Geoskill: Forest Fire Burn Severity
Compute forest fire burn severity from pre/post-fire NIR and SWIR imagery using differenced Normalized Burn Ratio (dNBR). Classifies severity into unburned, low, moderate, and high…
ruiduobao
@ruiduobao
Install
$ openclaw skills install @ruiduobao/geoskill-forest-fire-burn-severityForest Fire Burn Severity
Computes dNBR from pre/post-fire NIR+SWIR bands and classifies burn severity.
CLI Usage
python scripts/forest_fire_burn_severity.py \
--pre-nir pre_nir.tif --pre-swir pre_swir.tif \
--post-nir post_nir.tif --post-swir post_swir.tif
Or with synthetic demo data (no real inputs needed):
python scripts/forest_fire_burn_severity.py --synthetic
Parameters
| Flag | Type | Required | Description |
|---|---|---|---|
--pre-nir | path | one-of | Pre-fire NIR band GeoTIFF |
--pre-swir | path | one-of | Pre-fire SWIR band GeoTIFF |
--post-nir | path | one-of | Post-fire NIR band GeoTIFF |
--post-swir | path | one-of | Post-fire SWIR band GeoTIFF |
--synthetic | flag | one-of | Run with synthetic demo data (no real inputs needed) |
--output-dir, -o | path | no | Output directory (default: burn-severity-output) |
--version | flag | no | Show version and exit |
Output
| File | Description |
|---|---|
report.html | Burn severity report |
burn-severity-report.json | Detailed results |
output-manifest.json | Machine-readable manifest |
Exit Codes
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Argument error |
| 7 | Processing failure |
数据下载
本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):
python forest_fire_burn_severity.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)。
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