Change Detection

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.

1. 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).

1. 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.

1. 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: ,  → 
- Data: , co-registration RMSE: , valid overlap: 
- Method:  threshold/params:  (sensitivity: )
- Transitions: 
- Accuracy: stratified n=<>, UA/PA per class, adjusted areas ± CI
- Impostor controls: season , radiometry 

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](references/authoritative-sources.md) before using version-sensitive products or APIs and record the checked date.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: muend
• Source: muend/geoai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.