Data & analysis
Monitor wetland extent, inundation frequency, and land cover transitions to identify degradation, recovery, and human encroachment. Use when the user wants to track wetland changes over time, assess wetland health, or detect conversion of wetlands to other land covers.
Monitor wetland extent, inundation frequency, and land cover transitions to identify degradation, recovery, and human encroachment. Use when the user wants to track wetland changes over time, assess wetland health, or detect conversion of wetlands to other land covers.
Monitors wetland extent, inundation frequency, and land cover transitions from multi-year water occurrence rasters. Identifies degradation, recovery, and human encroachment patterns.
python scripts/wetland_change_monitor.py --water-frequency water_freq.tif --years 2015 2016 2017 2018 2019
python scripts/wetland_change_monitor.py --water-frequency wf.tif --years 2018 2019 2020 --frequency-thresholds 0.25 0.75
python scripts/wetland_change_monitor.py --water-masks masks.tif --years 2015 2020 --change-schema degradation recovery stable
本 skill 可自动从 Microsoft Planetary Computer 下载水体数据 (无需 API key):
# 自动下载 JRC GSW (water frequency) + Sentinel-1 GRD (water mask)
python scripts/wetland_change_monitor.py \
--bbox 116.30,39.85,116.45,39.95 \
--date-range 2020-06-01,2020-09-30 \
--years 2015 2020 \
--output-dir ./wcm-auto
下载的内容:
jrc-gsw) — 历史水体频率，替换 --water-frequencysentinel-1-grd) — SAR 水体掩膜，替换 --water-masks下载元数据（data_source, fetched_at, collection, jrc_downloaded, sentinel1_downloaded）会写入 output-manifest.json。
| Parameter | Description |
|---|---|
--water-frequency | Multi-band water frequency raster (JRC-style, bands = years) |
--water-masks | Alternative: multi-band binary water masks (1=water, 0=dry) |
--years | List of years corresponding to bands |
--frequency-thresholds | Two thresholds for classification (default: 0.25 0.75) |
--change-schema | Change types to detect (default: degradation recovery stable) |
--include-sar | Include SAR-based water detection (optional) |
--min-area | Minimum patch area in hectares (default: 0.5) |
--output-dir | Output directory (default: wetland-output) |
| File | Description |
|---|---|
wetland_type.tif | Wetland type classification raster |
inundation_frequency.tif | Inundation frequency raster (0-1) |
wetland_change.geojson | Vectorized change patches with type and confidence |
transition_matrix.csv | Year-to-year transition matrix |
wetland-report.json | Detailed statistics and summary |
output-manifest.json | Machine-readable manifest |
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Argument error |
| 3 | Dependency missing |
| 6 | Validation error |
| 7 | Processing failure |
本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):
python wetland_change_monitor.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir
--bbox W,S,E,N: WGS-84 边界框 (西, 南, 东, 北)--date-range START,END: 日期范围 (YYYY-MM-DD,YYYY-MM-DD)--aoi-file : 替代 --bbox 的 GeoJSON 多边形--cache-dir : 缓存目录 (默认 ~/.geoskill_cache)当用户只给 --bbox + --date-range (没有 --image) 时，skill 自动下载数据。
当用户给 --image 时，走原文件路径 (向后兼容)。
Monitor grassland degradation and recovery trends from multi-temporal vegetation cover, phenology, bare ground and climate baselines. Outputs management zones for restoration prioritization. Use when assessing grassland health, identifying degraded areas, evaluating restoration effectiveness, or generating management recommendations.
Identify and monitor aquaculture ponds from multi-temporal remote sensing. Detects coastal and inland ponds, computes area statistics, tracks expansion/abandonment, and analyzes conversion with wetlands/cropland. Use when mapping aquaculture distribution, monitoring pond dynamics, assessing environmental impacts, or generating pond inventories.
Monitor construction progress from multi-temporal satellite imagery. Classifies project stages (clearing, earthwork, foundation, structure, finishing, completed) using spectral indices and detects stagnation. Use when tracking infrastructure projects, auditing construction timelines, or generating progress reports from remote sensing data.
Monitor forest canopy vitality decline, drought stress, pest damage, or wind throw from multi-temporal spectral indices. Distinguishes short-term fluctuations from persistent decline using historical baselines, persistence state machines, and climate attribution. Use when assessing forest health, detecting anomalies, or planning field sampling.
Detect and classify cropland changes from before/after NDVI rasters. Identifies suspected construction, water, forest, and bare soil changes and generates compliance investigation materials. Use when the user wants to check farmland for unauthorized changes, monitor cropland conversion, or generate compliance reports.
Multi-temporal shoreline change rate analysis. Generates transects, computes Endpoint Rate (EPR) and Linear Regression Rate (LRR), and identifies erosion hotspots. Use when monitoring coastline retreat/accretion or identifying erosion-prone segments.