geoskill-biodiversity-mapping

生物多样性制图 | Biodiversity Mapping

Vegetation productivity (NDVI), habitat structural diversity (local standard deviation of NDVI), and terrain heterogeneity (gradient magnitude of the DEM) are the most commonly used remote sensing proxies for biodiversity. This skill normalizes each of the three proxies individually and then fuses them with weights into a "habitat quality" index, which is mapped to relative species richness via the saturating curve S = Smax·(1 − exp(−k·q)) to avoid linear extrapolation.

Applicable scenarios: protected area siting, ecological baseline surveys, and identification of priority zones for biodiversity conservation. Supports two weighting schemes: heterogeneity (emphasizing structural/terrain diversity) and productivity (emphasizing energy availability).

Dependencies / 依赖

pip install numpy rasterio scipy

Usage / 使用方法

Example 1: Synthetic Data, Offline Run

python geoskill-biodiversity-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./output

Example 2: Real Multispectral Imagery (band1=Red, band2=NIR, band3=DEM)

python geoskill-biodiversity-mapping.py --input scene.tif --method heterogeneity --output-dir ./real

Example 3: Productivity-Dominant Mode

python geoskill-biodiversity-mapping.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method productivity --output-dir ./prod

Example 4: Larger Texture Window + Higher Saturation Cap

python geoskill-biodiversity-mapping.py --bbox 116 39 117 40 --synthetic --window 9 --s-max 300 --k 2.5 --output-dir ./tuned

Example 5: Quiet Batch Mode

python geoskill-biodiversity-mapping.py --bbox 113 23 114 24 --synthetic --quiet --output-dir ./batch

Output / 输出

Data Source / 数据源 / Source

Local multispectral GeoTIFF (band1=red, band2=NIR, band3=optional DEM); synthetic mode generates vegetation/water/bare-soil scenes and a rugged DEM locally, with no external data source.

Privacy / 隐私声明 / Privacy

• Runs fully offline by default; makes no network requests
• --synthetic mode reads no external data
• All computation is performed locally; no user data is uploaded

License / License

MIT

---

---

name: geoskill-biodiversity-mapping description: '基于生境异质性假说，用 NDVI 生产力、纹理结构异质性与地形粗糙度三类代理估算物种丰富度空间分布。Maps species richness proxies from NDVI, texture and terrain heterogeneity. 输出物种丰富度与生境质量 GeoTIFF + 参数 JSON。'

生物多样性制图 | Biodiversity Mapping

植被生产力（NDVI）、生境结构多样性（NDVI 局部标准差）与地形异质性（DEM 梯度模）是生物多样性最常用的遥感代理量。本 skill 将三者各自归一化后加权融合为「生境质量」，再经饱和曲线 S = Smax·(1 - exp(-k·q)) 映射为相对物种丰富度，避免线性外推。

适用场景：保护区选址、生态本底调查、生物多样性保护优先区识别。支持 heterogeneity（强调结构/地形多样性）与 productivity（强调能量可得性）两种加权方案。

依赖

pip install numpy rasterio scipy

使用方法

示例 1：合成数据离线运行

python geoskill-biodiversity-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./output

示例 2：真实多光谱影像（band1=红, band2=近红外, band3=DEM）

python geoskill-biodiversity-mapping.py --input scene.tif --method heterogeneity --output-dir ./real

示例 3：生产力主导模式

python geoskill-biodiversity-mapping.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method productivity --output-dir ./prod

示例 4：加大纹理窗口 + 调高饱和上限

python geoskill-biodiversity-mapping.py --bbox 116 39 117 40 --synthetic --window 9 --s-max 300 --k 2.5 --output-dir ./tuned

示例 5：静默批量模式

python geoskill-biodiversity-mapping.py --bbox 113 23 114 24 --synthetic --quiet --output-dir ./batch

输出

数据源 / Source

本地多光谱 GeoTIFF（band1=红、band2=近红外、band3 可选 DEM）；合成模式本地生成植被/水体/裸土场景与起伏 DEM，无外部数据源。

隐私声明 / Privacy

• 默认完全离线运行，不发起任何网络请求
• --synthetic 模式不读取任何外部数据
• 所有计算在本地完成，不上传用户数据

License

MIT