红树林制图 | Mangrove Mapping

Fuses multispectral and SAR features to map mangrove distribution in the intertidal zone of tropical/subtropical coasts, and supports multi-temporal change (gain/loss) detection. Suitable for mangrove resource baseline surveys, coastal ecological monitoring and conservation effectiveness assessment.

Four remotely detectable characteristics of mangroves:

• High NDVI: dense evergreen vegetation with high near-infrared reflectance and low red reflectance; NDVI is typically > 0.5.
• NDWI boundary: McFeeters NDWI=(Green−NIR)/(Green+NIR) is used to locate water bodies, and a distance transform yields the distance from each land pixel to the coastline.
• Coastal buffer: mangroves occur only within a certain buffer distance from the coastline (intertidal zone).
• SAR tidal influence: tidal inundation of the intertidal zone causes multiple scattering in tree trunks, making SAR backscatter brighter.

The algorithm converts the above features into [0,1] memberships and fuses them by rules (product + SAR modulation), then thresholding yields the mangrove mask. --synthetic mode generates a physically consistent coastal scene (offline).

Dependencies / 依赖

pip install numpy rasterio scipy

Usage / 使用方法

Basic Usage (synthetic data, offline)

python geoskill-mangrove-mapping.py --bbox 110.0 21.0 111.0 22.0 --synthetic --output-dir ./output

Example 1: multi-temporal change detection

python geoskill-mangrove-mapping.py \
    --bbox 110.0 21.0 111.0 22.0 \
    --synthetic --n-dates 3 \
    --output-dir ./change

Example 2: adjust the fusion threshold

python geoskill-mangrove-mapping.py \
    --bbox 110.0 21.0 111.0 22.0 \
    --synthetic --score-threshold 0.5 \
    --output-dir ./thr

Example 3: real multiband imagery

python geoskill-mangrove-mapping.py \
    --input coastal.tif \
    --output-dir ./real

Input band order: green / red / nir / swir (required), with the 5th band being SAR backscatter (optional; when missing, the algorithm degrades to NDVI + coastal buffer only).

Output / 输出

Data Source / 数据源 / Source

• Synthetic mode: generated locally, no external data source
• Real mode: user-provided multiband GeoTIFF (e.g., coregistered Sentinel-2 + Sentinel-1 products)

Privacy / 隐私声明 / Privacy

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

License / License

MIT

name: geoskill-mangrove-mapping description: '融合 NDVI 高值、NDWI 水陆边界、海岸缓冲与 SAR 潮汐多次散射特征，规则融合提取红树林分布，输出红树林 GeoTIFF、得分栅格、面积统计与多期变化。Maps mangroves by fusing NDVI, NDWI coast buffer and SAR tidal signatures.'

红树林制图 | Mangrove Mapping

融合多光谱与 SAR 特征提取热带/亚热带海岸潮间带的红树林分布，并支持多期 变化（增益/损失）检测。适用于红树林资源本底调查、海岸带生态监测与保护成效评估。

红树林的四个可遥感识别特征：

• NDVI 高值：茂密常绿植被，近红外高反射、红光低反射，NDVI 通常 > 0.5。
• NDWI 边界：用 McFeeters NDWI=(Green−NIR)/(Green+NIR) 定位水体，距离 变换得到每个陆地像元到海岸线的距离。
• 海岸缓冲：红树林只出现在距海岸线一定缓冲范围内（潮间带）。
• SAR 潮汐影响：潮间带淹水使树干产生多次散射，SAR 后向散射偏亮。

算法把上述特征转为 [0,1] 隶属度并规则融合（乘积 + SAR 调制），阈值化得到 红树林掩膜。支持 --synthetic 模式生成物理一致的海岸带场景（离线）。

依赖

pip install numpy rasterio scipy

使用方法

基本用法（合成数据，离线）

python geoskill-mangrove-mapping.py --bbox 110.0 21.0 111.0 22.0 --synthetic --output-dir ./output

示例 1：多期变化检测

python geoskill-mangrove-mapping.py \
    --bbox 110.0 21.0 111.0 22.0 \
    --synthetic --n-dates 3 \
    --output-dir ./change

示例 2：调整融合阈值

python geoskill-mangrove-mapping.py \
    --bbox 110.0 21.0 111.0 22.0 \
    --synthetic --score-threshold 0.5 \
    --output-dir ./thr

示例 3：真实多波段影像

python geoskill-mangrove-mapping.py \
    --input coastal.tif \
    --output-dir ./real

输入波段顺序：green / red / nir / swir（必需），第 5 波段为 SAR 后向散射（可选， 缺失时退化为仅 NDVI+海岸缓冲）。

输出

数据源 / Source

• 合成模式：本地生成，无外部数据源
• 真实模式：用户提供多波段 GeoTIFF（如 Sentinel-2 + Sentinel-1 配准产品）

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