Detect forest disturbance from multi-temporal NDVI. Use when the user wants to analyze changes, detect hazards, or generate assessment reports.
1 次安装
编程
Geoskill: Forest Health Monitor
试用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.
它能做什么
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.
技能文档
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
--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)。
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