Geoskill: Urban Heat Analysis

duplicate of@ruiduobao/urban-heat-analysis

Calculate Urban Heat Island intensity from MODIS LST GeoTIFFs, classify heat levels, perform temporal analysis, and output UHI maps with statistics.

ruiduobao@ruiduobao

Install

openclaw skills install @ruiduobao/geoskill-urban-heat-analysis

urban-heat-analysis

Calculate Urban Heat Island (UHI) intensity from MODIS Land Surface Temperature (LST) GeoTIFF data. Supports UHI classification, temporal analysis, and map output.

Features

UHI Intensity: T_urban - T_rural_reference
Auto Rural Reference: Uses coolest N% of pixels when no mask provided
Custom Rural Mask: Accepts user-defined rural reference mask
Heat Island Classification: Strong / Moderate / Weak / None
Temporal Analysis: Seasonal UHI patterns from multiple LST images
GeoTIFF Output: UHI intensity and classification maps
Statistics: Mean, max, min, classification percentages

Usage

bash

# Compute UHI from single LST image
python scripts\urban-heat-analysis.py analyze --lst MOD11A1.tif --output uhi.tif

# With rural reference mask
python scripts\urban-heat-analysis.py analyze --lst lst.tif --rural-mask rural.tif --output uhi.tif

# Classify UHI intensity map
python scripts\urban-heat-analysis.py classify --uhi-tif uhi.tif --output classified.tif

# Temporal analysis across multiple images
python scripts\urban-heat-analysis.py temporal --lst-dir ./lst_data/ --output seasonal.json

Parameters

Parameter	Description	Default
`--lst`	LST GeoTIFF file path	Required
`--rural-mask`	Rural reference mask (1=rural)	None
`--rural-fraction`	Fraction of coolest pixels as rural ref	0.1
`--uhi-tif`	UHI intensity GeoTIFF (classify mode)	Required
`--lst-dir`	Directory of LST files (temporal mode)	Required
`--output`	Output file path	Auto-generated

Installation

bash

pip install requests>=2.28.0 tqdm numpy scipy rasterio
# Or: pip install -r scripts/requirements.txt

Dependencies

Package	Purpose
`rasterio`	GeoTIFF I/O and CRS handling
`numpy`	Array computation and statistics
`requests`	Data download (if applicable)
`tqdm`	Progress bars

Data Source

MODIS LST (MOD11A1/MYD11A1) — NASA EOSDIS, Public Domain
Scale factor: value × 0.02 = Kelvin
Supports both Terra (MOD) and Aqua (MYD) products

Data Acquisition

Obtain MODIS LST data from:

NASA Earthdata Search (https://search.earthdata.nasa.gov/) — search for MOD11A1 or MYD11A1
NASA LAADS DAAC (https://ladsweb.modaps.eosdis.nasa.gov/) — if downloading the LST GeoTIFFs yourself

Note: this skill itself does not require Earthdata login — it consumes local LST GeoTIFFs (any source) and computes UHI intensity. To download MODIS LST products, register a free account at https://urs.earthdata.nasa.gov/ and use the modis-lst-download skill.

Nodata Handling

Nodata pixels (QC-flagged or fill values) are automatically excluded from UHI computation. The output GeoTIFF uses nodata=-9999. When using auto rural reference, nodata pixels are not considered in the coolest-N% calculation.

CRS Requirements

All input rasters should be in the same CRS. If combining Terra and Aqua images, ensure they share the same projection and spatial extent. Reproject with gdalwarp if needed:

bash

gdalwarp -t_srs EPSG:4326 input.tif reprojected.tif

Cloud Contamination Handling

Use the QC band (QC_Day or QC_Night) to filter cloud-contaminated pixels. Pixels with QC flags indicating cloud cover should be masked out before UHI analysis:

bash

python scripts\urban-heat-analysis.py analyze --lst MOD11A1.tif --qc-band qc.tif --output uhi.tif

If QC band is not available, consider using only clear-sky quality flags (QC == 0).

Temporal Analysis Date Logic

For temporal analysis, dates are extracted from filenames using the MODIS naming convention (e.g., MOD11A1.A2023001.h27v05.061.2023002010234.hdf). Alternatively, use --date-format to specify a custom pattern:

bash

python scripts\urban-heat-analysis.py temporal --lst-dir ./lst_data/ --date-format "%Y%m%d" --output seasonal.json

Output GeoTIFF Structure

Property	Value
Data type	`float32`
Bands	1 (UHI intensity in °C)
NoData	-9999
CRS	Same as input

Custom UHI Thresholds

Default classification uses fixed thresholds. To customize:

bash

python scripts\urban-heat-analysis.py classify --uhi-tif uhi.tif --thresholds "1.0,2.5,4.0" --output classified.tif

The 3 values define boundaries between: none / weak / moderate / strong UHI.

