geoskill-glacier-retreat-monitoring

用 NDSI 提取多期冰川范围并矢量化为边界多边形，分析冰川末端位置后退与面积变化曲线，输出边界 GeoJSON 序列、退缩速率与面积 JSON。Monitors glacier retreat from multi-temporal NDSI boundaries.

冰川退缩监测 | Glacier Retreat Monitoring

Extracts multi-temporal glacier extents with the Normalized Difference Snow Index NDSI=(Green−SWIR)/(Green+SWIR) (NDSI>0.4 = snow/ice), vectorizes each period's glacier raster into boundary polygons, and analyzes terminus retreat and area change along the time series. Suitable for monitoring alpine glacier response to climate change and updating glacier inventories.

Core algorithm:

• NDSI extraction: NDSI>0.4 identifies snow/ice; the threshold is adjustable.
• Boundary vectorization: contour tracing converts the binary mask into shapely polygons, which are projected to EPSG:4326, aggregated into a geopandas.GeoDataFrame, and exported as GeoJSON.
• Area change curve: glacier area per period (pixel count × pixel area).
• Terminus position and retreat rate: the row coordinate of the glacier centroid represents the terminus position; a decreasing row index indicates retreat to higher elevation; displacement between adjacent periods (m) / time interval gives the retreat rate.

--synthetic mode generates a physically consistent valley scene with glacier retreat (offline).

Dependencies / 依赖

bash

pip install numpy rasterio scipy geopandas shapely matplotlib

Usage / 使用方法

Basic usage (synthetic data, offline)

bash

python geoskill-glacier-retreat-monitoring.py --bbox 86.0 28.0 87.0 29.0 --synthetic --n-dates 3 --output-dir ./output

Example 1: multi-period retreat analysis

bash

python geoskill-glacier-retreat-monitoring.py \
    --bbox 86.0 28.0 87.0 29.0 \
    --synthetic --n-dates 5 --years-per-step 2 \
    --output-dir ./retreat

Example 2: adjust the NDSI threshold

bash

python geoskill-glacier-retreat-monitoring.py \
    --bbox 86.0 28.0 87.0 29.0 \
    --synthetic --ndsi-threshold 0.5 \
    --output-dir ./thr

Example 3: real multi-temporal image cube

bash

python geoskill-glacier-retreat-monitoring.py \
    --input glacier_cube.tif \
    --n-dates 3 \
    --output-dir ./real

Input cube convention: shape (n_dates, 2, H, W); the two bands of each period are green / swir.

Output / 输出

File	Format	Description
glacier_boundaries.geojson	GeoJSON	Multi-period glacier boundary polygons (with date_index / area)
glacier_last.tif	GeoTIFF (float32)	Final-period glacier mask, EPSG:4326
glacier_retreat.json	JSON	Area curve, terminus row series, retreat rate, polygon summary
output-manifest.json	JSON	Run manifest (inputs/outputs/QA/software versions)

Data Source / 数据源 / Source

• Synthetic mode: locally generated; no external data source
• Real mode: user-provided multi-temporal green/swir cube (e.g. Landsat / Sentinel-2)

Privacy / 隐私声明 / Privacy

• Fully offline by default; no network requests are made
• All computation is performed locally; user data is never uploaded

License / License

MIT

---

---

name: geoskill-glacier-retreat-monitoring description: '用 NDSI 提取多期冰川范围并矢量化为边界多边形，分析冰川末端位置后退与面积变化曲线，输出边界 GeoJSON 序列、退缩速率与面积 JSON。Monitors glacier retreat from multi-temporal NDSI boundaries.'

冰川退缩监测 | Glacier Retreat Monitoring

用归一化差分雪指数 NDSI=(Green−SWIR)/(Green+SWIR) 提取多期冰川范围 （NDSI>0.4 为雪/冰），把每期冰川栅格矢量化为边界多边形，并沿时间序列分析 末端后退与面积变化。适用于高山冰川对气候变化的响应监测与编目更新。

核心算法：

• NDSI 提取：NDSI>0.4 判识雪/冰，阈值可调。
• 边界矢量化：用等高线追踪把二值掩膜转为 shapely 多边形，投影到 EPSG:4326， 汇总为 geopandas.GeoDataFrame 并导出 GeoJSON。
• 面积变化曲线：每期冰川面积（像元计数 × 像元面积）。
• 末端位置与退缩速率：用冰川质心行坐标代表末端位置，行号减小说明向高 海拔后退；相邻期位移（米）/ 时间间隔即退缩速率。

支持 --synthetic 模式生成含冰川后退的物理一致山谷场景（离线）。

依赖

bash

pip install numpy rasterio scipy geopandas shapely matplotlib

使用方法

基本用法（合成数据，离线）

bash

python geoskill-glacier-retreat-monitoring.py --bbox 86.0 28.0 87.0 29.0 --synthetic --n-dates 3 --output-dir ./output

示例 1：多期退缩分析

bash

python geoskill-glacier-retreat-monitoring.py \
    --bbox 86.0 28.0 87.0 29.0 \
    --synthetic --n-dates 5 --years-per-step 2 \
    --output-dir ./retreat

示例 2：调整 NDSI 阈值

bash

python geoskill-glacier-retreat-monitoring.py \
    --bbox 86.0 28.0 87.0 29.0 \
    --synthetic --ndsi-threshold 0.5 \
    --output-dir ./thr

示例 3：真实多期影像立方体

bash

python geoskill-glacier-retreat-monitoring.py \
    --input glacier_cube.tif \
    --n-dates 3 \
    --output-dir ./real

输入立方体约定：形状 (n_dates, 2, H, W)，每个时期的两个波段为 green / swir。

输出

文件	格式	说明
glacier_boundaries.geojson	GeoJSON	多期冰川边界多边形（含 date_index / area）
glacier_last.tif	GeoTIFF (float32)	末期冰川掩膜，EPSG:4326
glacier_retreat.json	JSON	面积曲线、末端行序列、退缩速率、多边形汇总
output-manifest.json	JSON	运行清单（输入/输出/QA/软件版本）

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 green/swir 立方体（如 Landsat / Sentinel-2）

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