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。
编程
geoskill-green-infrastructure-mapping
试用Map green infrastructure from high-resolution NDVI, tree crown detection, green-space classification and patch connectivity.
它能做什么
Map green infrastructure from high-resolution NDVI, tree crown detection, green-space classification and patch connectivity.
技能文档
绿色基础设施制图 | Green Infrastructure Mapping
Maps green infrastructure (green spaces, trees) from high-resolution multispectral imagery to support ecological network assessment and green-space planning.
Core algorithm: NDVI = (NIR−Red)/(NIR+Red) with threshold segmentation of green spaces; local-maxima detection on the NDVI field counts tree-crown candidates; connectivity index = largest connected patch area / total green-space area ∈ [0,1], where higher values indicate better-connected green spaces. A single connected block yields 1, while fragmented patterns yield values below 1.
Dependencies / 依赖
pip install 'numpy' 'rasterio' 'scipy'
Usage / 使用方法
Basic Usage
python geoskill-green-infrastructure-mapping.py --bbox 116.0 39.0 117.0 40.0 [other options]
Examples
Example 1 (Synthetic Data (Offline))
python geoskill-green-infrastructure-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Example 2 (Usage 2)
python geoskill-green-infrastructure-mapping.py --input multispectral.tif --output-dir ./out
Example 3 (Usage 3)
python geoskill-green-infrastructure-mapping.py --bbox 121.0 31.0 122.0 32.0 --ndvi-threshold 0.35 --output-dir ./out --quiet
Example 4 (Usage 4)
python geoskill-green-infrastructure-mapping.py --input ms.tif --ndvi-threshold 0.25 --output-dir ./out
Example 5 (Usage 5)
python geoskill-green-infrastructure-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out --quiet
Output / 输出
| File | Format | Description |
|---|---|---|
green_infrastructure.tif | GeoTIFF | Two bands: band1=NDVI, band2=green-space mask |
green_stats.json | JSON | Mean NDVI, green-space fraction, tree count, connectivity index, patch count |
output-manifest.json | JSON | Run manifest |
Data Source / 数据源 / Source
Local multispectral GeoTIFF (Red, NIR); --synthetic mode simulates a scene of connected green spaces, scattered tree crowns, and an impervious background.
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network at all. - All processing is done locally; user data is never uploaded.
License / License
MIT
name: geoskill-green-infrastructure-mapping description: 'Map green infrastructure from high-resolution NDVI, tree crown detection, green-space classification and patch connectivity.'
绿色基础设施制图 | Green Infrastructure Mapping
从高分辨率多光谱影像制图绿色基础设施（绿地、树木），服务于生态网络评估与绿地规划。
核心算法：NDVI = (NIR−Red)/(NIR+Red)，阈值分割绿地；对 NDVI 场做局部极大值检测统计树冠候选数；连通性指数 = 最大连通斑块面积/总绿地面积 ∈ [0,1]，值越高表示绿地越连通。单一连通块为 1，碎片化小于 1。
依赖
pip install 'numpy' 'rasterio' 'scipy'
使用方法
基本用法
python geoskill-green-infrastructure-mapping.py --bbox 116.0 39.0 117.0 40.0 [其他参数]
示例
示例 1（合成数据（离线））
python geoskill-green-infrastructure-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2（用法 2）
python geoskill-green-infrastructure-mapping.py --input multispectral.tif --output-dir ./out
示例 3（用法 3）
python geoskill-green-infrastructure-mapping.py --bbox 121.0 31.0 122.0 32.0 --ndvi-threshold 0.35 --output-dir ./out --quiet
示例 4（用法 4）
python geoskill-green-infrastructure-mapping.py --input ms.tif --ndvi-threshold 0.25 --output-dir ./out
示例 5（用法 5）
python geoskill-green-infrastructure-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out --quiet
输出
| 文件 | 格式 | 说明 |
|---|---|---|
green_infrastructure.tif | GeoTIFF | 双波段：band1=NDVI，band2=绿地掩膜 |
green_stats.json | JSON | 平均 NDVI、绿地比例、树木数、连通性指数、斑块数 |
output-manifest.json | JSON | 运行清单 |
数据源 / Source
本地多光谱 GeoTIFF（Red, NIR）；--synthetic 模式模拟连通绿地 + 散布树冠 + 不透水背景的场景。
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
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