2018年塔里木河国家级重点预防区土地利用统计表数据集包括新疆维吾尔自治区阿克苏市、乌什县、阿瓦提县、泽普县、莎车县、叶城县、麦盖提县、巴楚县、和田市、和田县、墨玉县、皮山县、洛浦县、策勒县、于田县、民丰县、阿合奇县、阿拉尔市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": "塔里木河国家级重点预防区", "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": 248159, "ds_files_count": 2, "ds_format": "xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1bc310c3-3af3-45b9-8e3f-8c0891a056f8.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 10:30:15", "last_updated": "2025-10-15 14:49:21", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2020.243", "i18n": { "en": { "title": "Data set of land use statistics of Tarim River National key prevention area 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 statistics of the national key prevention areas of the Tarim River in 2018 includes Aksu City, Wushi County, Awati County, Zepu County, Shache County, Yecheng County, Maigaiti County, Bachu County, Hotan City, Hotan County, Moyu County, Pishan County, Luopu County, Cele County, Yutian County, Minfeng County, AHEQI county and alar city of Xinjiang Uygur Autonomous Region in 2018 The statistical table of land use is obtained based on the processing of satellite remote sensing images with spatial resolution better than 16m. 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": "https://creativecommons.org/licenses/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": [ "塔里木河国家级重点预防区，新疆维吾尔自治区，阿克苏市，乌什县，阿瓦提县，泽普县，莎车县，叶城县，麦盖提县，巴楚县，和田市，和田县，墨玉县，皮山县，洛浦县，策勒县，于田县，民丰县，阿合奇县，阿拉尔市" ], "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" }