原始内容
name: forest-health-monitor description: > 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.
Prerequisites / 先准备 X 文件
⚠️ 必读 — 本 skill 不属于即用型，需要先准备特定文件才能跑。
本 skill 需要 bbox + 年份。所有 NDVI/EVI/NDMI/NBR 时序数据自动从 MPC 下载。
👉 完整教程见仓库根目录 PREREQUISITES.md 1.3 节。
先准备 X 文件：--synthetic 一行跑通，验证工作流后再传真实 AOI。
快速试跑命令：
python forest_health_monitor.py --synthetic --output-dir ./test
Forest Health Monitor
Detects forest health anomalies from spectral indices (NDVI, EVI, NDMI, NBR) and distinguishes short-term fluctuations from sustained deterioration using historical baselines, a persistence state machine, and climate attribution.
Trigger
Use when the user wants to:
- Detect forest canopy vitality decline from satellite imagery
- Distinguish drought stress, pest damage, or wind throw from seasonal variation
- Identify persistent decline zones vs. short-term fluctuations
- Correlate forest anomalies with climate variables (SPI/SPEI)
- Generate stratified sampling plans for field verification
- Assess forest health by stand type (evergreen, deciduous, mixed)
CLI Usage
# Basic health monitoring with bounding box
python scripts/forest_health_monitor.py \
--bbox 116.0,39.0,117.0,40.0 \
--forest-type evergreen \
--year 2024
# With AOI file and custom indices
python scripts/forest_health_monitor.py \
--aoi-file forest_aoi.geojson \
--forest-type-file stand_types.geojson \
--start-date 2022-01-01 \
--end-date 2024-12-31 \
--indices ndvi,evi,ndmi,nbr \
--persistence 3 \
--climate-attribution spi
# Full parameter set
python scripts/forest_health_monitor.py \
--aoi-file aoi.geojson \
--forest-type mixed \
--baseline-years 5 \
--indices ndvi,ndmi,nbr \
--persistence 2 \
--climate-attribution spei \
--output-dir ./fhm-output \
--overwrite
# Synthetic demo (no AOI/rasters needed)
python scripts/forest_health_monitor.py --synthetic --output-dir ./fhm-output
Parameters
| Parameter | Default | Description |
|---|---|---|
--aoi-file |
— | AOI boundary (GeoJSON/Shapefile) |
--bbox |
— | Bounding box: xmin,ymin,xmax,ymax |
--place |
— | Named place (requires geocoding) |
--forest-type |
mixed | Forest type: evergreen, deciduous, mixed |
--forest-type-file |
— | Forest type polygons GeoJSON |
--year |
current | Monitoring year |
--start-date |
— | Start date (YYYY-MM-DD) |
--end-date |
— | End date (YYYY-MM-DD) |
--baseline-years |
5 | Years for historical baseline |
--indices |
ndvi,evi,ndmi,nbr | Comma-separated spectral indices |
--persistence |
2 | Persistence threshold (months) |
--climate-attribution |
spi | Climate variable: spi, spei, temperature, precipitation |
--severity-schema |
built-in | Custom severity schema JSON |
--output-dir |
fhm-output | Output directory |
--overwrite |
false | Allow overwriting existing output |
--synthetic |
false | Run with synthetic demo data (auto-generates NDVI/EVI rasters + AOI) |
Output
| File | Description |
|---|---|
forest_health.tif |
Multi-temporal severity classification raster |
persistent_decline.geojson |
Zones with persistent decline |
climate_links.csv |
Climate attribution per zone |
timeseries.parquet |
Full health time series per zone |
sampling_plan.geojson |
Stratified sampling point recommendations |
request.json |
Input request record |
dataset-manifest.json |
Data source manifest |
output-manifest.json |
Output file manifest |
qa.json |
Quality assurance report |
run.log |
Execution log |
Health Severity Levels
| Level | Code | Color | Criteria |
|---|---|---|---|
| Healthy | 0 | 00FF00 | All indices within 1 std of baseline |
| Mild Stress | 1 | FFFF00 | 1+ indices below 1.5 std, or alert state |
| Moderate Decline | 2 | FF9900 | 2+ indices below 1.5 std, decline state |
| Severe Decline | 3 | FF0000 | 2+ indices below 2.0 std, persistent decline |
| Mortality | 4 | 990000 | Extreme decline, absorbing state |
Health State Machine
| State | Description | Transition |
|---|---|---|
| stable | Normal condition | → alert on anomaly |
| alert | Initial anomaly detected | → decline if persistent |
| decline | Sustained deterioration | → recovery if improving |
| recovery | Improving trend | → stable if sustained |
| mortality | Extreme decline (absorbing) | → recovery only with strong evidence |
Key Design Principles
- Stratified baselines: Each forest type (evergreen/deciduous/mixed) has its own phenological baseline. No universal threshold across all forests.
- Multi-index consensus: At least 2 indices must agree before flagging high-confidence anomalies.
- Three independent dimensions: Anomaly (deviation), Persistence (state machine), Attribution (climate correlation) are reported separately.
- Phenology-aware: Deciduous winter NDVI drop is not flagged as disease because the baseline accounts for seasonal amplitude.
Exit Codes
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Argument error |
| 3 | Dependency missing |
| 6 | Data validation failure |
| 7 | Processing failure |
Limitations
- Species and age-class differences affect baseline accuracy
- Pest/disease attribution typically requires field data
- Long-term sensor differences require cross-normalization
- Output is remote sensing analysis support, not regulatory determination
数据下载
本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):
python forest_health_monitor.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir <tmp>
--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 <path.geojson>: 替代 --bbox 的 GeoJSON 多边形--cache-dir <path>: 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)。