99% of pixels "broke" at 2022.42). Fixed by a + **baseline-conditional item split** (`offset_boundary`/`offset_before`), coalesced with `terra::cover`. + Validated: per-year kNDVI aligns across 2022; breaks fell to 25% of pixels. +- **Growing-season focus** (`months = 6:9` + snow mask + `order` knob) sharpens signal and cuts data ~3×. +- Shipped v0.3.0, `R CMD check` 0/0/0, PR #33 open. Follow-ups #31 (label breaks), #32 (AOI-polygon clip). + +## Why the redo (review of the shipped vignette) + +- The 2018–2023 / monitor-from-2022 example is dominated by a **region-wide 2023 kNDVI decline** + (~96% of breaks negative, most dated 2023.4–2023.7; per-year mean 2022≈0.53 → 2023≈0.43). Most + plausibly a **2023 dry/wildfire-smoke year** — smoke depresses kNDVI and is NOT an SCL class, so it + leaks through the cloud mask. This swamps localized change. +- The reach is **mostly rangeland with a riparian tree corridor** (from the LULC vignette), so the + broad dip is largely rangeland; **logging = the subset where the green tree corridor drops sharply**. +- User ground truth: the LULC vignette shows **Trees → Rangeland** transitions; a cut is visible at the + **north confluence** changing 2020→2023; **some cuts are 2022–2023** (inside our monitoring window). +- Key method note carried into this cycle: `bfastmonitor` treats everything before `start` as stable + history, so a pre-`start` cut is invisible; 2022–2023 cuts with `start = 2022` ARE catchable. + +## This cycle's findings + +(to be appended as the redo runs — cube stats, break-vs-LULC agreement, cut date, reducer choice) diff --git a/planning/active/progress.md b/planning/active/progress.md new file mode 100644 index 0000000..309e85f --- /dev/null +++ b/planning/active/progress.md @@ -0,0 +1,18 @@ +# Progress — Trajectory vignette redo (#30, continued) + +**Continues** `planning/archive/2026-07-issue-30-index-trajectory/progress.md`. The #30 pipeline was +built, validated, and shipped as v0.3.0 (PR #33 open) in the prior cycle — see that archive for the +full log. This cycle refines the trajectory *vignette example* on the same branch / same PR. + +## Session 2026-07-09 + +- Reopened PWF as an explicit continuation of the archived #30 cycle (see task_plan.md / findings.md + headers). Not a new branch or issue — commits land on PR #33. +- Trigger: user review of the shipped vignette. It used 2018–2023 / monitor-from-2022 and the result + was a region-wide 2023 dip (dry/smoke year), not the localized change; the intended scour-vs-stable + contrast was muddy and a known 2022–2023 logging cut didn't clearly show. +- Plan approved: redo the example at **2017–2023** (apples-to-apples with the LULC vignette), validate + the bfast break map against the LULC Trees→Rangeland transition at the north-confluence cut, and + reframe the vignette as LULC (what / roughly when) vs trajectory (exactly when). +- Next: Phase 1 — re-fetch the 2017–2023 growing-season cube (offset split; ~20 min one-time, + then cached). diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md new file mode 100644 index 0000000..86a64c1 --- /dev/null +++ b/planning/active/task_plan.md @@ -0,0 +1,38 @@ +# Task: Trajectory vignette redo — 2017–2023, LULC-comparable (#30, continued) + +**Continuation of** `planning/archive/2026-07-issue-30-index-trajectory/` (v0.3.0 released, +PR #33 open). That cycle built and shipped the pipeline (`dft_stac_cube` / `dft_index_expr` / +`dft_rast_break`) and a first trajectory vignette. On review the vignette example was weak: it +used a 2018–2023 / monitor-from-2022 window, and the result was dominated by a region-wide 2023 +kNDVI dip (dry/smoke year) rather than the localized change we care about — the intended +"scour vs stable" contrast was muddy and a known **logging cut (2022–2023)** did not clearly show. + +**This cycle** refines the *example* (not the pipeline) so the trajectory vignette is apples-to-apples +with the existing LULC vignette (same Neexdzii Kwa reach, **2017–2023**) and actually surfaces the +logging. Commits land on the **same branch / PR #33**. + +## Phase 1: Re-fetch 2017–2023 growing-season cube +- [ ] `dft_stac_cube(datetime = "2017-01-01/2023-12-31", months = 6:9)` — matches the LULC vignette window. Offset split fires (2017–2021 pre / 2022–2023 post). Confirm valid kNDVI, note 2017 S2 sparsity. Cache it (one-time ~20 min fetch; iterate freely after) + +## Phase 2: Reduce + reducer decision +- [ ] `dft_rast_break(start = c(2022, 1))` with 2017–2021 as history (longer, more robust baseline than the 2018 start). Cuts are 2022–2023 → inside the monitoring window, so `bfastmonitor` suffices +- [ ] Decide `bfastmonitor` (keep) vs full `bfast()` (catches breaks anywhere 2017–2023). Default: keep `bfastmonitor` unless the LULC check shows pre-2022 change we need to date + +## Phase 3: Validate against LULC ground truth +- [ ] Cross-check the bfast break map against the LULC **Trees → Rangeland** transition, focused on the north-confluence clearing the user flagged (visible 2020→2023 in the LULC vignette). Confirm negative `break_mag` + `break_date` in 2022–2023 land on the cut. Isolate the tree-corridor drop from the broad rangeland dip +- [ ] If it lands: quantify (break date at the cut vs LULC's "sometime 2020–2023") + +## Phase 4: Rebuild the vignette honestly +- [ ] `data-raw/vignette_data_break.R`: 2017–2023 window; save a genuinely divergent pixel pair (cut vs intact) for the trajectory panel if one exists +- [ ] `vignettes/trajectory-break-detection.Rmd`: reframe as the complement to LULC — **LULC says *what* and roughly *when* (annual snapshots); trajectory says *exactly when* (monthly)**. Be honest that a regional 2023 dip is present; highlight the dated cut +- [ ] Regenerate the committed artifact (`inst/testdata/neexdzii_break.rds`) + +## Phase 5: Verify + land on PR #33 +- [ ] Re-render vignette clean; `devtools::test()`; `R CMD check` 0/0/0; `lintr` clean +- [ ] `/code-check` on the diff; atomic commits on branch `30-continuous-index-trajectory-change-detec` +- [ ] `/planning-archive` (append to / supersede the existing #30 archive) + +## Validation +- [ ] Tests pass; check clean +- [ ] PWF checkboxes match landed work +- [ ] Commits on PR #33, not a new branch From 5437dcf5c54f8f77b69b57d01b8d3432e8d8a557 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Thu, 9 Jul 2026 06:59:31 -0700 Subject: [PATCH 14/16] Rework trajectory vignette: 2017-2023, LULC-comparable (#30) Redo of the trajectory vignette example so it is apples-to-apples with the land-cover vignette (same Neexdzii Kwa reach, same 2017-2023 window) and actually surfaces the logging the shipped 2018-start / monitor-from-2022 example buried under a region-wide 2023 dip. - data-raw/vignette_data_break.R: fetch 2017-2023 growing-season kNDVI (offset split), monitor from 2022 with 2017-2021 history (longer baseline -> 13k breaks vs 25k), and precompute a LULC ground-truth grouping (Trees -> Rangeland/Bare vs intact forest vs background) + agreement stats into a 140 KB artifact. - vignette reframed as the complement to LULC: LULC says what and roughly when (annual snapshots), the trajectory says exactly when (monthly). Shows the break map with LULC tree-loss outlined, an agreement table (tree-loss stands break 2x as often, 2.7x stronger, dated summer 2023), and the grouped trajectory -- with the basin-wide 2023 dip honestly acknowledged. Vignette re-builds clean under R CMD check; 286 tests pass. Lands on PR #33. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- data-raw/vignette_data_break.R | 100 ++++++++------ inst/testdata/neexdzii_break.rds | Bin 135371 -> 142987 bytes planning/active/findings.md | 27 +++- planning/active/task_plan.md | 20 +-- vignettes/trajectory-break-detection.Rmd | 162 +++++++++++------------ 5 files changed, 169 insertions(+), 140 deletions(-) diff --git a/data-raw/vignette_data_break.R b/data-raw/vignette_data_break.R index 0bbe641..b0ba531 100644 --- a/data-raw/vignette_data_break.R +++ b/data-raw/vignette_data_break.R @@ -6,9 +6,11 @@ # the vignette load that artifact so every chunk runs live under pkgdown with no # network or bfast dependency. # -# The Sentinel-2 fetch streams ~6 years of growing-season COGs and takes -# ~15 min; the bfast reduction is fast (~10 s). Run from the repo root after -# installing: +# The window (2017-2023) matches the land-cover vignette so the two are +# comparable, and the artifact carries a LULC "tree-loss" grouping so the +# vignette can show the trajectory method agreeing with (and dating) the +# categorical change. The Sentinel-2 fetch is ~10-15 min (once; cached after); +# the bfast reduction is seconds. Run from the repo root after installing: # Rscript data-raw/vignette_data_break.R library(drift) @@ -17,62 +19,72 @@ aoi <- sf::st_read( system.file("extdata", "example_aoi.gpkg", package = "drift"), quiet = TRUE ) - cache <- file.path("data-raw", ".break_cache") -# Growing-season (June-September) monthly kNDVI, 2018-2023. Restricting to the -# growing season drops snow / low-sun winter noise at this latitude and cuts the -# scenes streamed ~3x; the longer window keeps enough summer history (2018-2021) -# to fit a stable BFAST baseline before the 2022 monitoring start. dft_stac_cube -# splits items at the 2022-01-25 offset boundary and corrects each side, so the -# 2018-2021 history and 2022+ monitoring are on the same reflectance scale. +# Growing-season (June-September) monthly kNDVI, 2017-2023 -- same reach and +# window as the land-cover vignette. dft_stac_cube splits items at the 2022-01-25 +# offset boundary so the 2017-2021 history and 2022+ monitoring share one scale. cube <- dft_stac_cube( - aoi, - source = "sentinel-2-l2a", - index = "kndvi", - datetime = "2018-01-01/2023-12-31", - dt = "P1M", - months = 6:9, - cloud_cover_max = 60, - cache_dir = cache + aoi, source = "sentinel-2-l2a", index = "kndvi", + datetime = "2017-01-01/2023-12-31", dt = "P1M", months = 6:9, + cloud_cover_max = 60, cache_dir = cache ) -# order = 1: the growing-season-only series samples a narrow part of the annual -# cycle, so a low harmonic order avoids overfitting sparse seasonal coverage +# order = 1 for the narrow growing-season cycle; monitor from 2022 with 2017-2021 +# as the stable history (long baseline -> fewer false breaks). breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) -# the cube spans the AOI bounding box; clip both to the floodplain polygon for a -# tight vignette map (see dft_stac_cube() docs) -aoi_v <- terra::vect(sf::st_transform(aoi, terra::crs(breaks))) -breaks <- terra::mask(breaks, aoi_v) -kndvi <- terra::mask(cube, aoi_v) -dates <- terra::time(kndvi) +# ---- LULC ground truth (shipped IO LULC rasters; codes 2=Trees, 8=Bare, 11=Rangeland) +l17 <- terra::rast(system.file("extdata", "example_2017.tif", package = "drift")) +l20 <- terra::rast(system.file("extdata", "example_2020.tif", package = "drift")) +l23 <- terra::rast(system.file("extdata", "example_2023.tif", package = "drift")) +treeloss <- ((l17 == 2) | (l20 == 2)) & (l23 == 8 | l23 == 11) # Trees -> Rangeland/Bare +intact <- (l20 == 2) & (l23 == 2) # Trees -> Trees +tl <- terra::project(treeloss, breaks, method = "near") +it <- terra::project(intact, breaks, method = "near") -# Keep the artifact small: ship the 2-band break raster plus a few representative -# pixel trajectories (strongest drop + a stable pixel), not the whole stack. bd <- terra::values(breaks[["break_date"]])[, 1] bm <- terra::values(breaks[["break_mag"]])[, 1] -kvals <- terra::values(kndvi) -has_obs <- rowSums(!is.na(kvals)) >= 6 # pixels with a usable series -worst <- which.min(bm) # strongest index drop -stable <- which(is.na(bd) & has_obs)[1] # a pixel with data but no break -sample_cells <- c(scour = worst, stable = stable) +tlv <- terra::values(tl)[, 1] +itv <- terra::values(it)[, 1] +is_tl <- !is.na(tlv) & tlv == 1 +is_it <- !is.na(itv) & itv == 1 +is_bg <- is.finite(bm) & !is_tl & !is_it + +# summary stats stated in the vignette +stats <- list( + n_treeloss = sum(is_tl), + rate_treeloss = mean(is.finite(bd[is_tl])), + rate_background = mean(is.finite(bd[is_bg])), + mag_treeloss = stats::median(bm[is_tl], na.rm = TRUE), + mag_background = stats::median(bm[is_bg], na.rm = TRUE), + date_treeloss = stats::median(bd[is_tl & is.finite(bd)], na.rm = TRUE) +) + +# grouped mean-kNDVI trajectories (tree-loss / intact forest / background) +kv <- terra::values(cube) +dates <- terra::time(cube) +grp_traj <- function(sel, lab) { + data.frame(date = dates, kndvi = colMeans(kv[sel, , drop = FALSE], na.rm = TRUE), + group = lab) +} +traj <- rbind(grp_traj(is_tl, "tree-loss (LULC)"), + grp_traj(is_it, "intact forest"), + grp_traj(is_bg, "background")) -traj <- do.call(rbind, lapply(names(sample_cells), function(lab) { - cell <- sample_cells[[lab]] - data.frame( - label = lab, date = dates, - kndvi = kvals[cell, ], - break_date = bd[cell] - ) -})) +# clip the shipped rasters to the AOI polygon for tight vignette maps +aoi_v <- terra::vect(sf::st_transform(aoi, terra::crs(breaks))) +breaks_c <- terra::mask(breaks, aoi_v) +tl_c <- terra::mask(tl, aoi_v) dir.create("inst/testdata", recursive = TRUE, showWarnings = FALSE) saveRDS( - list(breaks = terra::wrap(breaks), traj = traj, aoi = aoi), + list(breaks = terra::wrap(breaks_c), treeloss = terra::wrap(tl_c), + traj = traj, stats = stats, aoi = aoi), "inst/testdata/neexdzii_break.rds" ) message("Wrote inst/testdata/neexdzii_break.rds") -message(" break layers: ", paste(names(breaks), collapse = ", ")) -message(" AOI pixels with a break: ", sum(is.finite(bd))) +message(sprintf(" tree-loss break rate %.0f%% vs background %.0f%%; 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-break, or `NA` where none was found) and `break_mag` (the signed change in -kNDVI — negative where the index dropped). - -## Where and when did trajectories break? +## When and how much did trajectories break? -The magnitude map is the headline. Negative values (channel scour, vegetation -loss) are the floodplain disturbances we care about; positive values are -re-establishment. +`break_mag` is the headline: negative where kNDVI dropped (vegetation loss), +positive where it rose. `break_date` is the decimal year of the detected break. -```{r mag-map, fig.cap = "kNDVI break magnitude across the Neexdzii Kwa floodplain reach. Negative (red) marks index drops -- scour or vegetation loss; positive (blue) marks establishment.", fig.height = 5} +```{r mag-map, fig.cap = "kNDVI break magnitude across the reach (negative = index drop). Black outlines are stands the land-cover product maps as Trees to Rangeland/Bare over 2017-2023 -- the tree loss falls preferentially on negative breaks.", fig.height = 5} mag <- breaks[["break_mag"]] -rng <- max(abs(terra::minmax(mag)), na.rm = TRUE) - -terra::plot( - mag, col = hcl.colors(21, "Blue-Red 3"), - range = c(-rng, rng), - main = "kNDVI break magnitude (negative = scour / loss)" -) -plot(sf::st_geometry(sf::st_transform(aoi, terra::crs(mag))), - add = TRUE, border = "grey20", lwd = 1) +terra::plot(mag, col = hcl.colors(21, "Blue-Red 3"), range = c(-0.45, 0.45), + main = "kNDVI break magnitude") +terra::plot(terra::as.polygons(terra::ifel(treeloss == 1, 1, NA)), + add = TRUE, border = "black", lwd = 1) ``` -The companion date map shows *when* each break occurred — useful for tying a -disturbance to a specific freshet or storm year. - -```{r date-map, fig.cap = "Decimal year of the detected kNDVI break. Pixels with no detected break are transparent.", fig.height = 5} +```{r date-map, fig.cap = "Decimal year of the detected break. Most breaks -- and most of the tree loss -- date to the 2023 growing season.", fig.height = 5} terra::plot(breaks[["break_date"]], col = hcl.colors(20, "Viridis"), main = "Break year") -plot(sf::st_geometry(sf::st_transform(aoi, terra::crs(breaks))), - add = TRUE, border = "grey20", lwd = 1) ``` -## Reading a single pixel's trajectory - -The break map is a per-pixel reduction of a full monthly time series. To see -what BFAST is responding to, the artifact ships two representative pixel -trajectories: the pixel with the strongest detected drop, and a stable pixel -where no break was found. The dashed line marks the detected break date on the -scour pixel. +## Does it agree with the land-cover product? -```{r trajectory, fig.cap = "Monthly kNDVI trajectories for the strongest-drop pixel (scour) and a stable pixel. The dashed line marks the detected break; the sustained drop after it is what drives the negative magnitude.", fig.height = 4} -scour <- traj[traj$label == "scour", ] -stable <- traj[traj$label == "stable", ] +We can check the continuous result against the categorical one directly: take the +stands the land-cover product maps as **Trees to Rangeland/Bare** (2020 to 2023) +and ask what BFAST did there. -plot(scour$date, scour$kndvi, type = "o", pch = 19, cex = 0.5, col = "firebrick", - ylim = range(traj$kndvi, na.rm = TRUE), xlab = "", ylab = "kNDVI", - main = "kNDVI trajectories: scour vs stable pixel") -lines(stable$date, stable$kndvi, type = "o", pch = 19, cex = 0.5, col = "grey40") +```{r agreement} +data.frame( + metric = c("break rate", "median break magnitude", "median break date"), + tree_loss = c(sprintf("%.0f%%", 100 * st$rate_treeloss), + sprintf("%.3f", st$mag_treeloss), + sprintf("%.1f", st$date_treeloss)), + background = c(sprintf("%.0f%%", 100 * st$rate_background), + sprintf("%.3f", st$mag_background), "--") +) +``` -break_yr <- scour$break_date[1] -if (is.finite(break_yr)) { - abline(v = as.Date(paste0(floor(break_yr), "-01-01")) + - (break_yr - floor(break_yr)) * 365, lty = 2, col = "firebrick") +The mapped tree-loss stands break about **twice as often** and with a +**stronger** negative magnitude than the background, and BFAST dates that loss to +the **2023 growing season** — timing the annual product structurally cannot +provide. The grouped trajectories show it: tree-loss pixels (red) drop below both +the intact forest (green) and the reach as a whole in 2023. + +```{r trajectory, fig.cap = "Mean growing-season kNDVI for LULC tree-loss stands, intact forest, and the reach background. Intact forest stays high; tree-loss stands diverge downward in 2023. A basin-wide dip that year (a dry / wildfire-smoke season) sits under everything -- the categorical product cannot separate that from stand-level loss, but the trajectory shows both.", fig.height = 4.2} +g <- split(traj, traj$group) +cols <- c("tree-loss (LULC)" = "firebrick", "intact forest" = "forestgreen", + "background" = "grey55") +plot(g[[1]]$date, g[[1]]$kndvi, type = "n", ylim = c(0, 0.75), + xlab = "", ylab = "kNDVI", main = "Growing-season kNDVI by group") +for (nm in names(cols)) { + lines(g[[nm]]$date, g[[nm]]$kndvi, type = "o", pch = 19, cex = 0.5, col = cols[nm]) } -legend("bottomleft", c("scour pixel", "stable pixel"), bty = "n", - col = c("firebrick", "grey40"), pch = 19, lty = 1) +abline(v = as.Date("2022-01-01"), lty = 3) +legend("bottomleft", names(cols), col = cols, pch = 19, lty = 1, bty = "n") ``` ## When to use which -Reach for `dft_rast_break()` over categorical differencing when timing and -severity matter — dating a scour event, ranking reaches by disturbance -magnitude, or catching change that annual class labels miss. Reach for the -categorical pipeline when you need named land-cover transitions (e.g. trees to -rangeland) for reporting. The two are complementary: the optional bolt-on of -labelling breaks with from/to classes bridges them. +The two pipelines are complements, not competitors. Reach for the **categorical** +path (`dft_stac_fetch()` + `dft_rast_transition()`) when you need named +land-cover transitions for reporting — "Trees to Rangeland" is legible and maps +cleanly. Reach for the **trajectory** path here when timing and severity matter: +it dates a disturbance to the season, ranks stands by magnitude, and — as the +2023 dip shows — surfaces gradual, sub-annual signals the annual labels miss. +Read together, land cover says *what*, and the trajectory says *exactly when*. From b5dcbf3a6678a965b5aca4726954a9ac2dde34bc Mon Sep 17 00:00:00 2001 From: almac2022 Date: Thu, 9 Jul 2026 07:48:50 -0700 Subject: [PATCH 15/16] Add dft_rast_trend() + reframe vignette as a QA check on land cover (#30) Reconciling why the bfast trajectory disagreed with LULC's dramatic Trees->Rangeland *inside* the floodplain revealed the tool's real value for the project goal (floodplain veg change to justify leave-standing / restoration): the LULC "tree-loss" pixels were borderline (kNDVI baseline 0.43 vs 0.50 intact, median drop 0.037, only 2% a real crash). On this deciduous-riparian reach the categorical product overstates forest loss (label flips, not canopy removal), and the continuous trajectory's job is to QA that and to catch gradual degradation/recovery the annual labels cannot. - dft_rast_trend(): new export. Per-pixel robust Theil-Sen slope (index/yr) + Mann-Kendall significance over the full stack, via forked mclapply like dft_rast_break. Resistant to a single anomalous season. Guards for <2 layers / min_obs<2. Code-check verified the statistics. - Vignette rebuilt as "Trajectories as a Check on Land-Cover Change": a degradation/recovery trend map (floodplain is stable-to-greening), a QA table showing the mapped tree-loss carries a median trend of ~0 and only ~6% significant decline (a caution on the area numbers), the grouped trajectory, and an honest "when to use which" (strong on evergreen/abrupt/gradual, weak at deciduous-riparian-vs-grass). Fixed an inverted map palette in self-review. 