本数据集基于Landsat卫星遥感数据、WorldView-4卫星数据以及GLC_FCS30-1985_2020地表监测数据，对研究区荒草二三圩区、蒿子圩、汪波东荡等典型蓄滞洪区下垫面地理实体信息进行解译和分析,获得了研究区域的基础地理高程信息和下垫面土地类型信息。\n
WorldView-4卫星数据空间分辨率为0.15m，土地利用类型数据GLC_FCS30-1985_2020地表监测数据分辨率为30m。经过分析可以为蓄滞洪区的合理规划、有效管理以及可持续发展提供科学依据和决策支持，同时为蓄滞洪区洪水精细化模拟模型构建提供了数据支撑。", "ds_source": "
地理高程信息来自与Landsat和WorldView-4。其中Landsat是美国陆地探测卫星系统，成功地向地面输送了大量的、高质量的地球表面信息的观测数据。\n
目前最新的Landsat-8卫星携带两个传感器，分别是OLI陆地成像仪和TIRS热红外传感器，一共有11个波段，波段1-7，9-11的空间分辨率为30米，波段8为15米分辨率的全色波段，卫星每16天可以实现一次全球覆盖。WorldView-4卫星是DigitalGlobe商用高分辨率遥感卫星，于2016年11月搭乘美国擎天神5号运载火箭发射升空 重采样0.13m。其有着超高的分辨率，全色分辨率达到31厘米；同时其多光谱分辨率能够达到1.24米，并且具有高定位精度以及在多种影像采集模式下的超大储存容量。\n
土地利用数据为中国科学院空天信息创新研究院利用1984-2020年所有Landsat卫星数据（Landsat TM，ETM+和OLI）生产的1985年-2020年全球30米精细地表覆盖动态监测产品。该产品沿用了2020年基准数据的分类体系，共包含29个地表覆盖类型，更新周期为5年。", "ds_process_way": "
针对GLC_FCS30-1985_2020地表监测数据，运用ArcGIS栅格重分类工具，将主要覆盖类型分为多个类别，分别为建筑、林地、水体、水田、耕地、草地等；针对Landsat-8卫星数据，对影像进行重采样处理以改正输出影像的像元偏差，以此来建立新的图像矩阵，通过双线性内插法对图像重新采样，利用ArcGIS栅格处理中的波段合成功能，选取2、3、4波段进行真彩色波段合成，再将多光谱图像8波段全色图像进行融合处理，使其具有较高分辨率。", "ds_quality": "
数据研究整理发现荒草二三圩、蒿子圩、十八联圩蓄滞洪区内，耕地为第一大类土地利用类型，其次是水体或水田，最少是建筑。2020年所有蓄滞洪区中耕地面积占比最大的是十八联圩，约为93.9%，最小的是蒿子圩，约为67.6%；水体和水田、建筑面积占比最大的均是蒿子圩，约为27.6%、4.4%，最小的均是十八联圩，仅占4.5%、1.1%；荒草二三圩中林地面积占比大于蒿子圩和十八联圩。与实际情况贴合。同时研究所获取的地理高程信息也与实际相符，因此，本数据集可作为典型蓄滞洪区评价分析的可靠基础数据。", "ds_acq_start_time": "1985-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "长江下游典型蓄滞洪区", "ds_acq_lon_east": 119.03, "ds_acq_lat_south": 31.52, "ds_acq_lon_west": 117.28, "ds_acq_lat_north": 32.44, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 448632644, "ds_files_count": 101, "ds_format": "*.adf,*.txt", "ds_space_res": "30m,15m,1m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "da1174ed-7197-4c64-a16a-fd04506013c3.png", "ds_thumb_from": 0, "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": "2025-03-28 10:15:27", "last_updated": "2025-06-30 11:34:28", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6799.2025", "i18n": { "en": { "title": "Geographic Elevation Data and Land Use Type Data Set for Typical Flood Storage Areas in the Chu River Basin (1985-2020)", "ds_format": "*.adf,*.txt", "ds_source": "
Geographic elevation information is derived from Landsat and WorldView-4, which is the U.S. Land Exploration Satellite System (LES) that has successfully delivered a large amount of high-quality Earth surface information observations to the ground.\n
Currently the latest Landsat-8 satellite carries two sensors, respectively OLI land imager and TIRS thermal infrared sensors, a total of 11 bands, bands 1-7, 9-11 spatial resolution of 30 meters, band 8 for the 15-meter resolution of panchromatic bands, satellites can be achieved once every 16 days global coverage. WorldView-4 is DigitalGlobe's commercial high-resolution remote sensing satellite, launched in November 2016 aboard the U.S. Optimus Prime 5 launch vehicle and weighing 0.13 m. It has ultra-high resolution, with a panchromatic resolution of 31 cm; its multispectral resolution can reach 1.24 m, and it has a high positional accuracy as well as large storage capacity under a variety of image acquisition modes. It has a high resolution of 31 cm in panchromatic, 1.24 meters in multispectral, high positioning accuracy and large storage capacity in multiple image acquisition modes.\n
The land use data is a global 30-meter fine ground cover dynamic monitoring product for 1985-2020 produced by the Institute of Space and Astronautical Information Innovation (ISAI) of the Chinese Academy of Sciences (CAS), using all Landsat satellite data (Landsat TM, ETM+ and OLI) from 1984 to 2020. The product follows the classification system of the 2020 baseline data and contains a total of 29 surface cover types, with an update cycle of five years.", "ds_quality": "
