change-detection

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)

1. 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.
2. 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).
3. 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.
4. 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.