2000–2023年平陆运河区域环境时空变化特征数据集

区域环境数据体包括水文气象、生态环境、大气环境和社会经济四类参数，总计涵盖了23套参数数据。气象水文包括地表水体类型分布数据、降水量数据和平均地表温度数据；生态环境包括湿地类型分布数据、地表覆盖类型数、陆地生态系统空间分布数据、数字高程数据、地貌类型空间分布数据、土壤类型分布数据、植被类型分布数据、植被指数数据和物种丰富度数据；大气环境包括近地表PM2.5浓度数据、近地表PM10浓度数据、近地表SO2浓度数据和近地表NO2浓度数据；社会经济包括人口网格数据、生产总值数据、人居建筑分布数据、不透水空间分布数据、夜间灯光数据、铁路线矢量数据和道路线矢量数据。通过采用土地利用动态度指标和趋势检验等时空特征分析方法，分析了环境变化特征与信息。最终，整理形成平陆运河区域环境时空变化特征数据体，并将制备好的数据存储为相应的Shapefile格式。

The regional environmental dataset encompasses four categories of parameters: hydrometeorology, ecological environment, atmospheric environment, and socio-economy, totaling 23 sets of parameter data. The hydrometeorological parameters include surface water body type distribution data, precipitation data, and average surface temperature data. The ecological environment parameters cover wetland type distribution data, land cover type distribution data, spatial distribution data of terrestrial ecosystems, digital elevation model (DEM) data, spatial distribution data of geomorphic types, spatial distribution data of soil types, spatial distribution data of vegetation types, vegetation index data, and species richness data. The atmospheric environment parameters involve near-surface PM2.5 concentration data, near-surface PM10 concentration data, near-surface SO2 concentration data, and near-surface NO2 concentration data. The socio-economic parameters include population grid data, gross domestic product (GDP) data, human settlement building distribution data, impervious space distribution data, nighttime light data, railway line vector data, and road line vector data. Spatiotemporal feature analysis methods such as land use dynamic degree index and trend test were employed to analyze the characteristics and information of environmental changes. Finally, the spatiotemporal change characteristic dataset of the Pinglu Canal regional environment was compiled, and the prepared data was stored in the corresponding Shapefile format.

搜集汇总

背景与挑战

背景概述

该数据集是一个覆盖2000至2023年的多维度环境数据集，包含水文气象、生态环境、大气质量和社会经济四大类共26组参数，旨在为平陆运河区域的环境评估和可持续发展研究提供数据支持。数据集采用标准化网格方法，确保了公里级的多维时空一致性，适用于区域数据可视化和数字孪生应用。