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) Preconditions 1 and 2 are *checked* here but *established* elsewhere. When either fails, the owning skill leads and this skill resumes once comparable observations exist. Precondition 3 is this skill's own problem and is never a reason to route away. 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. For elevation surfaces or point clouds, vertical datum agreement, co-registration and the vertical-accuracy budget belong to `point-cloud-lidar` — a datum offset is not subsidence. 2. **Radiometric consistency**: same processing level (surface reflectance), same sensor, same processing baseline. If any of the three differ, this is a harmonization problem, not a thresholding one: hand it to `remote-sensing-analysis` (HLS for Landsat↔Sentinel-2, relative normalization with PIFs, `BOA_ADD_OFFSET` across the Sentinel-2 2022 baseline change). 3. **Same season / phenological stage** for bi-temporal work — a May vs September pair "detects" summer. If season ...