Data for Urban Ecological Hazard-Risk Assessment in Delhi-National Capital Region

The satellite datasets were acquired from the openly available Google Earth Engine (GEE) platform from various satellites and sensors. The given dataset was used for computing the ecological hazard risk and vulnerability of Delhi-NCR region using 13 contributing variables, such as built-up density (Landsat; 2020), local climate zones (WUDAPT; 2022), green cover density (Landsat; 2020), rate of built-up growth (Landsat; from 1973 to 2020), loss of green cover (Landsat; 1973 to 2020), land surface temperature (Landsat; May 2022), PCA of biochemical and biophysical variables (Sentinel 2A; May 2022), road density, drainage density, slope and elevation (SRTM DEM), population growth, and population density (GHSL). All the above were prepared using 200x200 m grids and resampled at 10 m spatial resolution. Utilizing the fuzzy-AHP model, all the thematic layers were rescaled based on the weights, where a lower value of the variable indicates lower susceptibility and vice versa.

本卫星数据集取自公开可用的谷歌地球引擎（Google Earth Engine, GEE）平台，涵盖多颗卫星与传感器采集的数据。本数据集被用于德里国家首都辖区（Delhi-NCR）的生态灾害风险与脆弱性评估，共纳入13项贡献变量，具体包括：建筑密度（陆地卫星（Landsat）；2020年）、局地气候区（WUDAPT；2022年）、绿地覆盖率（Landsat；2020年）、建筑扩张速率（Landsat；1973—2020年）、绿地流失量（Landsat；1973—2020年）、地表温度（Landsat；2022年5月）、基于哨兵2号A星（Sentinel 2A）2022年5月数据生成的生化与生物物理变量主成分分析（Principal Component Analysis, PCA）、道路密度、排水密度、坡度与海拔（航天飞机雷达地形测绘任务数字高程模型（SRTM DEM））、人口增长及人口密度（全球人类住区层（Global Human Settlement Layer, GHSL））。上述所有变量均采用200×200米网格生成，并重采样至10米空间分辨率。本研究采用模糊层次分析法（Fuzzy-AHP），基于权重对所有专题图层进行重新缩放，其中变量取值越低，对应的灾害易感性越低，反之亦然。