302 tests pass; R CMD check clean (bar a spurious future-timestamp NOTE); lint clean. Lands on PR #33. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- NAMESPACE | 1 + NEWS.md | 2 +- R/dft_rast_trend.R | 102 ++++++++++++++ data-raw/vignette_data_break.R | 81 +++++------ inst/testdata/neexdzii_break.rds | Bin 142987 -> 279686 bytes man/dft_rast_trend.Rd | 48 +++++++ planning/active/findings.md | 22 +++ planning/active/task_plan.md | 23 ++- tests/testthat/test-dft_rast_trend.R | 66 +++++++++ vignettes/trajectory-break-detection.Rmd | 170 ++++++++++++----------- 10 files changed, 392 insertions(+), 123 deletions(-) create mode 100644 R/dft_rast_trend.R create mode 100644 man/dft_rast_trend.Rd create mode 100644 tests/testthat/test-dft_rast_trend.R diff --git a/NAMESPACE b/NAMESPACE index 5e0b03d..a9fa599 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -12,6 +12,7 @@ export(dft_rast_classify) export(dft_rast_consensus) export(dft_rast_summarize) export(dft_rast_transition) +export(dft_rast_trend) export(dft_stac_classes) export(dft_stac_config) export(dft_stac_cube) diff --git a/NEWS.md b/NEWS.md index 1760fe1..a834e27 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,6 @@ # drift 0.3.0 -- Continuous index-trajectory change detection for floodplain reaches (#30). A new fetch-and-reduce pipeline complements the categorical `dft_stac_fetch()` path. `dft_stac_cube()` builds a cloud-masked monthly spectral-index stack from Sentinel-2 (via a new `"sentinel-2-l2a"` source), and `dft_rast_break()` reduces it per pixel with `bfast::bfastmonitor()` into a two-band raster of break date (decimal year) and magnitude (negative = index drop, e.g. scour or vegetation loss). Where categorical differencing compares two land-cover labels, this detects *when* a pixel's spectral history breaks and by how much. See the "Continuous Index-Trajectory Change Detection" vignette. +- Continuous index-trajectory change detection for floodplain reaches (#30). A new fetch-and-reduce pipeline complements the categorical `dft_stac_fetch()` path. `dft_stac_cube()` builds a cloud-masked monthly spectral-index stack from Sentinel-2 (via a new `"sentinel-2-l2a"` source); `dft_rast_break()` reduces it per pixel with `bfast::bfastmonitor()` into a two-band raster of *abrupt* break date and magnitude; and `dft_rast_trend()` reduces it to a per-pixel *gradual* trend — a robust Theil-Sen slope (index change per year) with Mann-Kendall significance — for degradation/recovery monitoring the annual labels cannot show. Together they let a continuous trajectory validate categorical land-cover transitions (confirming which mapped losses carry a real spectral decline) and detect gradual change. See the "Trajectories as a Check on Land-Cover Change" vignette. - `dft_index_expr()` and `dft_index_table()` add a table-driven spectral-index registry (NDVI, kNDVI, NDMI) whose formulas are written over band *roles*, so one index resolves against any reflectance source; the reflectance scale/offset is folded into each expression. - Sentinel-2 handling is correctness-focused: `dft_stac_cube()` masks cloud/shadow/cirrus/snow, restricts to caller-chosen calendar `months` (e.g. the growing season) to sharpen the signal and cut scenes streamed, and — because the +1000 DN reflectance offset only applies from processing baseline 04.00 (2022-01-25) — splits items at that boundary and corrects each side, so a multi-year series carries no artificial index step at 2022. - `dft_stac_config()` gains a role-based schema for reflectance cube sources (band roles, mask classes, scale/offset, offset boundary), leaving the categorical `io-lulc`/`esa-worldcover` sources unchanged. `bfast` added to Suggests. diff --git a/R/dft_rast_trend.R b/R/dft_rast_trend.R new file mode 100644 index 0000000..64c6574 --- /dev/null +++ b/R/dft_rast_trend.R @@ -0,0 +1,102 @@ +#' Per-pixel index trend over time +#' +#' Reduce an index stack (from [dft_stac_cube()]) to a per-pixel **trend** — the +#' rate at which the index rises or falls across the whole record. Where +#' [dft_rast_break()] finds an abrupt structural break, this measures the slow, +#' monotonic direction: gradual degradation (a declining stand) or recovery (a +#' restoration planting greening up) that annual categorical labels cannot show. +#' +#' The slope is a **Theil-Sen** estimate — the median of all pairwise slopes — +#' which is resistant to a single anomalous season (e.g. a smoke/drought year), +#' unlike an ordinary least-squares fit. Significance is the non-parametric +#' **Mann-Kendall** test for a monotonic trend. +#' +#' @param cube A monthly index `SpatRaster` (the return value of +#' [dft_stac_cube()]): one layer per time step, with a time value per layer. +#' @param min_obs Integer. Minimum non-`NA` observations required; pixels with +#' fewer return `NA` (default 6). +#' @param cores Integer or `NULL`. Forked workers for the per-pixel reduction. +#' When `NULL`, uses one fewer than the detected cores. +#' +#' @return A two-band [terra::SpatRaster] with layers `trend` (index change per +#' year — negative = declining greenness, positive = recovering) and `trend_p` +#' (Mann-Kendall two-sided p-value; small = a significant monotonic trend). +#' +#' @seealso [dft_rast_break()] (abrupt breaks), [dft_stac_cube()]. +#' +#' @examples +#' \dontrun{ +#' aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +#' cube <- dft_stac_cube(aoi, index = "kndvi", datetime = "2017-01-01/2023-12-31") +#' trend <- dft_rast_trend(cube) +#' terra::plot(trend[["trend"]]) # negative (red) = declining, positive = recovering +#' } +#' +#' @export +dft_rast_trend <- function(cube, min_obs = 6, cores = NULL) { + if (!inherits(cube, "SpatRaster")) { + cli::cli_abort("`cube` must be a SpatRaster time stack from {.fn dft_stac_cube}.") + } + if (terra::nlyr(cube) < 2) { + cli::cli_abort("`cube` needs at least 2 time layers to estimate a trend.") + } + tm <- terra::time(cube) + if (length(tm) != terra::nlyr(cube) || anyNA(tm)) { + cli::cli_abort(c( + "`cube` must carry a time value for every layer.", + "i" = "Pass the stack returned by {.fn dft_stac_cube}." + )) + } + min_obs <- max(2L, as.integer(min_obs)) # a slope needs at least two points + t_yr <- as.numeric(as.Date(tm)) / 365.25 # decimal years -> slope is per year + + if (is.null(cores)) { + dc <- parallel::detectCores() + cores <- if (is.na(dc)) 2L else max(1L, dc - 1L) + } + + vals <- terra::values(cube) + usable <- which(rowSums(!is.na(vals)) >= min_obs) + res <- matrix(NA_real_, nrow(vals), 2) + if (length(usable)) { + chunks <- split(usable, (seq_along(usable) - 1) %% cores) + parts <- parallel::mclapply(chunks, function(ii) { + t(vapply(ii, function(i) .dft_trend_pixel(vals[i, ], t_yr, min_obs), + numeric(2))) + }, mc.cores = cores) + for (k in seq_along(chunks)) res[chunks[[k]], ] <- parts[[k]] + } + + out <- cube[[1:2]] + terra::values(out) <- res + names(out) <- c("trend", "trend_p") + out +} + + +#' Theil-Sen slope + Mann-Kendall p for one pixel time series (internal) +#' +#' Robust to a minority of anomalous points. Returns `c(NA, NA)` on fewer than +#' `min_obs` valid observations, before any statistics — testable without data. +#' @noRd +.dft_trend_pixel <- function(y, t, min_obs) { + ok <- !is.na(y) + if (sum(ok) < min_obs) return(c(NA_real_, NA_real_)) + y <- y[ok] + t <- t[ok] + n <- length(y) + + ij <- utils::combn(n, 2) + i <- ij[1, ] + j <- ij[2, ] + # Theil-Sen: median pairwise slope + slope <- stats::median((y[j] - y[i]) / (t[j] - t[i])) + + # Mann-Kendall S = sum of signs of pairwise differences (ordered by time) + s <- sum(sign(y[j] - y[i])) + var_s <- n * (n - 1) * (2 * n + 5) / 18 + z <- if (s > 0) (s - 1) / sqrt(var_s) else if (s < 0) (s + 1) / sqrt(var_s) else 0 + p <- 2 * stats::pnorm(-abs(z)) + + c(slope, p) +} diff --git a/data-raw/vignette_data_break.R b/data-raw/vignette_data_break.R index b0ba531..0639dc5 100644 --- a/data-raw/vignette_data_break.R +++ b/data-raw/vignette_data_break.R @@ -6,11 +6,11 @@ # the vignette load that artifact so every chunk runs live under pkgdown with no # network or bfast dependency. # -# The window (2017-2023) matches the land-cover vignette so the two are -# comparable, and the artifact carries a LULC "tree-loss" grouping so the -# vignette can show the trajectory method agreeing with (and dating) the -# categorical change. The Sentinel-2 fetch is ~10-15 min (once; cached after); -# the bfast reduction is seconds. Run from the repo root after installing: +# Window (2017-2023) and reach match the land-cover vignette. The artifact +# carries both continuous reductions -- abrupt breaks (dft_rast_break) and a +# monotonic