This dataset focuses on the changes of surface imperviousness in the Chaohu Lake Basin, Chu River Basin, Lishiahe River Area and the Yangtze River Delta Demonstration Area at three key time points, namely, 2000, 2010 and 2020, and adopts high-precision remote sensing technology with a spatial resolution of 12.5 meters to carry out a detailed dynamic monitoring and detailed interpretation of the imperviousness properties of the surface in the Demonstration Area.
The dataset not only provides the statistical results of the average imperviousness of the watersheds and the demonstration area in different time periods, but also reveals the spatial and temporal characteristics of the imperviousness of the subsurface (i.e., the surface cover) through the comparative analysis of the time series. This information is essential for understanding the hydrologic cycle process in the watersheds and for assessing the risk of flooding and the storage and drainage capacity.
Soil moisture content data are dynamic raster data, primarily used to determine pre-existing soil moisture, and are derived from national imperviousness data. Downloaded from https://data-starcloud.pcl.ac.cn/zh/resource/13.
(1) Use the “Raster Calculator” or “Area Analysis” tool of ArcGIS to calculate the imperviousness raster for each time period based on the interpretation results.
(2) Using the function of “Extract by Mask”, input the vector surface file of the demonstration area and extract the average imperviousness of the demonstration area from the imperviousness raster.
(3) Compare the imperviousness rasters and statistical data of different time periods, and analyze the temporal and spatial characteristics of the subsurface, including the increase, decrease and spatial distribution of impervious area.
This dataset focuses on the imperviousness situation in the Chaohu Lake Basin, the Chu River Basin, the Lishiahe River area, and the Yangtze River Delta Demonstration Area, with an emphasis on analyzing the important impacts of the proportion of impervious area and its spatial distribution on the flooding process in the basins.
In terms of data processing, high-precision satellite remote sensing imagery was used as the data source, combined with professional GIS software and algorithms to accurately identify and classify impervious surfaces. By applying advanced image processing techniques and spatial analysis algorithms, the proportion of impervious surface was accurately calculated and a detailed spatial distribution map was drawn. The data were rigorously analyzed and corrected for errors to ensure that the accuracy of the data met high standards.
| # | number | name | type |
| 1 | 2021YFC3000100 | Lower Yangtze River Flood Disaster Integration and Control and Emergency De-risking Technology and Equipment | National key R & D plan |
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CSTR:11738.11.NCDC.NHRI.DB6776.2025
10.12072/ncdc.nhri.db6776.2025