2018年黄河多沙粗沙国家级水土流失重点治理区土地利用统计表数据集包括山西省古交市、古交市、娄烦县、右玉县、静乐县、神池县、五寨县、岢岚县、河曲县、保德县、偏关县、吉县、乡宁县、大宁县、隰县、永和县、蒲县、汾西县、离石区、兴县、临县、柳林县、石楼县、岚县、方山县、中阳县、交口县，内蒙古托克托县、和林格尔县、清水河县、东胜区、达拉特旗、准格尔旗、鄂托克前旗、鄂托克旗、杭锦旗、乌审旗、伊金霍洛旗、磴口县、凉城县，陕西省韩城市、宝塔区、安塞县、延长县、延川县、子长县、志丹县、吴起县、宜川县、榆阳区、神木市、府谷县、横山区、靖边县、定边县、绥德县、米脂县、佳县、吴堡县、清涧县、子洲县，甘肃省泾川县、灵台县、西峰区、庆城县、环县、华池县、合水县、宁县、镇原县，宁夏回族自治区盐池县等70个县2018年的土地利用统计表，基于空间分辨率优于16m的卫星遥感影像加工获得，保存格式为xlsx，数据命名采用“所属重点治理区＋行政区＋年份＋土地利用统计表”的形式，如“××重点治理区××县××年土地利用统计表”。土地利用的分类系统包括8个一级类、25个二级类。
", "ds_source": "土地利用遥感影像数据源为资源三号和高分一号卫星影像，从水利部信息中心获取。
", "ds_process_way": "基于eCognition软件平台，采用面向对象计算机自动分类与人工目视解译相结合的方法，提取研究区逐年土地利用数据。最后采用三种方法对数据精度进行验证：野外样本点调查、高分辨率影像识别和Google Earth的样本点识别。
", "ds_quality": "遥感影像均经过辐射纠正、正射纠正以及融合、镶嵌等预处理。\n最小图斑面积对应的实际地物面积不小于0.1h㎡，多边形无重叠、无空隙，图斑属性无空置或冗余。\n遥感影像解译前，采用遥感影像、典型调查、与实地对照的方法建立土地利用遥感解译标志。\n基于遥感影像，结合解译标志，提取土地利用类型。\n解译结果复查：抽取不少于总图斑的5%进行核查。\n野外验证样本数量和成果满足《水土保持遥感监测技术规范》（SL592-2012）的要求，对于核查图斑，抽取10%作为验证样本进行实地验证。
", "ds_acq_start_time": "2018-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "黄河多沙粗沙国家级水土流失重点治理区", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 779005, "ds_files_count": 2, "ds_format": "xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "0e0ea6cb-ae5c-42cf-a86c-b8e9f795aab4.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "18fc6591-ef53-4202-bc01-c3961ad212d2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [], "quality_level": 3, "publish_time": "2021-02-01 10:31:13", "last_updated": "2025-10-15 14:49:19", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2020.233", "i18n": { "en": { "title": "Data set of land use statistical table of national key control area of soil and water loss in the Yellow River in 2018", "ds_format": "", "ds_source": "The land use remote sensing image data sources are ZY-3 and gaofen-1 satellite images, which are obtained from the information center of the Ministry of water resources.
", "ds_quality": "The remote sensing images are preprocessed by radiation correction, orthorectification, fusion and mosaic.\nThe actual surface area corresponding to the minimum patch area is not less than 0.1 Hm2, the polygon has no overlap, no gap, and the patch attribute has no vacancy or redundancy.\nBefore remote sensing image interpretation, remote sensing image, typical survey and field comparison were used to establish remote sensing interpretation marks of land use.\nBased on the remote sensing image, combined with the interpretation marks, the land use types are extracted.\nReexamination of interpretation results: take no less than 5% of the total map spots for verification.\nThe number and results of field verification samples meet the requirements of technical specification for remote sensing monitoring of soil and water conservation (sl592-2012). For the verification map spots, 10% of the verification samples are selected for field verification.
", "ds_ref_way": "", "ds_abstract": "The data set of land use statistical table of national key control areas for heavy and coarse sand of the Yellow River in 2018 includes Gujiao City, Gujiao City, Loufan County, Youyu County, jingle County, Shenchi County, Wuzhai County, Kelan County, Hequ County, Baode County, Pianguan County, Jixian County, Xiangning County, Daning County, Xi county, Yonghe County, Pu County, Fenxi County, Lishi District, Xingxian County, Linxian county and Liuzhou county Linxian County, Shilou County, Lanxian County, Fangshan County, Zhongyang County, Jiaokou County, Inner Mongolia Tuoketuo County, Helingeer County, Qingshuihe County, Dongsheng District, Dalate Banner, Zhungeer banner, etokeqian banner, etoke banner, Hangjin Banner, Wushen Banner, Yijinhuoluo banner, Dengkou County, Liangcheng County, Hancheng City, Baota District, Ansai County, Yanchang County, Yanchuan County, Zichang County, Zhizhi County, etc Land use of 70 counties in 2018, including Dan County, Wuqi County, Yichuan County, Yuyang District, Shenmu City, Fugu County, Hengshan District, Jingbian County, Dingbian County, Suide County, Mizhi County, Jiaxian County, Wubao County, Qingjian County, Zizhou County, Jingchuan County, Lingtai County, Xifeng District, Qingcheng County, Huanxian County, Huachi County, Heshui County, Ningxian County, Zhenyuan County and Yanchi County of Ningxia Hui Autonomous Region The statistical table is obtained based on the processing of satellite remote sensing image with spatial resolution better than 16m, and the format is xlsx. The data is named in the form of \"key governance area + administrative region + year + land use statistical table\", such as \"land use statistical table of ×× key governance area ×× County ××\". The classification system of land use includes 8 first class classes and 25 second class classes.
", "ds_time_res": "", "ds_acq_place": "", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Based on ecognition software platform, the method of combining object-oriented computer automatic classification with manual visual interpretation was used to extract the annual land use data of the study area. Finally, three methods are used to verify the data accuracy: field sample point survey, high resolution image recognition and Google Earth sample point recognition.
", "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": [ "土地利用统计表，8个一级类要素，25个二级类要素" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "黄河多沙粗沙国家级水土流失重点治理区，山西省，内蒙古，陕西省，甘肃省，宁夏回族自治区" ], "ds_time_tags": [ 2018 ], "ds_contributors": [ { "true_name": "黄河流域水土保持生态环境监测中心", "email": "1283337@qq.com", "work_for": "黄河流域水土保持生态环境监测中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "黄河流域水土保持生态环境监测中心", "email": "szyjdata@163.com", "work_for": "黄河流域水土保持生态环境监测中心", "country": "中国" } ], "ds_managers": [ { "true_name": "黄河流域水土保持生态环境监测中心", "email": "szyjdata@163.com", "work_for": "黄河流域水土保持生态环境监测中心", "country": "中国" } ], "category": "水土保持" }