Data research and organization found that the barren grass two or three dike, artemisia dike, eighteen lianxu flood storage area, arable land is the first major category of land-use types, followed by water bodies or paddy fields, and the least is the building. 2020 all of the stagnant flood storage area of arable land area accounted for the largest proportion of the eighteen lianxu dike, about 93.9%, the smallest is the artemisia dike, about 67.6%; water bodies and paddy fields, Building area accounted for the largest are Artemisia pike, about 27.6%, 4.4%, the smallest are eighteen Lianxu, only 4.5%, 1.1%; barren grass two or three pikes in the forest area accounted for more than Artemisia pike and eighteen Lianxu. With the actual situation fit. At the same time, the geographic elevation information obtained by the research institute is also consistent with the actual situation, therefore, this dataset can be used as a reliable basic data for the evaluation and analysis of typical flood storage area.", "ds_ref_way": "", "ds_abstract": "
This dataset is based on Landsat satellite remote sensing data, WorldView-4 satellite data and GLC_FCS30-1985_2020 surface monitoring data, and interprets and analyzes the information of geographic entities on the subsurface of typical flood storage areas such as Arakusa 2-3 polder area, Artemisia polder, and Wangbo Dongdang, etc., and acquires the study area's The basic geographic elevation information and land type information of the subsurface were obtained.\n
WorldView-4 satellite data with a spatial resolution of 0.15 m, and land use type data GLC_FCS30-1985_2020 surface monitoring data with a resolution of 30 m. After analysis, the data can be analyzed to provide a scientific basis and decision-making support for the rational planning, effective management and sustainable development of the flood storage area, and at the same time provide a scientific basis and decision-making support for the construction of flood refinement simulation model of the flood storage and retention area. At the same time, it provides data support for the construction of the flood simulation model.
", "ds_time_res": "", "ds_acq_place": "Typical flood storage and detention areas in the lower reaches of the Yangtze River", "ds_space_res": "30m,15m,1m", "ds_projection": "", "ds_process_way": "For the GLC_FCS30-1985_2020 surface monitoring data, the ArcGIS raster reclassification tool was used to classify the main coverage types into multiple categories, which are buildings, forests, water bodies, paddy fields, cultivated land, grasslands, etc. For the Landsat-8 satellite data, the image resampling process was carried out to correct the image deviation of the output image, so as to establish a new image matrix, and the image was resampled by bilinear interpolation, and the band synthesis function in ArcGIS raster processing was utilized to select 2, 3, and 4 waves. For Landsat-8 satellite data, the image resampling process is carried out to correct the image deviation of the output image, so as to establish a new image matrix, resample the image by bilinear interpolation, use the band synthesis function in ArcGIS raster processing, select 2, 3, 4 bands for true color band synthesis, and then fusion process the multispectral image 8-band panchromatic image, so as to make it have a high resolution.", "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": [ "蓄滞洪区", "地理高程信息", "土地覆盖", "遥感影像" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "滁河", "荒草二圩", "荒草三圩", "蒿子圩", "十八联圩" ], "ds_time_tags": [ 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020 ], "ds_contributors": [ { "true_name": "陈兆懿", "email": "czy_nhri@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈兆懿", "email": "czy_nhri@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈兆懿", "email": "czy_nhri@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "category": "基础地理" }