CSV Export for Statistics

Export classification statistics to CSV:

bash

python scripts\urban-heat-analysis.py analyze --lst lst.tif --output uhi.tif --csv-stats stats.csv

CSV columns: class, pixel_count, percentage, mean_intensity.

Multi-Sensor Combination

Combine Terra (MOD) and Aqua (MYD) for same-day average:

bash

python scripts\urban-heat-analysis.py combine --terra MOD11A1.tif --aqua MYD11A1.tif --output lst_avg.tif

Then run UHI analysis on the averaged LST.

Validation / Quality Assessment

Compare UHI results with ground-based air temperature stations
Check for anomalous values (|UHI| > 10°C may indicate cloud residual or data error)
Report rural reference temperature sanity (should be within regional climate norms)

Citation

bibtex

@article{imhoff2010remote,
  title={Remote sensing of the urban heat island effect across biomes in the continental USA},
  author={Imhoff, Marc L and Zhang, Ping and Wolfe, Robert E and Bounoua, Lahouari},
  journal={Remote Sensing of Environment},
  volume={114},
  number={3},
  pages={504--513},
  year={2010},
  doi={10.1016/j.rse.2009.10.008}
}

Visualization Guidance

python

import rasterio
import matplotlib.pyplot as plt
import numpy as np

with rasterio.open("uhi.tif") as src:
    uhi = src.read(1)
    nodata = src.nodata

uhi_plot = np.where(uhi == nodata, np.nan, uhi)

fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(uhi_plot, cmap="RdYlBu_r", vmin=-2, vmax=6)
cbar = plt.colorbar(im, ax=ax, shrink=0.8)
cbar.set_label("UHI Intensity (°C)")
ax.set_title("Urban Heat Island Intensity")
ax.axis("off")
plt.tight_layout()
plt.savefig("uhi_map.png", dpi=200)

UHI Classification

UHI Intensity (°C)	Classification
> 4.0	Strong UHI
2.0 to 4.0	Moderate UHI
0.0 to 2.0	Weak UHI
< 0.0	None/Cool

Troubleshooting

Error	Cause	Solution
`ConnectionError`	Network issue	Check internet, retry
`HTTP 429`	Rate limit	Wait 60s, retry
`ValueError`	Invalid input	Check parameter format
Empty output	No data	Try different parameters
`ModuleNotFoundError`	Missing dep	Run pip install

Advanced Usage

Batch Multi-Date Analysis

bash

for lst_file in data/MOD11A1*.tif; do
  python scripts\urban-heat-analysis.py analyze     --input "$lst_file" --rural-fraction 0.1     --output "uhi_$(basename $lst_file)"
done

CI/CD Integration (GitHub Actions)

yaml

# .github/workflows/uhi-monitor.yml
name: UHI Monthly Analysis
on:
  schedule:
    - cron: '0 0 5 * *'  # Monthly
jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install numpy rasterio
      - run: |
          python scripts\urban-heat-analysis.py analyze \
            --input data/latest_lst.tif \
            --rural-fraction 0.1 \
            --output data/uhi_latest.tif

PostGIS Raster Import

bash

raster2pgsql -s 4326 -I -C uhi_latest.tif public.uhi_intensity | psql -d gis_db

Performance Tips

--rural-fraction 0.1 uses coolest 10% as rural reference; adjust for your region
Use --thresholds 1.5 3.0 for arid regions (default 2.0/4.0 is for temperate)
Combine Terra + Aqua: run analysis separately, then average results

中文说明

基于 MODIS 地表温度（LST）GeoTIFF 数据计算城市热岛（UHI）强度，支持热岛分级、时序分析和专题图输出。

安装

bash

pip install requests>=2.28.0 tqdm numpy scipy rasterio
# 或: pip install -r scripts/requirements.txt

依赖

包	用途
`rasterio`	GeoTIFF 读写和 CRS 处理
`numpy`	数组计算和统计
`requests`	数据下载（如适用）
`tqdm`	进度条

数据获取

从以下渠道获取 MODIS LST 数据：

NASA Earthdata Search (https://search.earthdata.nasa.gov/) — 搜索 MOD11A1 或 MYD11A1
NASA LAADS DAAC (https://ladsweb.modaps.eosdis.nasa.gov/)
需要免费 Earthdata 账号 (https://urs.earthdata.nasa.gov/)

NoData 处理

NoData 像元（QC 标记或填充值）自动从 UHI 计算中排除。输出 GeoTIFF 使用 nodata=-9999。使用自动乡村参考时，NoData 像元不参与最冷 N% 计算。