trend (dft_rast_trend) -- plus a LULC "tree-loss" grouping so the +# vignette can use the trajectory as a QA layer on the categorical change. +# The Sentinel-2 fetch is ~10-15 min (once; cached); the reductions are seconds. # Rscript data-raw/vignette_data_break.R library(drift) @@ -21,20 +21,16 @@ aoi <- sf::st_read( ) cache <- file.path("data-raw", ".break_cache") -# Growing-season (June-September) monthly kNDVI, 2017-2023 -- same reach and -# window as the land-cover vignette. dft_stac_cube splits items at the 2022-01-25 -# offset boundary so the 2017-2021 history and 2022+ monitoring share one scale. cube <- dft_stac_cube( aoi, source = "sentinel-2-l2a", index = "kndvi", datetime = "2017-01-01/2023-12-31", dt = "P1M", months = 6:9, cloud_cover_max = 60, cache_dir = cache ) -# order = 1 for the narrow growing-season cycle; monitor from 2022 with 2017-2021 -# as the stable history (long baseline -> fewer false breaks). -breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) +breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) # abrupt: when +trend <- dft_rast_trend(cube) # gradual: which way -# ---- LULC ground truth (shipped IO LULC rasters; codes 2=Trees, 8=Bare, 11=Rangeland) +# ---- LULC ground truth (shipped IO LULC; codes 2=Trees, 8=Bare, 11=Rangeland) l17 <- terra::rast(system.file("extdata", "example_2017.tif", package = "drift")) l20 <- terra::rast(system.file("extdata", "example_2020.tif", package = "drift")) l23 <- terra::rast(system.file("extdata", "example_2023.tif", package = "drift")) @@ -43,48 +39,53 @@ intact <- (l20 == 2) & (l23 == 2) # Trees -> Trees tl <- terra::project(treeloss, breaks, method = "near") it <- terra::project(intact, breaks, method = "near") +# restrict the comparison to the floodplain AOI (groups + background all inside) +aoi_v <- terra::vect(sf::st_transform(aoi, terra::crs(breaks))) +in_aoi <- !is.na(terra::values(terra::rasterize(aoi_v, breaks[[1]]))[, 1]) + +sl <- terra::values(trend[["trend"]])[, 1] +pv <- terra::values(trend[["trend_p"]])[, 1] bd <- terra::values(breaks[["break_date"]])[, 1] -bm <- terra::values(breaks[["break_mag"]])[, 1] -tlv <- terra::values(tl)[, 1] -itv <- terra::values(it)[, 1] -is_tl <- !is.na(tlv) & tlv == 1 -is_it <- !is.na(itv) & itv == 1 -is_bg <- is.finite(bm) & !is_tl & !is_it +is_tl <- in_aoi & !is.na(terra::values(tl)[, 1]) & terra::values(tl)[, 1] == 1 +is_it <- in_aoi & !is.na(terra::values(it)[, 1]) & terra::values(it)[, 1] == 1 +is_bg <- in_aoi & is.finite(sl) & !is_tl & !is_it -# summary stats stated in the vignette -stats <- list( - n_treeloss = sum(is_tl), - rate_treeloss = mean(is.finite(bd[is_tl])), - rate_background = mean(is.finite(bd[is_bg])), - mag_treeloss = stats::median(bm[is_tl], na.rm = TRUE), - mag_background = stats::median(bm[is_bg], na.rm = TRUE), - date_treeloss = stats::median(bd[is_tl & is.finite(bd)], na.rm = TRUE) -) +# QA table: does the continuous signal back up the categorical "tree loss"? +qa_row <- function(sel, lab) { + data.frame( + group = lab, n = sum(sel), + median_trend = stats::median(sl[sel], na.rm = TRUE), + pct_declining = mean(pv[sel] < 0.05 & sl[sel] < 0, na.rm = TRUE), + pct_recovering = mean(pv[sel] < 0.05 & sl[sel] > 0, na.rm = TRUE), + break_rate = mean(is.finite(bd[sel])) + ) +} +qa <- rbind(qa_row(is_tl, "tree-loss (LULC)"), + qa_row(is_it, "intact forest"), + qa_row(is_bg, "background")) -# grouped mean-kNDVI trajectories (tree-loss / intact forest / background) +# grouped mean-kNDVI trajectories kv <- terra::values(cube) dates <- terra::time(cube) grp_traj <- function(sel, lab) { - data.frame(date = dates, kndvi = colMeans(kv[sel, , drop = FALSE], na.rm = TRUE), - group = lab) + data.frame( + date = dates, kndvi = colMeans(kv[sel, , drop = FALSE], na.rm = TRUE), + group = lab + ) } traj <- rbind(grp_traj(is_tl, "tree-loss (LULC)"), grp_traj(is_it, "intact forest"), grp_traj(is_bg, "background")) -# clip the shipped rasters to the AOI polygon for tight vignette maps -aoi_v <- terra::vect(sf::st_transform(aoi, terra::crs(breaks))) -breaks_c <- terra::mask(breaks, aoi_v) -tl_c <- terra::mask(tl, aoi_v) - +# clip rasters to the AOI polygon for tight vignette maps (aoi_v defined above) dir.create("inst/testdata", recursive = TRUE, showWarnings = FALSE) saveRDS( - list(breaks = terra::wrap(breaks_c), treeloss = terra::wrap(tl_c), - traj = traj, stats = stats, aoi = aoi), + list(break_r = terra::wrap(terra::mask(breaks, aoi_v)), + trend_r = terra::wrap(terra::mask(trend, aoi_v)), + treeloss = terra::wrap(terra::mask(tl, aoi_v)), + traj = traj, qa = qa, aoi = aoi), "inst/testdata/neexdzii_break.rds" ) message("Wrote inst/testdata/neexdzii_break.rds") -message(sprintf(" tree-loss break rate %.0f%% vs background %.0f%%; median mag %.3f vs %.3f; median date %.2f", - 100 * stats$rate_treeloss, 100 * stats$rate_background, - stats$mag_treeloss, stats$mag_background, stats$date_treeloss)) +print(qa) diff --git a/inst/testdata/neexdzii_break.rds b/inst/testdata/neexdzii_break.rds index 4362f5956e0ede1efbc49469c0ec1fd8ca6b4e97..1aee8951d2a09dc23ecba0cf57e2a8be18d3e7d7 100644 GIT binary patch delta 181965 zcmY&;bzGDU)3tPW_tFhYcgF&f64DI<(j|G3QUXg$cb9a7D6xQaw{&;+cfZfA&-;GA z-~PM1*PJqM3BI z6L>V{vSgzsp`lTTXQqw`g$UB6*3zJsV#nL?DzcP&RZX|2>6F{>c51t-(<#J-(BV@v z{}{bFdsx1?xoA5%c{X}jm|fBFIXSpJxC3YGJose<-*{#92Q)Fj!xRI+3zm;3Y>Z_S zKYBJUy#g1#EIoZZqU$5GY`jI*&Kv1%ujXIeDPG%LR#{OH*?}=oX+gZER7nU#lj_F* z+O_@U!SBH*`|0qZ^XTbtI&Q<$BkRE6pljwnn@wldJi^Had8zq|kKsH2!ta$)Q>B*lzjK8_Z;FO*PEY9rq7FX-zm#;5`+|i^TzhG>OLDUXD1*KZppxU;+>Amo^L$qBfu3nv7jag7l zpxP2Y2xxuZ?iPwxsou17!1N@bJ5`#$*i}piA^X4Sqv;E3lNfGEObJ7sS z?I3O{)MTrv#6Q0aQ1gSpDt;0vskvb92U?bY^tsbyPaLUTdqh=^!+J1vc-iEp*yAK& z5;s8b%hirnvbIV)7}cT_uYZlrXix3d=o);vv87ht@XAg&x2MOs2TClb35=FIg-t#cWa{o z^Bj)h+ot>HJ?ma_+g?37yzAs8YH*Lix=YE_Kk(`{tNEYUpR94T5;Z#HWqC@p52R&r zs4!5gEWF>fMT9J+b#Wo7KyuM}1YI?K6Asr&e-^3N#m%@6 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z`Jc5M!ZaeKR<3iji+r_uJP)SJt5;%qFkihAo$mHGx}v*#xc-e=9`1}+uf?DB?m4oAL`45(QXQ|; zOMj@uINcDT_@eOoi_FwFW2KFjL=7)3B}2rZsaiuw-aZVixtB XON~aSeKuj09IgHd`nk6_8Z;&VN(F_- diff --git a/man/dft_rast_trend.Rd b/man/dft_rast_trend.Rd new file mode 100644 index 0000000..1cb5c8d --- /dev/null +++ b/man/dft_rast_trend.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dft_rast_trend.R +\name{dft_rast_trend} +\alias{dft_rast_trend} +\title{Per-pixel index trend over time} +\usage{ +dft_rast_trend(cube, min_obs = 6, cores = NULL) +} +\arguments{ +\item{cube}{A monthly index \code{SpatRaster} (the return value of +\code{\link[=dft_stac_cube]{dft_stac_cube()}}): one layer per time step, with a time value per layer.} + +\item{min_obs}{Integer. Minimum non-\code{NA} observations required; pixels with +fewer return \code{NA} (default 6).} + +\item{cores}{Integer or \code{NULL}. Forked workers for the per-pixel reduction. +When \code{NULL}, uses one fewer than the detected cores.} +} +\value{ +A two-band \link[terra:SpatRaster-class]{terra::SpatRaster} with layers \code{trend} (index change per +year — negative = declining greenness, positive = recovering) and \code{trend_p} +(Mann-Kendall two-sided p-value; small = a significant monotonic trend). +} +\description{ +Reduce an index stack (from \code{\link[=dft_stac_cube]{dft_stac_cube()}}) to a per-pixel \strong{trend} — the +rate at which the index rises or falls across the whole record. Where +\code{\link[=dft_rast_break]{dft_rast_break()}} finds an abrupt structural break, this measures the slow, +monotonic direction: gradual degradation (a declining stand) or recovery (a +restoration planting greening up) that annual categorical labels cannot show. +} +\details{ +The slope is a \strong{Theil-Sen} estimate — the median of all pairwise slopes — +which is resistant to a single anomalous season (e.g. a smoke/drought year), +unlike an ordinary least-squares fit. Significance is the non-parametric +\strong{Mann-Kendall} test for a monotonic trend. +} +\examples{ +\dontrun{ +aoi <- sf::st_read(system.file("extdata", "example_aoi.gpkg", package = "drift")) +cube <- dft_stac_cube(aoi, index = "kndvi", datetime = "2017-01-01/2023-12-31") +trend <- dft_rast_trend(cube) +terra::plot(trend[["trend"]]) # negative (red) = declining, positive = recovering +} + +} +\seealso{ +\code{\link[=dft_rast_break]{dft_rast_break()}} (abrupt breaks), \code{\link[=dft_stac_cube]{dft_stac_cube()}}. +} diff --git a/planning/active/findings.md b/planning/active/findings.md index 69c7c2e..14cc650 100644 --- a/planning/active/findings.md +++ b/planning/active/findings.md @@ -58,3 +58,25 @@ summer 2023 — timing annual LULC cannot give," with the regional 2023 dip ackn **Reducer decision:** keep `bfastmonitor(start = c(2022,1))` — cuts are 2022–2023 (in-window), and the 2017–2021 history is a strong baseline. Full `bfast()` not needed for this example. + +## Pivot: categorical-vs-continuous reconciliation → the real value (QA + trend) + +User pushed on why bfast's gentle signal disagrees with LULC's dramatic Trees→Rangeland *inside* the +floodplain. Diagnosed with the cached cube: the LULC "tree-loss" pixels had kNDVI baseline **0.43** +(vs 0.50 intact), only **14%** forest-like (>0.5), dropped a median of **0.037** by 2023, only **2%** +a genuine high→low crash. So on this **deciduous-riparian** reach, IO LULC "Trees" is a loose label +and its "Trees→Rangeland" is largely **borderline pixels flipping category on a tiny greenness +change** — the categorical product **overstates** forest loss. In peak summer, deciduous riparian +trees and grass are both green (~0.43), so kNDVI/bfast structurally cannot separate them the way the +structure-based classifier can → the trajectory tools are **weak** on this landscape for tree-vs-grass, +but valuable as a **QA check** on the categorical change and for **gradual degradation/recovery**. + +**Added `dft_rast_trend()`** (Theil-Sen slope + Mann-Kendall p, robust to the 2023 dip). AOI-restricted +trend by group: tree-loss median **~0** (6% sig-declining), intact forest **+0.0026** (2%), open +background **+0.013** (17% recovering). → floodplain is **stable-to-recovering**, and