荒漠化监测 | Desertification Monitoring

Fuses multi-epoch NDVI trend, albedo, and vegetation scarcity to score and grade desertification severity (stable / mild / moderate / severe). Suitable for long time-series monitoring of land degradation in arid and semi-arid regions, identifying degradation hotspots, and evaluating restoration effectiveness.

Core algorithm:

• NDVI trend: estimates the slope of the NDVI time series for each pixel, supporting Sen's slope (robust median slope, resistant to outliers) and least-squares linear regression. A negative slope indicates vegetation degradation.
• Albedo: mean of the visible-light bands. Bare soil / desert has high albedo, while vegetated areas are low.
• Vegetation scarcity: reflected by mean NDVI; low NDVI indicates sparse vegetation or bare ground.
• Fusion score: score = 0.4×scarcity + 0.35×bare + 0.25×decline, thresholded into four levels.

--synthetic mode generates a physically consistent simulated sequence with degradation trends, allowing the full workflow to be validated without network access or real data.

Dependencies / 依赖

pip install numpy rasterio scipy

Usage / 使用方法

Basic Usage (synthetic data, offline)

python geoskill-desertification-monitoring.py --bbox 100.0 40.0 101.0 41.0 --synthetic --n-dates 6 --output-dir ./output

Example 1: Sen's slope trend estimation

python geoskill-desertification-monitoring.py \
    --bbox 100.0 40.0 101.0 41.0 \
    --synthetic --n-dates 6 --method sens \
    --output-dir ./sens

Example 2: least-squares linear trend

python geoskill-desertification-monitoring.py \
    --bbox 100.0 40.0 101.0 41.0 \
    --synthetic --n-dates 6 --method linear \
    --output-dir ./linear

Example 3: real multi-epoch NDVI raster

python geoskill-desertification-monitoring.py \
    --input ndvi_series.tif \
    --n-dates 6 --method sens \
    --output-dir ./real

Input band convention: the first n-dates bands are the NDVI of each epoch; if one extra band is present (n-dates+1 in total), the last band is treated as albedo; otherwise (1 − mean_ndvi) is used as an albedo proxy.

Output / 输出

Data Source / 数据源 / Source

• Synthetic mode: generated locally, no external data source
• Real mode: user provides multi-epoch NDVI GeoTIFF (e.g., MODIS MOD13 / Landsat NDVI time series)

Privacy / 隐私声明 / Privacy

• Runs fully offline by default and makes no network requests
• All computation is done locally; no user data is uploaded

License / License

MIT

name: geoskill-desertification-monitoring description: '融合 NDVI 趋势（Sen 斜率/线性回归）、反照率与植被稀缺度，综合评分分级荒漠化（稳定/轻/中/重度），输出等级与趋势栅格、面积统计。Fuses NDVI trend (Sen/linear slope), albedo and vegetation scarcity to grade desertification.'

荒漠化监测 | Desertification Monitoring

融合多期 NDVI 趋势、反照率（albedo）与植被稀缺度，对荒漠化程度综合评分并 分级（稳定 / 轻度 / 中度 / 重度）。适用于干旱-半干旱区土地退化的长时序监测、 退化热点识别与治理成效评估。

核心算法：

• NDVI 趋势：对每个像元的 NDVI 时间序列估计斜率，支持 Sen's slope（稳健 中位数斜率，抗异常值）与最小二乘线性回归。负斜率指示植被退化。
• 反照率：可见光波段均值。裸土/沙漠反照率高，植被覆盖区低。
• 植被稀缺度：由平均 NDVI 反映，低 NDVI 指示稀疏植被或裸地。
• 融合评分：score = 0.4×scarcity + 0.35×bare + 0.25×decline，阈值化为四级。

支持 --synthetic 模式生成含退化趋势的物理一致模拟序列，无需网络和真实数据 即可验证全流程。

依赖

pip install numpy rasterio scipy

使用方法

基本用法（合成数据，离线）

python geoskill-desertification-monitoring.py --bbox 100.0 40.0 101.0 41.0 --synthetic --n-dates 6 --output-dir ./output

示例 1：Sen's slope 趋势估计

python geoskill-desertification-monitoring.py \
    --bbox 100.0 40.0 101.0 41.0 \
    --synthetic --n-dates 6 --method sens \
    --output-dir ./sens

示例 2：最小二乘线性趋势

python geoskill-desertification-monitoring.py \
    --bbox 100.0 40.0 101.0 41.0 \
    --synthetic --n-dates 6 --method linear \
    --output-dir ./linear

示例 3：真实多期 NDVI 栅格

python geoskill-desertification-monitoring.py \
    --input ndvi_series.tif \
    --n-dates 6 --method sens \
    --output-dir ./real

输入波段约定：前 n-dates 个波段为各期 NDVI；若再多一个波段（共 n-dates+1）， 末波段被视为反照率，否则用 (1 − mean_ndvi) 作为反照率代理。

输出

数据源 / Source

• 合成模式：本地生成，无外部数据源
• 真实模式：用户提供多期 NDVI GeoTIFF（如 MODIS MOD13 / Landsat NDVI 时序）

隐私声明 / Privacy

• 默认完全离线运行，不发起任何网络请求
• 所有计算在本地完成，不上传用户数据

License

MIT