CRS 要求

所有输入栅格应使用相同 CRS。如合并 Terra 和 Aqua 影像，确保它们共享相同的投影和空间范围。如需重投影：

bash

gdalwarp -t_srs EPSG:4326 input.tif reprojected.tif

云污染处理

使用 QC 波段（QC_Day 或 QC_Night）过滤云污染像元。QC 标记为云覆盖的像元应在 UHI 分析前掩膜掉：

bash

python scripts\urban-heat-analysis.py analyze --lst MOD11A1.tif --qc-band qc.tif --output uhi.tif

时序分析日期逻辑

时序分析中，日期从文件名按 MODIS 命名约定提取。也可使用 --date-format 指定自定义格式：

bash

python scripts\urban-heat-analysis.py temporal --lst-dir ./lst_data/ --date-format "%Y%m%d" --output seasonal.json

输出 GeoTIFF 结构

属性	值
数据类型	`float32`
波段数	1（UHI 强度，°C）
NoData	-9999
CRS	与输入相同

自定义 UHI 阈值

bash

python scripts\urban-heat-analysis.py classify --uhi-tif uhi.tif --thresholds "1.0,2.5,4.0" --output classified.tif

3 个值定义无/弱/中/强热岛的分界。

CSV 统计导出

bash

python scripts\urban-heat-analysis.py analyze --lst lst.tif --output uhi.tif --csv-stats stats.csv

CSV 列：class, pixel_count, percentage, mean_intensity。

多传感器组合

组合 Terra (MOD) 和 Aqua (MYD) 计算同日平均：

bash

python scripts\urban-heat-analysis.py combine --terra MOD11A1.tif --aqua MYD11A1.tif --output lst_avg.tif

验证/质量评估

将 UHI 结果与地面气温站对比
检查异常值（|UHI| > 10°C 可能为云残留或数据错误）
报告乡村参考温度合理性（应在区域气候正常范围内）

引用格式

bibtex

@article{imhoff2010remote,
  title={Remote sensing of the urban heat island effect across biomes in the continental USA},
  author={Imhoff, Marc L and Zhang, Ping and Wolfe, Robert E and Bounoua, Lahouari},
  journal={Remote Sensing of Environment},
  volume={114},
  number={3},
  pages={504--513},
  year={2010},
  doi={10.1016/j.rse.2009.10.008}
}

可视化指南

python

import rasterio
import matplotlib.pyplot as plt
import numpy as np

with rasterio.open("uhi.tif") as src:
    uhi = src.read(1)
    nodata = src.nodata

uhi_plot = np.where(uhi == nodata, np.nan, uhi)

fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(uhi_plot, cmap="RdYlBu_r", vmin=-2, vmax=6)
cbar = plt.colorbar(im, ax=ax, shrink=0.8)
cbar.set_label("UHI 强度 (°C)")
ax.set_title("城市热岛强度")
ax.axis("off")
plt.tight_layout()
plt.savefig("uhi_map.png", dpi=200)

故障排除

错误	原因	解决方案
`ConnectionError`	网络问题	检查网络，重试
`HTTP 429`	速率限制	等待 60 秒后重试
`ValueError`	无效输入	检查参数格式
空输出	无数据	尝试不同参数
`ModuleNotFoundError`	缺少依赖	运行 pip install

基于 MODIS 地表温度（LST）GeoTIFF 数据计算城市热岛（UHI）强度，支持热岛分级、时序分析和专题图输出。

功能特性

UHI 强度计算：T_城市 - T_乡村参考
自动乡村参考：无掩膜时使用最冷 N% 像元
自定义乡村掩膜：支持用户定义乡村参考区
热岛分级：强/中/弱/无四级分类
时序分析：多期 LST 影像的季节性热岛变化
GeoTIFF 输出：热岛强度和分级专题图
统计信息：均值、最大最小值、分级占比

使用方法

bash

# 单期 LST 计算 UHI
python scripts\urban-heat-analysis.py analyze --lst MOD11A1.tif --output uhi.tif

# 使用乡村参考掩膜
python scripts\urban-heat-analysis.py analyze --lst lst.tif --rural-mask rural.tif --output uhi.tif

# 热岛分级
python scripts\urban-heat-analysis.py classify --uhi-tif uhi.tif --output classified.tif

# 时序分析
python scripts\urban-heat-analysis.py temporal --lst-dir ./lst_data/ --output seasonal.json

数据来源

MODIS LST (MOD11A1/MYD11A1) — NASA EOSDIS，公共领域
缩放系数：值 × 0.02 = 开尔文
支持 Terra (MOD) 和 Aqua (MYD) 产品

热岛分级标准

UHI 强度 (°C)	等级
> 4.0	强热岛
2.0 至 4.0	中等热岛
0.0 至 2.0	弱热岛
< 0.0	无热岛/冷岛