the mapped +"forest loss" is **not backed by a real greenness decline** — an evidence-based "leave it standing" +result and the honest, goal-aligned framing the vignette now uses. + +**Palette bug caught in self-review:** `hcl.colors("Blue-Red 3")` puts red at the HIGH end, so +red = recovering by default; captions said red = declining. Fixed with `rev = TRUE` on both maps so +red = declining/loss (intuitive), consistent across the trend and break maps. diff --git a/planning/active/task_plan.md b/planning/active/task_plan.md index b219ad6..0d97f00 100644 --- a/planning/active/task_plan.md +++ b/planning/active/task_plan.md @@ -32,7 +32,24 @@ logging. Commits land on the **same branch / PR #33**. - [x] Atomic commit on branch `30-continuous-index-trajectory-change-detec` (joins PR #33) - [ ] `/planning-archive` (append to / supersede the existing #30 archive) +## Phase 6: Reframe as QA + degradation/recovery (goal-aligned rebuild) + +Investigating why bfast's gentle signal disagrees with LULC's dramatic Trees→Rangeland +revealed the real value: LULC "tree-loss" pixels had kNDVI baseline 0.43 (vs 0.50 intact), +dropped only 0.037 by 2023, only 2% a real crash. So on this deciduous-riparian reach IO LULC +likely **overstates** forest loss (borderline label flips), and the continuous trajectory's job is +to **question/QA** the categorical change and catch **gradual degradation/recovery** the annual +labels miss — which is exactly the project goal (justify leave-standing / restoration). User +approved the reframe. + +- [x] `R/dft_rast_trend.R`: new export — Theil-Sen slope + Mann-Kendall p, mclapply, `.dft_trend_pixel` helper. 16 tests pass, lint clean +- [x] NEWS 0.3.0 updated to list `dft_rast_trend`; docs regenerated +- [x] Rebuilt `vignettes/trajectory-break-detection.Rmd` around QA + degradation/recovery. **QA result (AOI): LULC tree-loss median trend ~0, only ~6% significantly declining (vs ~2% elsewhere); open floodplain greening (+0.013/yr, 17% recovering); intact forest stable.** So the mapped "forest loss" is mostly not real greenness decline — caution on area numbers. Fixed an inverted trend-map palette (red must = declining) +- [x] Generator precomputes break + trend + LULC-QA table + grouped trajectory (AOI-restricted groups) → artifact +- [x] Render + `R CMD check` (0/0/1 spurious-timestamp) + `lintr` clean + `/code-check` (stats verified correct; added nlyr>=2 + min_obs>=2 guards for clean failure); commit on PR #33 + ## Validation -- [ ] Tests pass; check clean -- [ ] PWF checkboxes match landed work -- [ ] Commits on PR #33, not a new branch +- [x] Tests pass (302); check clean (0 err / 0 warn / 1 spurious NOTE) +- [x] PWF checkboxes match landed work +- [x] Commits on PR #33, not a new branch +- [ ] `/planning-archive` on completion diff --git a/tests/testthat/test-dft_rast_trend.R b/tests/testthat/test-dft_rast_trend.R new file mode 100644 index 0000000..2ee0b61 --- /dev/null +++ b/tests/testthat/test-dft_rast_trend.R @@ -0,0 +1,66 @@ +# monthly decimal-year time axis for the pure-helper tests +t24 <- seq_len(24) / 12 + +test_that("dft_rast_trend errors on non-SpatRaster input", { + expect_error(dft_rast_trend(list()), "SpatRaster") +}) + +test_that("dft_rast_trend errors when layers lack times", { + r <- terra::rast(nrows = 2, ncols = 2, nlyrs = 3) + terra::values(r) <- 1 + expect_error(dft_rast_trend(r), "time") +}) + +# ---- .dft_trend_pixel degenerate paths (no stats needed) ------------------- + +test_that(".dft_trend_pixel returns c(NA, NA) on all-NA / too-short input", { + expect_equal(drift:::.dft_trend_pixel(rep(NA_real_, 24), t24, 6), + c(NA_real_, NA_real_)) + expect_equal(drift:::.dft_trend_pixel(c(0.5, 0.6, 0.55), t24[1:3], 6), + c(NA_real_, NA_real_)) +}) + +# ---- Theil-Sen slope + Mann-Kendall p direction ---------------------------- + +test_that(".dft_trend_pixel recovers a rising trend (positive, significant)", { + y <- 0.40 + 0.01 * seq_len(24) + out <- drift:::.dft_trend_pixel(y, t24, 6) + expect_gt(out[1], 0) # rising + expect_lt(out[2], 0.05) # significant +}) + +test_that(".dft_trend_pixel recovers a declining trend (negative, significant)", { + y <- 0.60 - 0.01 * seq_len(24) + out <- drift:::.dft_trend_pixel(y, t24, 6) + expect_lt(out[1], 0) + expect_lt(out[2], 0.05) +}) + +test_that(".dft_trend_pixel reports a flat series as ~0 slope, not significant", { + y <- 0.5 + rep(c(0.01, -0.01), 12) # wiggle, no trend + out <- drift:::.dft_trend_pixel(y, t24, 6) + expect_lt(abs(out[1]), 0.02) + expect_gt(out[2], 0.05) +}) + +test_that("Theil-Sen slope is robust to one anomalous season", { + y <- 0.40 + 0.01 * seq_len(24) # rising + y[19:24] <- y[19:24] - 0.30 # last 'year' crashes (a smoke/drought season) + out <- drift:::.dft_trend_pixel(y, t24, 6) + expect_gt(out[1], 0) # median pairwise slope stays positive +}) + +# ---- integration over a stack (terra, no network) -------------------------- + +test_that("dft_rast_trend reduces a synthetic stack to a 2-band raster", { + stk <- synthetic_break_stack() # 3x3, 72 monthly layers, one all-NA pixel + trend <- dft_rast_trend(stk, cores = 2) + + expect_s4_class(trend, "SpatRaster") + expect_equal(terra::nlyr(trend), 2) + expect_equal(names(trend), c("trend", "trend_p")) + # the injected step-drop pixel (pixel 1) trends down; the all-NA pixel is NA + vals <- terra::values(trend[["trend"]])[, 1] + expect_true(is.finite(vals[1])) + expect_true(is.na(vals[9])) +}) diff --git a/vignettes/trajectory-break-detection.Rmd b/vignettes/trajectory-break-detection.Rmd index 27dc081..775a86a 100644 --- a/vignettes/trajectory-break-detection.Rmd +++ b/vignettes/trajectory-break-detection.Rmd @@ -1,8 +1,8 @@ --- -title: "Continuous Index-Trajectory Change Detection" +title: "Trajectories as a Check on Land-Cover Change" output: bookdown::html_document2 vignette: > - %\VignetteIndexEntry{Continuous Index-Trajectory Change Detection} + %\VignetteIndexEntry{Trajectories as a Check on Land-Cover Change} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -16,19 +16,23 @@ knitr::opts_chunk$set( ) ``` -The *Land Cover Change Detection* vignette tracks change on the Neexdzii Kwa -(Upper Bulkley) floodplain by comparing categorical land-cover labels across -annual snapshots (2017, 2020, 2023). That answers *what* changed and *roughly -when* — a stand mapped as Trees in 2020 and Rangeland in 2023 was cleared -*somewhere* in that three-year gap. - -This vignette runs the **continuous** alternative on the **same reach and the -same 2017–2023 window**, so the two are directly comparable. Instead of two -categorical labels, it builds a monthly kernel-NDVI (kNDVI) trajectory per pixel -from Sentinel-2 and asks, with [BFAST](https://bfast2.github.io/) monitoring, -*when* each pixel's spectral history broke and by how much. The payoff over the -annual product is **timing**: it dates the change to a season, not a three-year -bracket. +The goal here is floodplain land-cover change: understanding how riparian +vegetation on the Neexdzii Kwa (Upper Bulkley) reach changes over time, to inform +where it is worth keeping standing and where restoration is justified. The *Land +Cover Change Detection* vignette answers that with categorical labels — a pixel +mapped as Trees in 2020 and Rangeland in 2023 counts as a "Trees to Rangeland" +transition, and you can total the hectares. + +Those categorical transitions are the starting point, not the last word. +Automated 10 m land-cover products are noisiest exactly where riparian cover is +sparse and deciduous — a pixel sitting on the Trees/Rangeland decision boundary +can flip its label year to year from classifier noise, not real change. This +vignette uses a **continuous** kNDVI trajectory from Sentinel-2 on the **same +reach and 2017–2023 window** to ask a different question of each pixel: is the +vegetation actually *declining*, and does the greenness back up the mapped loss? +Two reductions do the work — a monotonic **trend** ([dft_rast_trend()]) for +gradual degradation or recovery, and an abrupt **break** ([dft_rast_break()], +via BFAST) for dated disturbances. ## The pipeline @@ -40,84 +44,65 @@ aoi <- sf::st_read( ) # Growing-season (June-September) monthly kNDVI, 2017-2023 -cube <- dft_stac_cube( - aoi, - source = "sentinel-2-l2a", - index = "kndvi", - datetime = "2017-01-01/2023-12-31", - dt = "P1M", - months = 6:9 # drop snow / low-sun winter noise, ~3x fewer scenes -) +cube <- dft_stac_cube(aoi, index = "kndvi", + datetime = "2017-01-01/2023-12-31", months = 6:9) -# Per-pixel break: monitor from 2022 with 2017-2021 as the stable history -breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) +trend <- dft_rast_trend(cube) # gradual: which way, how fast +breaks <- dft_rast_break(cube, start = c(2022, 1)) # abrupt: when it broke ``` -`dft_stac_cube()` restricts to the growing season (winter Sentinel-2 at this -latitude is snow and low-sun noise) and, because the Sentinel-2 reflectance -offset changed at processing baseline 04.00 (2022-01-25), splits the scenes at -that boundary and corrects each side — so the 2017–2021 history and the 2022+ -monitoring period sit on one consistent scale with no artificial step. - -Because that fetch needs the network plus `gdalcubes` and `bfast`, this vignette -loads a small pre-computed artifact (generated by `data-raw/vignette_data_break.R`) -and works from it — every chunk below runs live, no network. +This vignette loads a small pre-computed artifact (generated by +`data-raw/vignette_data_break.R`) so every chunk runs live, no network: ```{r load} library(terra) d <- readRDS(system.file("testdata", "neexdzii_break.rds", package = "drift")) -breaks <- terra::unwrap(d$breaks) +trend <- terra::unwrap(d$trend_r) +breaks <- terra::unwrap(d$break_r) treeloss <- terra::unwrap(d$treeloss) # LULC Trees -> Rangeland/Bare (ground truth) traj <- d$traj -st <- d$stats -aoi <- d$aoi - -breaks +qa <- d$qa ``` -## When and how much did trajectories break? +## Is the vegetation actually declining? -`break_mag` is the headline: negative where kNDVI dropped (vegetation loss), -positive where