森林覆盖变化检测 | Forest Cover Change Detection
Detects forest loss / gain / stable from multi-temporal NDVI. For each period, an NDVI threshold (default 0.3) classifies forest pixels; comparing the first and last periods, a pixel changing from forest to non-forest with a drop ≥ the drop threshold is recorded as loss, a pixel changing from non-forest to forest with an increase ≥ the gain threshold is recorded as gain, and everything else is recorded as stable. The change vector magnitude (CVA, the L2 norm of the adjacent-period difference) is also computed to quantify change intensity.
Typical applications: deforestation monitoring, afforestation effectiveness assessment, and annual forest resource change ledgers. In synthetic mode, logging (loss) and afforestation (gain) are injected into defined areas; the detection results match the injected mask pixel-by-pixel, and CVA intensity in the loss areas is significantly higher than in stable areas, enabling offline validation.
Dependencies / 依赖
pip install 'numpy' 'rasterio' 'scipy'
Usage / 使用方法
Basic usage
python geoskill-forest-cover-change.py --bbox 116.0 39.0 117.0 40.0 --n-dates 4
Example 1 (synthetic data, offline)
python geoskill-forest-cover-change.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Example 2 (read multi-temporal NDVI rasters)
python geoskill-forest-cover-change.py --input ndvi_series.tif --threshold 0.3 --output-dir ./out
Example 3 (stricter loss criterion)
python geoskill-forest-cover-change.py --bbox 116 39 117 40 --synthetic --drop-threshold 0.2 --output-dir ./out
Example 4 (custom years + silence)
python geoskill-forest-cover-change.py --bbox 121 31 122 32 --synthetic --start-year 2001 --interval-years 3 --quiet --output-dir ./out
Output / 输出
| File | Format | Description |
|---|---|---|
forest_change_class.tif |
GeoTIFF (int32) | 0=stable, 1=loss, 2=gain |
cva_magnitude.tif |
GeoTIFF (float32) | Change vector magnitude |
area_stats.json |
JSON | Per-class area + per-period forest area series |
output-manifest.json |
JSON | Run manifest |
Data Source / 数据源 / Source
- Local multi-band NDVI GeoTIFF (one period per band);
--syntheticsynthesizes an NDVI series offline (no network, no account required).
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network at all. - All processing is performed locally; user data is never uploaded.
License / License
MIT
name: geoskill-forest-cover-change description: '多期 NDVI 阈值与变化矢量幅度检测森林损失/增益/稳定，输出变化栅格与年度面积统计'
森林覆盖变化检测 | Forest Cover Change Detection
从多期 NDVI 检测森林覆盖的损失 / 增益 / 稳定。逐期用 NDVI 阈值（默认 0.3） 判定森林像元，比较首末期：由森林降为非森林且降幅 ≥ drop 阈值记为损失， 由非森林升为森林且升幅 ≥ gain 阈值记为增益，其余为稳定。同时计算 变化矢量幅度（CVA，相邻期差分的 L2 范数）量化变化强度。
典型应用：毁林监测、造林成效评估、森林资源年度变化台账。合成模式在 确定区域注入砍伐（损失）与造林（增益），检测结果与注入掩膜逐像元一致， 且损失区 CVA 强度显著高于稳定区，便于离线验证。
依赖
pip install 'numpy' 'rasterio' 'scipy'
使用方法
基本用法
python geoskill-forest-cover-change.py --bbox 116.0 39.0 117.0 40.0 --n-dates 4
示例 1（合成数据，离线）
python geoskill-forest-cover-change.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2（读取多期 NDVI 栅格）
python geoskill-forest-cover-change.py --input ndvi_series.tif --threshold 0.3 --output-dir ./out
示例 3（更严格的损失判定）
python geoskill-forest-cover-change.py --bbox 116 39 117 40 --synthetic --drop-threshold 0.2 --output-dir ./out
示例 4（自定义年份 + 静默）
python geoskill-forest-cover-change.py --bbox 121 31 122 32 --synthetic --start-year 2001 --interval-years 3 --quiet --output-dir ./out
输出
| 文件 | 格式 | 说明 |
|---|---|---|
forest_change_class.tif |
GeoTIFF (int32) | 0=稳定, 1=损失, 2=增益 |
cva_magnitude.tif |
GeoTIFF (float32) | 变化矢量幅度 |
area_stats.json |
JSON | 逐类面积 + 逐期森林面积序列 |
output-manifest.json |
JSON | 运行清单 |
数据源 / Source
- 本地多波段 NDVI GeoTIFF（每波段一期）；
--synthetic离线合成 NDVI 序列（无需网络、无需账号）。
隐私声明 / Privacy
- 默认离线运行，
--synthetic模式完全无网络。 - 所有处理在本地完成，不上传用户数据。
License
MIT