光污染评估 | Light Pollution Assessment
Light pollution grade thresholds (0.25/1/4/15/50 nW·cm⁻²·sr⁻¹) follow the global light pollution classes of Falchi et al. (2016): 0 = pristine dark sky, 5 = extreme light pollution. The ecological impact index uses a logarithmic response I = log10(1+k·R)/log10(1+k·Rmax) normalized to [0,1], characterizing the disruption of artificial light on nocturnal organism rhythms; the skyglow proxy = radiance × scattering coefficient.
Use cases: dark-sky reserve delineation, ecological light-environment assessment, and urban lighting planning.
Dependencies / 依赖
pip install numpy rasterio
Usage / 使用方法
Example 1: synthetic urban radiance gradient
python geoskill-light-pollution-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./output
Example 2: real VIIRS annual mean radiance raster
python geoskill-light-pollution-assessment.py --input viirs_annual.tif --output-dir ./real
Example 3: a different region
python geoskill-light-pollution-assessment.py --bbox 121 31 122 32 --synthetic --output-dir ./shanghai
Example 4: quick validation on a tiny area
python geoskill-light-pollution-assessment.py --bbox 116.39 39.90 116.40 39.91 --synthetic --output-dir ./tiny
Example 5: silent batch run
python geoskill-light-pollution-assessment.py --bbox 113 23 114 24 --synthetic --quiet --output-dir ./batch
Output / 输出
| File | Format | Description |
|---|---|---|
light_pollution_grade.tif |
GeoTIFF (float32) | Light pollution grade 0-5 |
ecological_impact.tif |
GeoTIFF (float32) | Ecological impact index ∈ [0,1] |
skyglow_proxy.tif |
GeoTIFF (float32) | Skyglow proxy |
light_pollution_params.json |
JSON | Threshold table, per-grade pixel counts, radiance statistics |
output-manifest.json |
JSON | Run manifest (input/output/QA/software versions) |
Data Source / 数据源 / Source
Local VIIRS night-time lights GeoTIFF (nW·cm⁻²·sr⁻¹); grading thresholds follow Falchi et al. 2016 (Science Advances, open access); synthetic mode generates an urban radiance gradient locally with no external data source.
Privacy / 隐私声明 / Privacy
- Runs fully offline by default; makes no network requests
--syntheticmode reads no external data- All computation is done locally; no user data is uploaded
License / License
MIT
name: geoskill-light-pollution-assessment description: '由 VIIRS 夜光辐射值按生态阈值分 0-5 级光污染等级，对数响应模型估算生态影响指数，附天空辉光代理。Assesses light pollution grades from VIIRS night-time lights and ecological thresholds. 输出等级/生态影响/天空辉光三张 GeoTIFF。'
光污染评估 | Light Pollution Assessment
光污染等级阈值（0.25/1/4/15/50 nW·cm⁻²·sr⁻¹）参考 Falchi et al. (2016) 全球光污染分级：0=原始暗夜，5=极端光污染。生态影响指数用对数响应 I = log10(1+k·R)/log10(1+k·Rmax) 归一化到 [0,1]，刻画人造光对夜行生物节律的干扰；天空辉光代理 = 辐射值×散射系数。
适用场景：暗夜保护区划定、生态光环境评估、城市照明规划。
依赖
pip install numpy rasterio
使用方法
示例 1：合成城市梯度夜光
python geoskill-light-pollution-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./output
示例 2：真实 VIIRS 年平均辐射栅格
python geoskill-light-pollution-assessment.py --input viirs_annual.tif --output-dir ./real
示例 3：不同区域
python geoskill-light-pollution-assessment.py --bbox 121 31 122 32 --synthetic --output-dir ./shanghai
示例 4：极小区域快速验证
python geoskill-light-pollution-assessment.py --bbox 116.39 39.90 116.40 39.91 --synthetic --output-dir ./tiny
示例 5：静默批量
python geoskill-light-pollution-assessment.py --bbox 113 23 114 24 --synthetic --quiet --output-dir ./batch
输出
| 文件 | 格式 | 说明 |
|---|---|---|
light_pollution_grade.tif |
GeoTIFF (float32) | 光污染等级 0-5 |
ecological_impact.tif |
GeoTIFF (float32) | 生态影响指数 ∈ [0,1] |
skyglow_proxy.tif |
GeoTIFF (float32) | 天空辉光代理 |
light_pollution_params.json |
JSON | 阈值表、等级像元计数、辐射统计 |
output-manifest.json |
JSON | 运行清单（输入/输出/QA/软件版本） |
数据源 / Source
本地 VIIRS 夜光 GeoTIFF（nW·cm⁻²·sr⁻¹）；分级阈值参考 Falchi et al. 2016（Science Advances, 开放获取）；合成模式本地生成城市梯度夜光，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