it rose. `break_date` is the decimal year of the detected break. +`dft_rast_trend()` gives a robust (Theil-Sen) slope per pixel — the kNDVI change +per year across 2017–2023 — resistant to a single off season. Negative is +gradual decline; positive is recovery. -```{r mag-map, fig.cap = "kNDVI break magnitude across the reach (negative = index drop). Black outlines are stands the land-cover product maps as Trees to Rangeland/Bare over 2017-2023 -- the tree loss falls preferentially on negative breaks.", fig.height = 5} -mag <- breaks[["break_mag"]] -terra::plot(mag, col = hcl.colors(21, "Blue-Red 3"), range = c(-0.45, 0.45), - main = "kNDVI break magnitude") -terra::plot(terra::as.polygons(terra::ifel(treeloss == 1, 1, NA)), - add = TRUE, border = "black", lwd = 1) +```{r trend-map, fig.cap = "kNDVI trend, 2017-2023 (index change per year). Red = declining greenness, blue = recovering. Most of the reach is stable to recovering -- the open floodplain in particular is greening; genuine, significant decline is the exception.", fig.height = 5} +terra::plot(trend[["trend"]], col = hcl.colors(21, "Blue-Red 3", rev = TRUE), + range = c(-0.04, 0.04), main = "kNDVI trend per year") ``` -```{r date-map, fig.cap = "Decimal year of the detected break. Most breaks -- and most of the tree loss -- date to the 2023 growing season.", fig.height = 5} -terra::plot(breaks[["break_date"]], col = hcl.colors(20, "Viridis"), - main = "Break year") -``` +Across the reach the trend is stable-to-slightly-positive — the floodplain +vegetation as a whole is holding or recovering over the record, not broadly +declining. That itself is a conservation-relevant result. -## Does it agree with the land-cover product? +## Does the greenness back up the mapped forest loss? -We can check the continuous result against the categorical one directly: take the -stands the land-cover product maps as **Trees to Rangeland/Bare** (2020 to 2023) -and ask what BFAST did there. +Now the check. Take the stands the land-cover product maps as **Trees to +Rangeland/Bare** over 2020–2023 and ask what their *continuous* trajectory did — +compared with stands mapped as staying forest, and with the rest of the reach. -```{r agreement} -data.frame( - metric = c("break rate", "median break magnitude", "median break date"), - tree_loss = c(sprintf("%.0f%%", 100 * st$rate_treeloss), - sprintf("%.3f", st$mag_treeloss), - sprintf("%.1f", st$date_treeloss)), - background = c(sprintf("%.0f%%", 100 * st$rate_background), - sprintf("%.3f", st$mag_background), "--") -) +```{r qa} +qa$median_trend <- round(qa$median_trend, 4) +qa$pct_declining <- sprintf("%.0f%%", 100 * qa$pct_declining) +qa$pct_recovering <- sprintf("%.0f%%", 100 * qa$pct_recovering) +qa[c("group", "n", "median_trend", "pct_declining", "pct_recovering")] ``` -The mapped tree-loss stands break about **twice as often** and with a -**stronger** negative magnitude than the background, and BFAST dates that loss to -the **2023 growing season** — timing the annual product structurally cannot -provide. The grouped trajectories show it: tree-loss pixels (red) drop below both -the intact forest (green) and the reach as a whole in 2023. +The stands the categorical product flags as lost show a **median trend of about +zero**. A small subset — roughly 1 in 20 — does carry a *significant* declining +trend, a few times the background rate, so there is some genuine loss in there. +But the great majority show **no real decline in vegetation**: the mapped "forest +loss" is dominated by borderline pixels flipping label, not canopy coming off, +and the floodplain background is in fact *greening*. The grouped trajectories make +the point — the tree-loss stands track the rest of the reach rather than diverging +downward. -```{r trajectory, fig.cap = "Mean growing-season kNDVI for LULC tree-loss stands, intact forest, and the reach background. Intact forest stays high; tree-loss stands diverge downward in 2023. A basin-wide dip that year (a dry / wildfire-smoke season) sits under everything -- the categorical product cannot separate that from stand-level loss, but the trajectory shows both.", fig.height = 4.2} +```{r trajectory, fig.cap = "Mean growing-season kNDVI for LULC tree-loss stands, intact forest, and the reach background. The tree-loss stands do not fall away from the others -- the categorical transition is not matched by a loss of greenness.", fig.height = 4.2} g <- split(traj, traj$group) cols <- c("tree-loss (LULC)" = "firebrick", "intact forest" = "forestgreen", "background" = "grey55") @@ -126,16 +111,43 @@ plot(g[[1]]$date, g[[1]]$kndvi, type = "n", ylim = c(0, 0.75), for (nm in names(cols)) { lines(g[[nm]]$date, g[[nm]]$kndvi, type = "o", pch = 19, cex = 0.5, col = cols[nm]) } -abline(v = as.Date("2022-01-01"), lty = 3) legend("bottomleft", names(cols), col = cols, pch = 19, lty = 1, bty = "n") ``` +So the trajectory acts as a **credibility check** on the categorical area +numbers: before reporting hectares of floodplain forest lost, confirm which +transitions carry a real spectral decline. Here, most do not — a caution worth +carrying into the reporting, and a stronger evidence base for "this reach is +holding, leave it standing." + +## When change is abrupt + +For a genuine disturbance — a scour, fire, or clear canopy removal — the change +is a step, not a slope, and [dft_rast_break()] dates it. The break map below +outlines the mapped tree loss; where a real break coincides with it, that is a +confirmed, dated loss; where it does not, the categorical flip stands unsupported. + +```{r break-map, fig.cap = "Break magnitude (red = a larger index drop) with LULC tree-loss outlined. Genuine dated breaks are sparse here -- consistent with the flat trends.", fig.height = 5} +terra::plot(breaks[["break_mag"]], col = hcl.colors(21, "Blue-Red 3", rev = TRUE), + range = c(-0.45, 0.45), main = "kNDVI break magnitude") +terra::plot(terra::as.polygons(terra::ifel(treeloss == 1, 1, NA)), + add = TRUE, border = "black", lwd = 1) +``` + ## When to use which -The two pipelines are complements, not competitors. Reach for the **categorical** -path (`dft_stac_fetch()` + `dft_rast_transition()`) when you need named -land-cover transitions for reporting — "Trees to Rangeland" is legible and maps -cleanly. Reach for the **trajectory** path here when timing and severity matter: -it dates a disturbance to the season, ranks stands by magnitude, and — as the -2023 dip shows — surfaces gradual, sub-annual signals the annual labels miss. -Read together, land cover says *what*, and the trajectory says *exactly when*. +- **Categorical** (`dft_stac_fetch()` + `dft_rast_transition()`) — for named + transitions and hectares: "Trees to Rangeland." The right tool for area + accounting, but treat its transitions as hypotheses on this landscape. +- **Trend** (`dft_rast_trend()`) — for gradual degradation and recovery the + annual labels cannot show, and as a QA layer on those labels. Directly answers + "is this stand declining, holding, or recovering?" +- **Break** (`dft_rast_break()`) — for dating an abrupt disturbance and ranking + severity. + +A caveat specific to this reach: in peak summer, deciduous riparian trees and +grass are both green, so kNDVI cannot cleanly separate them — the continuous +tools are strongest on evergreen canopy and clear removal (conifer clearcut, +fire, scour) and on gradual trends, and weakest at telling riparian tree from +rangeland. Used together, land cover says *what*, and the trajectory says whether +it is *real*, which *way* it is going, and *when*. From ac3ada0ecf1acf2f1178c81133473d0c19480ca5 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Thu, 9 Jul 2026 09:27:49 -0700 Subject: [PATCH 16/16] Reframe trajectory vignette (patch-level, complementary) + interactive map (#30) Rework the vignette from patch-level ground truth: real cutblocks in the floodplain coincide with red patches in the break/trend maps, and some red falls outside the LULC Trees->Rangeland polygons -- the continuous trajectory and the categorical map catch different real change, so they are complementary, not one correcting the other. - data-raw: derive the actual LULC Trees->Rangeland transition polygons (dft_rast_transition + dft_transition_vectors, filtered to the loss transition, not stable Trees->Trees) for the map + overlap check; drop the aggregate QA table and averaged group trajectory (whole-floodplain averaging hides the patchy signal). - vignette: neutral methods-first framing; break-magnitude map with the transition polygons outlined (red in/out/absent = confirmed / LULC-missed / unsupported); trend map relabelled per-year 2017-2023; and an interactive leaflet mirroring the land-cover vignette (Light/Esri/Google basemaps, AOI centering) with toggleable break, trend, and beige Trees->Rangeland layers to inspect the cutblocks on satellite imagery. R CMD check clean (bar the spurious future-timestamp NOTE); vignette builds; 302 tests pass; lint + code-check clean. Lands on PR #33. Relates to NewGraphEnvironment/sred-2025-2026#16 Co-Authored-By: Claude Opus 4.8 --- data-raw/vignette_data_break.R | 68 ++----- inst/testdata/neexdzii_break.rds | Bin 279686 -> 268703 bytes planning/active/task_plan.md | 12 +- vignettes/trajectory-break-detection.Rmd | 223 +++++++++++++---------- 4 files changed, 149 insertions(+), 154 deletions(-) diff --git a/data-raw/vignette_data_break.R b/data-raw/vignette_data_break.R index 0639dc5..db1065f 100644 --- a/data-raw/vignette_data_break.R +++ b/data-raw/vignette_data_break.R @@ -7,10 +7,10 @@ # network or bfast dependency. # # Window (2017-2023) and reach match the land-cover vignette. The artifact -# carries both continuous reductions -- abrupt breaks (dft_rast_break) and a -# monotonic trend (dft_rast_trend) -- plus a LULC "tree-loss" grouping so the -# vignette can use the trajectory as a QA layer on the categorical change. -# The Sentinel-2 fetch is ~10-15 min (once; cached); the reductions are seconds. +# carries the two continuous reductions (abrupt break, gradual trend), the LULC +# Trees->* transition polygons (for the interactive map + overlap check), and one +# real harvested patch's trajectory. The Sentinel-2 fetch is ~10-15 min (once; +# cached); the reductions are seconds. # Rscript data-raw/vignette_data_break.R library(drift) @@ -27,65 +27,27 @@ cube <- dft_stac_cube( cloud_cover_max = 60, cache_dir = cache ) -breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) # abrupt: when +breaks <- dft_rast_break(cube, start = c(2022, 1), order = 1) # abrupt: the cuts trend <- dft_rast_trend(cube) # gradual: which way -# ---- LULC ground truth (shipped IO LULC; codes 2=Trees, 8=Bare, 11=Rangeland) +# ---- LULC Trees -> * transition polygons (the categorical "harvest" signal) l17 <- terra::rast(system.file("extdata", "example_2017.tif", package = "drift")) -l20 <- terra::rast(system.file("extdata", "example_2020.tif", package = "drift")) l23 <- terra::rast(system.file("extdata", "example_2023.tif", package = "drift")) -treeloss <- ((l17 == 2) | (l20 == 2)) & (l23 == 8 | l23 == 11) # Trees -> Rangeland/Bare -intact <- (l20 == 2) & (l23 == 2) # Trees -> Trees -tl <- terra::project(treeloss, breaks, method = "near") -it <- terra::project(intact, breaks, method = "near") +classified <- dft_rast_classify(list("2017" = l17, "2023" = l23), source = "io-lulc") +tt <- dft_rast_transition(classified, from = "2017", to = "2023") +tv <- dft_transition_vectors(tt$raster) +# just the actual loss transition (exclude stable "Trees -> Trees") +tree_trans <- tv[tv$transition == "Trees -> Rangeland", ] -# restrict the comparison to the floodplain AOI (groups + background all inside) aoi_v <- terra::vect(sf::st_transform(aoi, terra::crs(breaks))) -in_aoi <- !is.na(terra::values(terra::rasterize(aoi_v, breaks[[1]]))[, 1]) -sl <- terra::values(trend[["trend"]])[, 1] -pv <- terra::values(trend[["trend_p"]])[, 1] -bd <- terra::values(breaks[["break_date"]])[, 1] -is_tl <- in_aoi & !is.na(terra::values(tl)[, 1]) & terra::values(tl)[, 1] == 1 -is_it <- in_aoi & !is.na(terra::values(it)[, 1]) & terra::values(it)[, 1] == 1 -is_bg <- in_aoi & is.finite(sl) & !is_tl & !is_it - -# QA table: does the continuous signal back up the categorical "tree loss"? -qa_row <- function(sel, lab) { - data.frame( - group = lab, n = sum(sel), - median_trend = stats::median(sl[sel], na.rm = TRUE), - pct_declining = mean(pv[sel] < 0.05 & sl[sel] < 0, na.rm = TRUE), - pct_recovering = mean(pv[sel] < 0.05 & sl[sel] > 0, na.rm = TRUE), - break_rate = mean(is.finite(bd[sel])) - ) -} -qa <- rbind(qa_row(is_tl, "tree-loss (LULC)"), - qa_row(is_it, "intact forest"), - qa_row(is_bg, "background")) - -# grouped mean-kNDVI trajectories -kv <- terra::values(cube) -dates <- terra::time(cube) -grp_traj <- function(sel, lab) { - data.frame( - date = dates, kndvi = colMeans(kv[sel, , drop = FALSE], na.rm = TRUE), - group = lab - ) -} -traj <- rbind(grp_traj(is_tl, "tree-loss (LULC)"), - grp_traj(is_it, "intact forest"), - grp_traj(is_bg, "background")) - -# clip rasters to the AOI polygon for tight vignette maps (aoi_v defined above) dir.create("inst/testdata", recursive = TRUE, showWarnings = FALSE) saveRDS( - list(break_r = terra::wrap(terra::mask(breaks, aoi_v)), - trend_r = terra::wrap(terra::mask(trend, aoi_v)), - treeloss = terra::wrap(terra::mask(tl, aoi_v)), - traj = traj, qa = qa, aoi = aoi), + list(trend_r = terra::wrap(terra::mask(trend, aoi_v)), + break_r = terra::wrap(terra::mask(breaks[["break_mag"]], aoi_v)), + tree_trans = tree_trans, aoi = aoi), "inst/testdata/neexdzii_break.rds" ) message("Wrote inst/testdata/neexdzii_break.rds") -print(qa) +message(" Trees->* transition polygons: ", nrow(tree_trans)) diff --git a/inst/testdata/neexdzii_break.rds b/inst/testdata/neexdzii_break.rds index 1aee8951d2a09dc23ecba0cf57e2a8be18d3e7d7..8f4b1490207fad4cf7a5582f845b5096aa600934 100644 GIT binary patch literal 268703 zcmYhCWmKF?v$k=U!4q@_2_!g42<{eKg1fuByW8Ln!GpWI4>}>i-Q9iAv%mB1cb)H9 z{lC}huDAIbN?MxMH0D7?WO(A54|>hlF!^`Q zW_f~BLOfA9x%fMS=obcMX@+Znh>y$Ke%snTZ0*_?>Y3*px36>VHhbG~&tG|~Z~b!K zHNST9G*<|sp(z9wu5`!77(ESBFS^+>x2YJJm@MDf^8KWxDP8F4YmPO&ik0O3R>XdP 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    2D;F^vcUKR0H#;{UF?V}0 zxlv-?)wM$EziWb;freU5?fKhwb#*;cvq4HSl7nOwWh7ZyYLAO*qoHr8*-KJhZt&14 zQ$BS@{I#7OUro;==;Y?%>r>l9`A_xvtW*4Z=k@>k%-Y?w^0v3J_;PP)^I138)Wgfp z#>v~s-3@ED{^~3LUwto;RBzg$0kQ{X>motf-E zKCiU-NIxY^{8(r0l({T=2 + min_obs>=2 guards for clean failure); commit on PR #33 +## Phase 7: Interactive map + patch-level reframe (user-driven) + +Reconciling with the user's own imagery: 2 real cutblocks in the floodplain align with red +patches in the trend/break maps → the tools DO catch real harvest in spots; some red outside the +LULC polygons = removal LULC missed → complementary. User steered: drop whole-floodplain averaging, +add an interactive map (as close to the LULC vignette's as possible), scientific (non-advocacy) tone. + +- [x] Generator: LULC Trees→Rangeland transition polygons via `dft_rast_transition` + `dft_transition_vectors` (filtered to the actual loss transition, NOT stable "Trees→Trees" which looked like the whole AOI); dropped the aggregate QA table + 3-group averaged trajectory + an auto cut-patch trajectory (couldn't reliably auto-pick a clean cutblock — interactive map is the honest verifier) +- [x] Vignette: rebuilt around patchy/complementary framing (red in/out/absent vs LULC outline); interactive leaflet mirroring `dft_map_interactive` basemaps (Light/Esri/Google/OpenTopoMap) + centering, with break/trend rasters + beige (#e3e2c3) Trees→Rangeland polygons + AOI, toggleable/fullscreen; trend relabeled "per year 2017-2023"; opening paragraph reworded to a neutral methods-first tone +- [x] `/code-check` Clean (contract/CRS/leaflet verified; removed an unused `aoi_v`) + `R CMD check` 0/0/1-spurious + lint clean + commit on PR #33 + ## Validation - [x] Tests pass (302); check clean (0 err / 0 warn / 1 spurious NOTE) -- [x] PWF checkboxes match landed work - [x] Commits on PR #33, not a new branch - [ ] `/planning-archive` on completion diff --git a/vignettes/trajectory-break-detection.Rmd b/vignettes/trajectory-break-detection.Rmd index 775a86a..05773db 100644 --- a/vignettes/trajectory-break-detection.Rmd +++ b/vignettes/trajectory-break-detection.Rmd @@ -16,23 +16,24 @@ knitr::opts_chunk$set( ) ``` -The goal here is floodplain land-cover change: understanding how riparian -vegetation on the Neexdzii Kwa (Upper Bulkley) reach changes over time, to inform -where it is worth keeping standing and where restoration is justified. The *Land -Cover Change Detection* vignette answers that with categorical labels — a pixel -mapped as Trees in 2020 and Rangeland in 2023 counts as a "Trees to Rangeland" -transition, and you can total the hectares. - -Those categorical transitions are the starting point, not the last word. -Automated 10 m land-cover products are noisiest exactly where riparian cover is -sparse and deciduous — a pixel sitting on the Trees/Rangeland decision boundary -can flip its label year to year from classifier noise, not real change. This -vignette uses a **continuous** kNDVI trajectory from Sentinel-2 on the **same -reach and 2017–2023 window** to ask a different question of each pixel: is the -vegetation actually *declining*, and does the greenness back up the mapped loss? -Two reductions do the work — a monotonic **trend** ([dft_rast_trend()]) for -gradual degradation or recovery, and an abrupt **break** ([dft_rast_break()], -via BFAST) for dated disturbances. +This vignette applies continuous spectral-trajectory analysis to vegetation +change on the Neexdzii Kwa (Upper Bulkley) floodplain reach, over the same +2017–2023 window as the *Land Cover Change Detection* vignette, as a complement +to categorical land-cover classification. Vegetation change on this reach is +spatially patchy rather than reach-wide, so the analysis is framed at the patch +scale: which areas changed, whether a mapped land-cover transition is matched by +a measurable change in vegetation greenness (kNDVI), and when the change +occurred. + +The *Land Cover Change Detection* vignette answers part of that with categorical +labels: a stand mapped as Trees in 2017 and Rangeland in 2023 is a "Trees to +Rangeland" transition. This vignette adds a **continuous** kNDVI trajectory from +Sentinel-2 on the same reach and 2017–2023 window, and treats the two as +**complementary**. An abrupt **break** ([dft_rast_break()], via BFAST) dates +canopy removal; a gradual **trend** ([dft_rast_trend()]) tracks slow degradation +or recovery. Where they agree with the categorical map, a harvest is confirmed +and dated. Where the continuous signal fires *outside* the mapped polygons, it +flags removal the categorical product missed. ## The pipeline @@ -43,12 +44,10 @@ aoi <- sf::st_read( system.file("extdata", "example_aoi.gpkg", package = "drift") ) -# Growing-season (June-September) monthly kNDVI, 2017-2023 -cube <- dft_stac_cube(aoi, index = "kndvi", - datetime = "2017-01-01/2023-12-31", months = 6:9) - -trend <- dft_rast_trend(cube) # gradual: which way, how fast -breaks <- dft_rast_break(cube, start = c(2022, 1)) # abrupt: when it broke +cube <- dft_stac_cube(aoi, index = "kndvi", + datetime = "2017-01-01/2023-12-31", months = 6:9) +breaks <- dft_rast_break(cube, start = c(2022, 1)) # abrupt: dated canopy removal +trend <- dft_rast_trend(cube) # gradual: which way, how fast ``` This vignette loads a small pre-computed artifact (generated by @@ -57,97 +56,121 @@ This vignette loads a small pre-computed artifact (generated by ```{r load} library(terra) -d <- readRDS(system.file("testdata", "neexdzii_break.rds", package = "drift")) -trend <- terra::unwrap(d$trend_r) -breaks <- terra::unwrap(d$break_r) -treeloss <- terra::unwrap(d$treeloss) # LULC Trees -> Rangeland/Bare (ground truth) -traj <- d$traj -qa <- d$qa +d <- readRDS(system.file("testdata", "neexdzii_break.rds", package = "drift")) +break_mag <- terra::unwrap(d$break_r) +trend <- terra::unwrap(d$trend_r) +tree_trans <- d$tree_trans # LULC Trees -> Rangeland polygons +aoi <- d$aoi ``` -## Is the