湟水洮河中下游国家级重点治理区土地利用图数据集包括甘肃省永靖县、青海省民和县等区域2013年、2014年、2015年、2016年和2017年的土地利用，空间分辨率为25米，保存格式为tif，数据命名采用“所属重点治理区＋行政区＋年份＋土地利用图”的形式，如“××重点治理区××县××年土地利用图”。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。所有专题图投影均采用高斯—克吕格，坐标系采用CGCS2000。
\n\n", "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。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0.1hm2，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": "湟水洮河中下游国家级重点治理区", "ds_acq_lon_east": 103.63305555555554, "ds_acq_lat_south": 35.76138888888889, "ds_acq_lon_west": 102.44805555555556, "ds_acq_lat_north": 36.44305555555555, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 24646320, "ds_files_count": 2, "ds_format": "TIF", "ds_space_res": "25.0m", "ds_time_res": "", "ds_coordinate": "CGCS2000", "ds_projection": "高斯—克吕格", "ds_thumbnail": "b4308d58-5947-40af-a524-e55b7898349b.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": "2021-01-12 09:11:34", "last_updated": "2024-03-19 15:17:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2020.131", "i18n": { "en": { "title": "Land use map data set of national key harnessing area in the middle and lower reaches of Taohe River in Huangshui from 2013 to 2017", "ds_format": "", "ds_source": "Data sources are Landsat TM and ETM + data, mainly from the United States Geological Survey (USGS, https://glovis.usgs.gov/ ）Access, a few from China geospatial data cloud（ http://www.gscloud.cn/ ）Get
", "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 map in the middle and lower reaches of Taohe River in Huangshui includes the land use of Yongjing County of Gansu Province and Minhe County of Qinghai Province in 2013, 2014, 2015, 2016 and 2017. The spatial resolution is 25m, and the format is TIF. The data is named in the form of \"key governance area + administrative region + year + land use map\", such as \"×× heavy\" Land use map of ×× County ×× year in the point management area. The classification system of land use includes 8 first class, 22 second class and 2 third class. Gauss Kruger is used for projection of all thematic maps, and CGCS2000 is used as coordinate system.
", "ds_time_res": "", "ds_acq_place": "National key harnessing area in middle and lower reaches of Taohe River in Huangshui", "ds_space_res": "25.0m", "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": [ "重点治理区", "湟水洮河中下游", "青海省", "民和县", "甘肃省", "永靖县", "土地利用" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "民和县", "永靖县", "湟水洮河中下游", "甘肃省", "青海省" ], "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": "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" }