Coding
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.
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.
Identifies grassland degradation and recovery trends from multi-temporal remote sensing data. Separates climate-driven from management-driven vegetation change and outputs management zones with restoration priorities.
Use when the user wants to:
# Basic analysis with default parameters (10 years, Theil-Sen, climate normalized)
python scripts/grassland_degradation_monitor.py --output-dir ./gdm-output
# Custom years and trend method
python scripts/grassland_degradation_monitor.py \
--years 15 \
--trend-method ols \
--output-dir ./gdm-output
# Without climate normalization (raw trend only)
python scripts/grassland_degradation_monitor.py \
--years 10 \
--no-climate-normalize \
--output-dir ./gdm-output
# With custom degradation schema
python scripts/grassland_degradation_monitor.py \
--years 10 \
--degradation-schema references/degradation_schema.json \
--output-dir ./gdm-output
| Parameter | Default | Description |
|---|---|---|
--input-ndvi | None | Input NDVI time series GeoTIFF (multi-band, optional) |
--years | 10 | Number of years for analysis |
--trend-method | theil-sen | Trend estimation: theil-sen, ols, mann-kendall |
--climate-normalize | True | Apply climate normalization (residual trend) |
--no-climate-normalize | - | Disable climate normalization |
--degradation-schema | built-in | Custom degradation schema JSON |
--output-dir | ./gdm-output | Output directory |
| File | Description |
|---|---|
degradation_status.tif | Raster map of degradation/recovery status codes |
trend.tif | Raster map of trend slopes |
priority_areas.geojson | Point features for priority restoration areas |
management_summary.csv | Area statistics and recommendations per class |
timeseries.csv | Mean NDVI, precipitation, temperature per year |
request.json | Analysis request metadata |
output-manifest.json | Output file inventory and area statistics |
qa.json | Quality assurance checks |
| Code | Name | Description | Recommendation |
|---|---|---|---|
| 3 | severe_degradation | NDVI declining >0.02/yr, 3+ years | Immediate restoration |
| 2 | moderate_degradation | NDVI declining 0.01-0.02/yr, 3+ years | Priority restoration |
| 1 | light_degradation | NDVI declining 0.005-0.01/yr, 3+ years | Preventive management |
| 0 | stable | No significant trend | Sustainable use |
| -1 | light_recovery | NDVI increasing 0.005-0.01/yr, 2+ years | Monitor and maintain |
| -2 | moderate_recovery | NDVI increasing 0.01-0.02/yr, 2+ years | Continue current practices |
| -3 | significant_recovery | NDVI increasing >0.02/yr, 2+ years | Success case, replicate |
Regresses NDVI against precipitation and temperature anomalies, then computes the trend of residuals. This separates climate-driven variation from management-driven change.
Combines three factors:
Before-After-Control-Impact design for management effectiveness:
BACI = (treat_after - treat_before) - (ctrl_after - ctrl_before)
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Argument error |
| 3 | Dependency missing |
| 6 | Data validation failure |
| 7 | Processing failure |
本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):
python grassland_degradation_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 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.
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Assess crop health from NDVI time series. Use when the user wants to analyze changes, detect hazards, or generate assessment reports.
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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.