本研究基于全球逐年不透水面变化: 1985-2018数据集，对长江下游地区的不透水面进行分析，长江下游地区不透水面空间分布格局与建设用地具有较高的相似性，在2000年之前，不透水面主要分布于上海、苏州、无锡、常州、南京、合肥以及杭州等大城市，2000年之后，不透水面沿大城市径向扩张的同时，中小城镇的不透水面也迅速发展，到2010年长江下游地区中南京、常州、无锡、苏州以及上海等城市逐渐形成连片化格局。", "ds_source": "
数据来源于清华大学（http://data.ess.tsinghua.edu.cn.）， 基于长时序的Landsat光学影像(近150万景)及其他的辅助数据(夜间灯光数据及Sentinel-1雷达数据)，首先通过空间掩模和特征评价(“Exclusion-Inclusion”)算法实现了对逐年不透水面的快速制图，然后通过时间一致性检验(“Temporal Consistency Check”)算法对初始的不透水面序列进行时间域滤波和转化逻辑推理，从而保证了获取的不透水面序列在时空上的合理性。\n
针对全球干旱区不透水面制图的难点，研究组引入了Sentinel-1雷达数据和夜间灯光数据，较之前的研究显著提高了产品在干旱区的制图精度。通过对典型年份的精度评价分析可知GAIA的平均总体精度超过了90%。同时对比全球主要的城市数据产品发现，GAIA在城市面积的量级和时序特征上均更为合理。", "ds_process_way": "
基于ArcGIS“按掩膜提取”功能，输入研究区矢量面文件和不透水面栅格文件，得到所研究长江下游地区不透水面分布图。", "ds_quality": "
数据研究整理长江下游地区不透水面，长江下游地区不透水率从1985年的1.2%升高至2018年的17.6%，时程变上总体呈现先慢后快又趋于平缓的特征，表现为2000年前缓慢增加，增速为0.27%/a，2000-2010年不透水率增速明显提高，达到0.44%/a，而到2010-2016年，增速提升至0.99%/a，2016-2018年进入相对平缓阶段。\n
从各省份角度看，不透水率的高低排序为上海市>太湖流域>江苏省>浙江省>安徽省；总体上、上海市、江苏省、浙江省以及太湖流域也呈现与长江下游地区出类似的时程变化，安徽省不透水率长期处于缓慢增加状态，但增速在2010年后有所增加；各省份在2016年后均呈现不透水率增速明显放缓的趋势，这与海绵城市低影响开发设施的建设有一定关系。", "ds_acq_start_time": "1985-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "长江下游地区", "ds_acq_lon_east": 121.97, "ds_acq_lat_south": 29.59, "ds_acq_lon_west": 115.47, "ds_acq_lat_north": 32.74, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 588972393, "ds_files_count": 7, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "a63f6add-98a0-4cc6-8bb8-2ebb77231a70.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": "2025-02-27 18:55:25", "last_updated": "2025-06-30 11:40:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6767.2025", "i18n": { "en": { "title": "Data set of impervious water surface in lower Yangtze River", "ds_format": "*.tif", "ds_source": "
Data from Tsinghua University (http://data.ess.tsinghua.edu.cn.) Based on the long time-series Landsat optical imagery (nearly 1.5 million views) and other auxiliary data (nighttime light data and Sentinel-1 radar data), the rapid mapping of year-by-year impervious surfaces was firstly realized by spatial mask and feature evaluation ( “Exclusion-Inclusion”) algorithm realizes rapid mapping of impervious surfaces from year to year, and then the temporal consistency check (“Temporal Consistency Check”) algorithm is applied to the initial sequence of impervious surfaces. The initial impervious surface sequence is then subjected to temporal domain filtering and transformational logic reasoning by the Temporal Consistency Check (“TCC”) algorithm, which ensures the temporal and spatial rationality of the acquired impervious surface sequence.\n
To address the difficulty of impervious surface mapping in the global arid zone, the research group introduced Sentinel-1 radar data and nighttime light data, which significantly improved the mapping accuracy of the product in the arid zone compared with previous studies. By analyzing the accuracy evaluation of typical years, it can be seen that the average overall accuracy of GAIA exceeds 90%. Meanwhile, comparing the major urban data products in the world, GAIA is found to be more reasonable in terms of the magnitude of the urban area and the temporal characteristics.", "ds_quality": "
Data research collation of impervious surface in the lower reaches of the Yangtze River, the impervious rate in the lower reaches of the Yangtze River rose from 1.2% in 1985 to 17.6% in 2018, the time course change on the general presentation of the characteristics of the first slow and then fast and tend to flatten out, the performance of the pre-2000 slow increase in the rate of growth of 0.27%/a, the growth rate of the impervious rate in the period of 2000-2010 increased markedly to reach 0.44%/a,. And by 2010-2016, the growth rate increased to 0.99%/a, and 2016-2018 entered a relatively flat phase.\n
From the perspective of each province, the impervious rate of high and low ranking for Shanghai > Taihu Lake Basin > Jiangsu Province > Zhejiang Province > Anhui Province; in general, Shanghai, Jiangsu Province, Zhejiang Province and Taihu Lake Basin also presents with the lower reaches of the Yangtze River out of a similar time course of change, the rate of imperviousness of Anhui Province in the long term in a slow increase in the status of the rate of growth in 2010, but the rate of growth in the years after an increase in the rate; provinces in 2016 after the impervious rate of growth show a significant slowdown trend, which has a certain relationship with the construction of sponge city low impact development facilities. growth rate slowed down significantly, which is related to the construction of low impact development facilities in sponge cities.", "ds_ref_way": "", "ds_abstract": "
This study analyzes the impervious surfaces in the lower reaches of the Yangtze River based on the Global Year-by-Year Changes in Impervious Surfaces: 1985-2018 dataset. The spatial distribution pattern of impervious surfaces in the lower reaches of the Yangtze River has a high degree of similarity with that of the built-up land, with impervious surfaces distributed in the large cities such as Shanghai, Suzhou, Wuxi, Changzhou, Nanjing, Hefei, and Hangzhou before 2000. Before 2000, impervious surfaces were mainly distributed in Shanghai, Suzhou, Wuxi, Changzhou, Nanjing, Hefei, and Hangzhou, etc. After 2000, while the radial expansion of impervious surfaces along the large cities, the impervious surfaces of small and medium-sized towns also developed rapidly, and by 2010, the cities of Nanjing, Changzhou, Wuxi, Suzhou, and Shanghai in the lower reaches of the Yangtze River had gradually formed a pattern of contiguity.
", "ds_time_res": "年", "ds_acq_place": "Lower Yangtze River region", "ds_space_res": "30m", "ds_projection": "", "ds_process_way": "Based on the function of ArcGIS “Extract by Mask”, we input the vector surface file and the raster file of impervious surface in the study area, and get the distribution map of impervious surface in the lower reaches of the Yangtze River under study.", "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": [ "长江下游", "土地利用", "不透水面" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "长江下游" ], "ds_time_tags": [ 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2018 ], "ds_contributors": [ { "true_name": "商守卫", "email": "ssw971216@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "商守卫", "email": "ssw971216@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "商守卫", "email": "ssw971216@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "category": "生态" }