黑河绿洲国家级重点预防区土地利用图数据集包括甘肃省金塔县、甘肃省高台县等区域2013年、2014年、2015年、2016年和2017年的土地利用，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、22个二级类和2个三级类。
", "ds_source": "数据源为Landsat TM和ETM+数据，主要从美国地质调查局（USGS，https://glovis.usgs.gov/） 获取，少数从中国地理空间数据云（http://www.gscloud.cn/） 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。然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。最后根据土地利用分类标准，统计得到土地利用统计表。
", "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0.1hm2，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": "2013-01-01 00:00:00", "ds_acq_end_time": "2017-12-31 00:00:00", "ds_acq_place": "黑河绿洲国家级重点预防区", "ds_acq_lon_east": 100.23972222222223, "ds_acq_lat_south": 39.07277777777778, "ds_acq_lon_west": 98.05888888888889, "ds_acq_lat_north": 40.93222222222222, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 0, "ds_files_count": 1, "ds_format": "xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "577b477b-82e1-4998-a2f0-4b56970891eb.jpg", "ds_thumb_from": 2, "ds_ref_way": "黄河流域水土保持生态环境监测中心，2013-2017年黑河绿洲国家级重点预防区土地利用统计表数据集，国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2019，doi：10.12072/ncdc.HHSTBC.db0031.2021", "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": null, "quality_level": 3, "publish_time": "2021-01-29 09:16:48", "last_updated": "2025-10-15 14:50:19", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2020.201", "license": null, "i18n": { "en": { "title": "Data set of land use statistics of national key prevention areas in Heihe oasis in 2013-2017", "ds_format": "", "ds_source": "The data source is Landsat TM and ETM + data, mainly obtained from USGS (https://glovis.usgs.gov/), and a few from China geospatial data cloud (http://www.gscloud.cn/)
", "ds_quality": "Remote-sensing images are pre-processed by radiation correction, orthorectification, and fusion and mosaic.\nThe actual surface area corresponding to the minimum map area is not less than 0.1hm2, the polygons have no overlap, no gaps, and the map properties are not vacant or redundant.\nBefore interpreting remote sensing images, the remote sensing imagery, typical surveys, and field comparison methods are used to establish the land use remote sensing interpretation marks.\nBased on the remote sensing image, combined with the interpretation mark, the land use type is extracted.\nInterpretation of interpretation results: extract no less than 5% of the total patch for verification.\nThe number and results of field verification samples meet the requirements of the Technical Specifications for Remote Sensing Monitoring of Soil and Water Conservation (SL592-2012). For verification maps, 10% are selected as verification samples for field verification. ", "ds_ref_way": "", "ds_abstract": "
The data set of land use map of national key prevention areas in Heihe oasis includes the land use of Jinta County and Gaotai County in Gansu Province in 2013, 2014, 2015, 2016 and 2017, with the format of xlsx. The data is named in the form of \"key management area + administrative area + year + land use statistics table\", such as \"key management area ×× County ×× year soil\" Statistical table of land utilization \". The classification system of land use includes 8 first class, 22 second class and 2 third class.
", "ds_time_res": "", "ds_acq_place": "", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Based on ecognition software platform, the method of object-oriented computer automatic classification and manual visual interpretation is used to extract the land use data of the study area year by year. Then three methods are used to verify the data accuracy: field sample point survey, high-resolution image recognition and Google Earth sample point recognition. Finally, according to the classification standard of land use, the statistical table of land use is obtained.
", "ds_ref_instruction": " " } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "土地利用统计表", "8个一级类要素，22个二级类要素，2个三级类要素" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黑河绿洲", "金塔县", "高台县", "甘肃省" ], "ds_time_tags": [ 2013, 2014, 2015, 2016, 2017 ], "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": "赵妍", "email": "447698395@qq.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" }