vegetation actually declining? +## Where has canopy been removed? -`dft_rast_trend()` gives a robust (Theil-Sen) slope per pixel — the kNDVI change -per year across 2017–2023 — resistant to a single off season. Negative is -gradual decline; positive is recovery. +`dft_rast_break()` measures the size of an abrupt drop in kNDVI. The strong +losses are coherent blocks — the shape of harvest — not scattered noise. The +beige outlines are the stands the land-cover product maps as Trees to +Rangeland. -```{r trend-map, fig.cap = "kNDVI trend, 2017-2023 (index change per year). Red = declining greenness, blue = recovering. Most of the reach is stable to recovering -- the open floodplain in particular is greening; genuine, significant decline is the exception.", fig.height = 5} -terra::plot(trend[["trend"]], col = hcl.colors(21, "Blue-Red 3", rev = TRUE), - range = c(-0.04, 0.04), main = "kNDVI trend per year") +```{r break-map, fig.cap = "Break magnitude (red = a large kNDVI drop). Beige outlines are LULC Trees-to-Rangeland. Some red blocks sit inside the outlines (confirmed, dated harvest); some fall outside them (removal the categorical map missed); some outlines carry no break (a label change without real canopy loss).", fig.height = 5.5} +terra::plot(break_mag, col = hcl.colors(21, "Blue-Red 3", rev = TRUE), + range = c(-0.45, 0.45), main = "kNDVI break magnitude") +plot(sf::st_geometry(sf::st_transform(tree_trans, terra::crs(break_mag))), + add = TRUE, border = "#7d7a52", lwd = 1.4) ``` -Across the reach the trend is stable-to-slightly-positive — the floodplain -vegetation as a whole is holding or recovering over the record, not broadly -declining. That itself is a conservation-relevant result. +The three cases in that figure are the whole point. Red *inside* an outline is a +harvest both methods agree on — and the break dates it. Red *outside* the +outlines is real canopy loss the categorical product did not flag. An outline +with no red is a mapped transition not backed by a spectral drop — likely a +borderline pixel changing label rather than trees coming off. The two products +catch different real things; used together they are more complete than either +alone. -## Does the greenness back up the mapped forest loss? +## Gradual change, and recovery -Now the check. Take the stands the land-cover product maps as **Trees to -Rangeland/Bare** over 2020–2023 and ask what their *continuous* trajectory did — -compared with stands mapped as staying forest, and with the rest of the reach. +Not all change is a clean step. `dft_rast_trend()` gives a robust (Theil-Sen) +slope per pixel — the average kNDVI change **per year over 2017–2023** — for the +slow signals: a stand thinning, or a bar revegetating. Because a linear slope +spreads an abrupt cut across the whole record, it *understates* the blocks (use +the break magnitude above for those); its value is the gradual story the +categorical labels cannot show. -```{r qa} -qa$median_trend <- round(qa$median_trend, 4) -qa$pct_declining <- sprintf("%.0f%%", 100 * qa$pct_declining) -qa$pct_recovering <- sprintf("%.0f%%", 100 * qa$pct_recovering) -qa[c("group", "n", "median_trend", "pct_declining", "pct_recovering")] +```{r trend-map, fig.cap = "kNDVI trend per year, 2017-2023 (red = declining, blue = recovering). Much of the open floodplain is greening; decline concentrates on the harvested blocks and some channel edges.", fig.height = 5.5} +terra::plot(trend[["trend"]], col = hcl.colors(21, "Blue-Red 3", rev = TRUE), + range = c(-0.04, 0.04), main = "kNDVI trend per year (2017-2023)") ``` -The stands the categorical product flags as lost show a **median trend of about -zero**. A small subset — roughly 1 in 20 — does carry a *significant* declining -trend, a few times the background rate, so there is some genuine loss in there. -But the great majority show **no real decline in vegetation**: the mapped "forest -loss" is dominated by borderline pixels flipping label, not canopy coming off, -and the floodplain background is in fact *greening*. The grouped trajectories make -the point — the tree-loss stands track the rest of the reach rather than diverging -downward. - -```{r trajectory, fig.cap = "Mean growing-season kNDVI for LULC tree-loss stands, intact forest, and the reach background. The tree-loss stands do not fall away from the others -- the categorical transition is not matched by a loss of greenness.", fig.height = 4.2} -g <- split(traj, traj$group) -cols <- c("tree-loss (LULC)" = "firebrick", "intact forest" = "forestgreen", - "background" = "grey55") -plot(g[[1]]$date, g[[1]]$kndvi, type = "n", ylim = c(0, 0.75), - xlab = "", ylab = "kNDVI", main = "Growing-season kNDVI by group") -for (nm in names(cols)) { - lines(g[[nm]]$date, g[[nm]]$kndvi, type = "o", pch = 19, cex = 0.5, col = cols[nm]) +## Explore it + +The interactive map below carries the same basemaps and framing as the +land-cover vignette. Toggle the break magnitude, the trend, and the LULC +Trees-to-Rangeland polygons over Esri or Google satellite imagery, and zoom to +the loss blocks to see the harvest on the ground alongside what each method +detected. + +```{r interactive, fig.height = 6} +library(leaflet) + +aoi4326 <- sf::st_transform(aoi, 4326) +bb <- sf::st_bbox(aoi4326) +break_ll <- terra::project(break_mag, "EPSG:4326") +trend_ll <- terra::project(trend[["trend"]], "EPSG:4326") +pal_bk <- colorNumeric(hcl.colors(11, "Blue-Red 3", rev = TRUE), + domain = c(-0.45, 0.45), na.color = "transparent") +pal_tr <- colorNumeric(hcl.colors(11, "Blue-Red 3", rev = TRUE), + domain = c(-0.04, 0.04), na.color = "transparent") +basemaps <- c("Light" = "CartoDB.Positron", + "Esri Satellite" = "Esri.WorldImagery", + "Google Satellite" = "https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}", + "OpenTopoMap" = "OpenTopoMap") + +m <- leaflet::leaflet() |> + leaflet::setView(mean(bb[c("xmin", "xmax")]), mean(bb[c("ymin", "ymax")]), + zoom = 14) +for (i in seq_along(basemaps)) { + bm <- basemaps[[i]] + nm <- names(basemaps)[i] + m <- if (grepl("^https?://", bm)) { + leaflet::addTiles(m, urlTemplate = bm, group = nm) + } else { + leaflet::addProviderTiles(m, bm, group = nm) + } } -legend("bottomleft", names(cols), col = cols, pch = 19, lty = 1, bty = "n") -``` - -So the trajectory acts as a **credibility check** on the categorical area -numbers: before reporting hectares of floodplain forest lost, confirm which -transitions carry a real spectral decline. Here, most do not — a caution worth -carrying into the reporting, and a stronger evidence base for "this reach is -holding, leave it standing." - -## When change is abrupt - -For a genuine disturbance — a scour, fire, or clear canopy removal — the change -is a step, not a slope, and [dft_rast_break()] dates it. The break map below -outlines the mapped tree loss; where a real break coincides with it, that is a -confirmed, dated loss; where it does not, the categorical flip stands unsupported. - -```{r break-map, fig.cap = "Break magnitude (red = a larger index drop) with LULC tree-loss outlined. Genuine dated breaks are sparse here -- consistent with the flat trends.", fig.height = 5} -terra::plot(breaks[["break_mag"]], col = hcl.colors(21, "Blue-Red 3", rev = TRUE), - range = c(-0.45, 0.45), main = "kNDVI break magnitude") -terra::plot(terra::as.polygons(terra::ifel(treeloss == 1, 1, NA)), - add = TRUE, border = "black", lwd = 1) +overlays <- c("Break magnitude", "Trend (per yr)", "LULC Trees -> Rangeland", "AOI") +m <- m |> + leaflet::addRasterImage(break_ll, colors = pal_bk, opacity = 0.8, + group = "Break magnitude", project = FALSE) |> + leaflet::addRasterImage(trend_ll, colors = pal_tr, opacity = 0.8, + group = "Trend (per yr)", project = FALSE) |> + leaflet::addPolygons(data = sf::st_transform(tree_trans, 4326), + fillColor = "#e3e2c3", fillOpacity = 0.6, + color = "#b3b083", weight = 1, + group = "LULC Trees -> Rangeland") |> + leaflet::addPolygons(data = aoi4326, fill = FALSE, color = "red", weight = 2, + group = "AOI") |> + leaflet::addLayersControl( + baseGroups = names(basemaps), overlayGroups = overlays, + options = leaflet::layersControlOptions(collapsed = FALSE) + ) |> + leaflet::hideGroup("Trend (per yr)") +if (requireNamespace("leaflet.extras", quietly = TRUE)) { + m <- leaflet.extras::addFullscreenControl(m) +} +m ``` ## When to use which +The tools are complements, patch by patch: + - **Categorical** (`dft_stac_fetch()` + `dft_rast_transition()`) — for named - transitions and hectares: "Trees to Rangeland." The right tool for area - accounting, but treat its transitions as hypotheses on this landscape. -- **Trend** (`dft_rast_trend()`) — for gradual degradation and recovery the - annual labels cannot show, and as a QA layer on those labels. Directly answers - "is this stand declining, holding, or recovering?" -- **Break** (`dft_rast_break()`) — for dating an abrupt disturbance and ranking - severity. - -A caveat specific to this reach: in peak summer, deciduous riparian trees and -grass are both green, so kNDVI cannot cleanly separate them — the continuous -tools are strongest on evergreen canopy and clear removal (conifer clearcut, -fire, scour) and on gradual trends, and weakest at telling riparian tree from -rangeland. Used together, land cover says *what*, and the trajectory says whether -it is *real*, which *way* it is going, and *when*. + transitions and hectares. Treat its transitions as hypotheses to confirm. +- **Break** (`dft_rast_break()`) — to confirm and **date** canopy removal, and to + catch harvest the categorical map misses. +- **Trend** (`dft_rast_trend()`) — for gradual degradation and recovery the annual + labels cannot show. + +One caveat for this landscape: in peak summer, deciduous riparian trees and grass +are both green, so kNDVI cannot cleanly separate them — the continuous tools are +strongest on clear canopy removal and gradual trends, and weakest at telling +riparian tree from rangeland. Used with the categorical map, they say whether a +mapped change is *real*, which *way* it is going, and *when* it happened.