2019年甘青宁国家级重点治理区植被覆盖度数据集包括青海省城东区、城中区、城西区、城北区、大通回族土族自治县、湟中县、湟源县、乐都县、平安县、民和回族土族自治县、互助土族自治县、化隆回族自治县、循化撒拉族自治县、尖扎县、贵德县，宁夏回族自治区同心县、原州区、西吉县、彭阳县、海原县甘肃省会宁县、安定区、永靖县、庄浪县、红古区、七里河区、东乡族自治县、武山县、榆中县、渭源县、康乐县、漳县、靖远县、西固区、积石山保安族东乡族撒拉族自治县、陇西县、广河县、临洮县、临夏市、临夏县、甘谷县、通渭县、安宁区、秦安县、和政县、麦积区、秦州区、城关区48个县2019年植被覆盖度统计表，基于空间分辨率为2米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＋年份＋林草植被覆盖度统计表”的形式，如“××重点治理区××年林草植被覆盖度统计表”。植被覆盖度划分为高覆盖度、中高覆盖度、中覆盖度、中低覆盖度、低覆盖度5级。
", "ds_source": "数据源为资源三号和高分一号卫星影像，主要从水利部信息中心获取。
", "ds_process_way": "基于遥感估算的方法，利用归一化植被指数（NDVI）采用像元二分模型法进行植被盖度估算，方法是首先利用多光谱影像的近红外波段与红波段数据计算每个像元的NDVI，然后使用模型计算整个区域植被覆盖度。再将该区域遥感解译得到的土地利用类型数据和基于遥感估算得到的植被覆盖度数据做叠加运算，获得每个像元的植被覆盖度信息。最后根据划分规则对植被覆盖度进行等级划分，统计得到林草植被覆盖度统计表。
", "ds_quality": "1．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。 2．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。 3．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林草样地遥感解译标志。 4．基于遥感影像，结合解译标志，提取土地利用类型。 5．解译结果复查：抽取不少于总图斑的5%进行核查。 6．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": "2019-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "甘青宁国家级重点治理区", "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": 402371, "ds_files_count": 2, "ds_format": "", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "0f11d9be-0d92-46d2-b3a8-03792ebcfbd5.png", "ds_thumb_from": 2, "ds_ref_way": "黄河流域水土保持生态环境监测中心，2019年甘青宁国家级重点治理区植被覆盖度数据集，国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2021，doi：10.12072/ncdc.hhstbc.db2664.2022", "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": "10.12072/ncdc.hhstbc.db2664.2022", "subject_codes": null, "quality_level": 3, "publish_time": "2021-10-27 14:09:18", "last_updated": "2025-10-15 14:47:41", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2021.313", "i18n": { "en": { "title": "Vegetation coverage data set of Ganqingning national key management area in 2019", "ds_format": "", "ds_source": "The data sources are ZY-3 and Gao FEN-1 satellite images, which are mainly obtained from the information center of the Ministry of water resources.
", "ds_quality": "The vegetation coverage data set of Ganqingning national key management areas in 2019 includes Chengdong District, Chengzhong District, Chengxi District, Chengbei District, Datong Hui and Tu Autonomous County, Huangzhong County, Huangyuan County, Ledu County, Ping'an County, Minhe Hui and Tu Autonomous County, Huzhu Tu Autonomous County, Hualong Hui Autonomous County, Xunhua sala Autonomous County, Jianzha county and guide county, Tongxin County, Yuanzhou District, Xiji County, Pengyang County, Haiyuan County, Ningxia Hui Autonomous Region Huining County, Anding District, Yongjing County, Zhuanglang County, Honggu District, Qilihe district, Dongxiang Autonomous County, Wushan County, Yuzhong County, Weiyuan County, Kangle County, Zhangxian County, Jingyuan County, Xigu District, Jishishan Bao'an County, Dongxiang Sala Autonomous County, Longxi County, Guanghe County, Lintao County The vegetation coverage statistical table of 48 counties in Linxia city, Linxia County, Gangu County, Tongwei County, Anning District, Qin'an County, Hezheng County, Maiji District, Qinzhou District and Chengguan District in 2019 is obtained by processing satellite remote sensing image with spatial resolution of 2m, and the saved format is xlsx. The data name is in the form of \"key management area + year + forest and grass vegetation coverage statistical table\", Such as“ ×× Key governance areas ×× Statistical table of annual forest and grass vegetation coverage. The vegetation coverage is divided into five levels: high coverage, medium high coverage, medium coverage, medium low coverage and low coverage
", "ds_time_res": "", "ds_acq_place": "Ganqingning national key management area", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Based on the method of remote sensing estimation, the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by using the pixel dichotomy model. The method is to first calculate the NDVI of each pixel using the near infrared band and red band data of multispectral images, and then use the model to calculate the vegetation coverage of the whole region. Then the land use type data from remote sensing interpretation and the vegetation coverage data from remote sensing estimation are superimposed to obtain the vegetation coverage information of each pixel. Finally, according to the classification rules, the vegetation coverage was classified, and the statistical table of forest and grass vegetation coverage was obtained.
", "ds_ref_instruction": " " } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "林草植被覆盖度统计表", "高、中高、中、中低、低林草不同覆盖度面积及比例，水力侵蚀区，风力侵蚀区，冻融侵蚀区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "甘青宁国家级重点治理区：青海省、宁夏回族自治区、甘肃省" ], "ds_time_tags": [], "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" }