| name | change-detection |
| description | Invoke whenever the primary question is what, where, or how much changed between times, including two-scene comparisons, deforestation, urban growth, disaster damage, and parcel-change audits. Covers bi-temporal differencing, post-classification comparison, adjusted area, and time-series trend/break analysis (BFAST/LandTrendr/CCDC-style). Invoke even when seasons, sensors, or processing levels are not comparable; diagnosing an invalid comparison is part of change analysis. Use remote-sensing-analysis for single-date preparation and add google-earth-engine only when GEE executes the work. |
| license | MIT |
| metadata | {"version":"0.1.0","author":"Muhammed Enes Duran"} |
Change Detection & Spatio-temporal Analysis
Purpose: separate real surface change from the four great impostors —
misregistration, radiometric drift, phenology, and classification error.
Every method below exists to control one of them; skipping the controls
produces confident maps of nothing.
Preconditions (where change detection is won or lost)
- Co-registration: sub-pixel alignment between dates (AROSICS or
manual tie-points). Half a pixel of shift creates edge-shaped phantom
change everywhere. Verify: flicker-compare crisp features.
- Radiometric consistency: same processing level (surface
reflectance), same sensor if possible; if mixing sensors, harmonize
(e.g., Landsat↔Sentinel-2 HLS) or use relative normalization (PIFs).
- Same season / phenological stage for bi-temporal work — a May vs
September pair "detects" summer. If season can't be matched, use
composites or time-series methods instead.
- Cloud/shadow masks intersected across dates; analyze only mutually
valid pixels and report that coverage %.
Method selection
| Situation | Method |
|---|
| Two dates, continuous "how much" | Index differencing (ΔNDVI, ΔNBR...) with statistical thresholding |
| Two dates, categorical "from-what-to-what" | Post-classification comparison (only with strong classifiers) |
| Two dates, multivariate robust | Change vector analysis (CVA); MAD/iMAD for sensor-robust detection |
| Dense stack, gradual + abrupt | Trend + break analysis (BFAST/LandTrendr/CCDC family; at archive scale → google-earth-engine) |
| Structure change (buildings) | DL bi-temporal segmentation (siamese U-Net) → geo-deep-learning |
| SAR pairs (clouds, disasters) | Log-ratio of calibrated backscatter + speckle handling |
| Vector vintages (parcels, buildings) | Geometry+attribute diff with tolerance (below) |
Thresholding — never eyeball it
Difference images need a defensible threshold: μ ± k·σ on the difference
histogram (report k), Otsu when bimodal, or supervised thresholds
calibrated on labeled change/no-change samples. Deliver the histogram with
the chosen cut marked. Sensitivity: report changed-area at k-0.5 and
k+0.5; if the story flips, the detection is fragile — say so.
Post-classification comparison (PCC) — handle with care
PCC error compounds: two 90%-accurate maps yield ≤ ~81% change accuracy,
and biased errors create systematic false transitions. Rules:
- Use ONE classifier trained on both dates' imagery (same legend, same
features) rather than two independent legacy maps.
- Build the full transition matrix (from-class → to-class areas), not
just a change/no-change binary — impossible transitions (water→forest
in 1 year) are your error detector.
- Apply a minimum mapping unit consistent across dates before differencing.
Time-series (dense stack) analysis
- Build a gap-filled, cloud-masked index stack (xarray, time dimension).
- Decompose trend + seasonality + breaks; per-pixel linear trends need
significance testing (Mann-Kendall + Sen's slope for monotonic trends —
and FDR correction across millions of pixels, or your "greening map" is
noise).
- Label break DATES, not just presence — timing is usually the analytic
payload (when did clearing start?).
- Validate detected breaks against known events (fires, construction
permits, disaster dates) wherever records exist.
Vector change audit (two vintages of the same layer)
- Match features by stable ID if it exists; else spatial matching with IoU
threshold (report it).
- Classify: added / removed / geometry-changed (area delta > tolerance) /
attribute-changed. Tolerances absorb digitization jitter — 1-2 m for
cadastre-grade, more for digitized-from-imagery.
- Sum area deltas by class and reconcile totals; unexplained residual =
matching bugs.
Accuracy assessment (the deliverable's spine)
Change is rare, so random sampling wastes effort on stable pixels — use
stratified sampling (strata: change/no-change or per-transition) with
good-practice area estimation (Olofsson et al. protocol): report
user's/producer's accuracy per stratum AND area estimates with
confidence intervals adjusted for map error. A raw pixel count of the
change map is a biased area estimate — always say the adjusted number.
Reporting template
## Change: <phenomenon>, <T1> → <T2 or period>
- Data: <sensor/level>, co-registration RMSE: <px>, valid overlap: <%>
- Method: <...> threshold/params: <...> (sensitivity: <stable/fragile>)
- Transitions: <matrix or top-5 list with areas ± CI>
- Accuracy: stratified n=<>, UA/PA per class, adjusted areas ± CI
- Impostor controls: season <matched?>, radiometry <harmonized?>
Pitfalls checklist
- Phantom edge-change from misregistration.
- Seasonal difference sold as land cover change.
- PCC with two independently produced legacy maps.
- Threshold chosen "because it looked right", no sensitivity.
- Raw changed-pixel counts reported as area (no error-adjusted estimate).
- Trend maps without multiple-testing control.
- SAR change on unfiltered linear-power images.
Execution contract
- Workflow: define the change question; harmonize extent, season, radiometry, resolution, and registration; select method; estimate change; validate; report uncertainty.
- Decision rules: use direct differencing only for comparable continuous signals, post-classification comparison for stable class legends, and time-series methods when a dense temporal stack exists.
- Verification protocol: quantify co-registration, valid overlap, threshold sensitivity, transition accounting, and accuracy-adjusted area with confidence intervals.
- Failure modes: reject causal change claims when season, sensor, clouds, registration, or independent map errors can explain the signal.
- Deliverables: change map, transition or trend table, parameter record, validation sample and metrics, adjusted-area estimate, and limitations.
- Source freshness: consult the authoritative source registry before using version-sensitive products or APIs and record the checked date.
