本数据集聚焦于巢湖流域、滁河流域、里下河地区及长三角示范区在2000年、2010年和2020年三个关键时间节点的地表不透水率变化，采用12.5米空间分辨率的高精度遥感技术，对示范区内的地表不透水性质进行了详尽的动态监测与细致解译。\n
数据集不仅提供了各流域及示范区在不同时段的平均不透水率统计结果，更通过时间序列的对比分析，深刻揭示了下垫面（即地表覆盖物）不透水性质的时空演变特征。这些特征信息对于理解流域内水文循环过程、评估洪涝灾害风险及蓄泄能力至关重要。", "ds_source": "
土壤含水量数据为动态栅格数据，主要用于确定前期土湿，数据来自于全国不透水率数据。下载地址：https://data-starcloud.pcl.ac.cn/zh/resource/13。", "ds_process_way": "
（1）使用ArcGIS的“栅格计算器”或“区域分析”工具，根据解译结果计算各时段的不透水率栅格。\n
（2）利用“按掩膜提取”功能，输入示范区矢量面文件，从不透水率栅格中提取示范区的平均不透水率。\n
（3）对比不同时段的不透水率栅格和统计数据，分析下垫面的时空变化特征，包括不透水面积的增加、减少和空间分布变化。", "ds_quality": "
本数据集专注于巢湖流域、滁河流域、里下河地区以及长三角示范区的不透水率情况，着重分析了不透水面积比例及其空间分布对流域洪涝过程的重要影响。\n
在数据加工方面，采用了高精度的卫星遥感影像作为数据源，结合专业GIS软件和算法，对不透水表面进行了精确的识别和分类。通过运用先进的图像处理技术和空间分析算法，准确地计算了不透水面积比例，并绘制了详细的空间分布图。对数据进行了严格的误差分析和校正，以确保数据的精度满足高标准要求。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "巢湖流域,滁河流域,里下河地区,长三角示范区", "ds_acq_lon_east": 122.26, "ds_acq_lat_south": 30.8, "ds_acq_lon_west": 116.39, "ds_acq_lat_north": 34.63, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1789382671, "ds_files_count": 37, "ds_format": "*.adf", "ds_space_res": "30m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "f8b3ad06-62cb-4263-985e-c1b5d781eb12.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "37eb642a-c117-47e4-a677-07ecffb4b8b7", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-03-27 16:20:26", "last_updated": "2025-06-30 11:34:28", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6776.2025", "i18n": { "en": { "title": "Data sets on imperviousness in the Chaohu Lake Basin, Chu River Basin, Lishang River Region, and Yangtze River Delta Demonstration Area (2000-2020)", "ds_format": "*.adf", "ds_source": "
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.", "ds_quality": "
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.\n
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.", "ds_ref_way": "", "ds_abstract": "
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.\n
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.
", "ds_time_res": "", "ds_acq_place": "Chaohu River Basin, Chuhe River Basin, Lixiahe Region, Yangtze River Delta Demonstration Zone", "ds_space_res": "30m", "ds_projection": "", "ds_process_way": "(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.\n
(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.\n
(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.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "示范区", "不透水率", "遥感影像" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "巢湖流域", "滁河流域", "里下河地区", "长三角示范区" ], "ds_time_tags": [ 2000, 2010, 2020 ], "ds_contributors": [ { "true_name": "胡庆芳", "email": "hqf_work@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "胡庆芳", "email": "hqf_work@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "胡庆芳", "email": "hqf_work@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "category": "其他" }