{ "created": "2021-06-23 09:16:16", "updated": "2026-04-28 11:27:45", "id": "0252432f-6535-4680-9aef-5f0036fbe32b", "version": 2, "ds_topic": null, "title_cn": "2019年甘青宁国家级重点治理区土壤侵蚀数据集", "title_en": "Soil erosion data set of Ganqingning national key control area in 2019", "ds_abstract": "

2019年甘青宁国家级重点治理区土壤侵蚀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年土壤侵蚀统计表，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＋土壤侵蚀统计表”的形式，如“××重点治理区××年土壤侵蚀统计表”。土壤侵蚀强度划分为微度侵蚀、轻度侵蚀、中度侵蚀、强烈侵蚀、极强烈侵蚀和剧烈侵蚀6级。

", "ds_source": "

1.土地利用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。 2.植被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。 3.1:5万DEM主要从水利部信息中心获取。

", "ds_process_way": "

1.基于土地利用、植被覆盖度和坡度等专题图，利用ArcGIS软件进行叠加运算，根据划分规则对土壤侵蚀强度进行等级划分。 2.其中土地利用加工方法为基于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。 3.植被覆盖度加工方法为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。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，并根据划分规则对植被覆盖度进行等级划分，最后使用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。 4.坡度数据加工方法为基于1:5万DEM提取得到。

", "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": 371195, "ds_files_count": 2, "ds_format": "", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "0252432f-6535-4680-9aef-5f0036fbe32b.png", "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": "10.12072/ncdc.hhstbc.db2672.2022", "subject_codes": [], "quality_level": 3, "publish_time": "2021-10-27 14:09:18", "last_updated": "2025-10-15 14:48:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2021.314", "i18n": { "en": { "title": "Soil erosion data set of Ganqingning national key control area in 2019", "ds_format": "", "ds_source": "
    \n
  1. The data sources of land use are ZY-3 and Gao FEN-1 satellite images, which are mainly obtained from the information center of the Ministry of water resources. 2. Vegetation 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. 3.1:50000 DEM is mainly obtained from the information center of the Ministry of water resources.
    2. \n
", "ds_quality": "
    \n
  1. Remote sensing images are preprocessed by radiation correction, orthorectification, fusion and mosaic. 2. The actual surface area corresponding to the minimum spot area is not less than 0.1 h ^, the polygons have no overlap, no gap, and the spot attributes have no vacancy or redundancy. 3. Before the remote sensing image interpretation, the remote sensing image, typical investigation and field comparison methods are used to establish the remote sensing interpretation marks of forest and grass sample plots. 4. Based on remote sensing images, combined with interpretation marks, extract land use types. 5. Review of interpretation results: no less than 5% of the total map spots shall be selected for verification. 6. The 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 verification spots, 10% are selected as verification samples for field verification.
    2. \n
", "ds_ref_way": "", "ds_abstract": "

The soil erosion data set of Gansu, Qinghai and Ningxia national key control 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 statistical table of soil erosion 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 the satellite remote sensing image with spatial resolution of 2m, and the storage format is xlsx. The data is named in the form of \"key management area + year + soil erosion statistical table\", such as“ ×× Key governance areas ×× Statistical table of soil erosion in 2000. Soil erosion intensity can be divided into 6 grades: Micro erosion, light erosion, moderate erosion, strong erosion, very strong erosion and severe erosion

", "ds_time_res": "", "ds_acq_place": "Ganqingning national key management area", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
    \n
  1. Based on the thematic maps of land use, vegetation coverage and slope, the soil erosion intensity was graded by ArcGIS software. 2. The land use processing method is based on ecognition software platform, using the method of object-oriented computer automatic classification and manual visual interpretation to extract the land use data of the study area year by year. Finally, three methods are used to verify the accuracy of the data: field sample point survey, high-resolution image recognition and Google Earth sample point recognition. 3. The processing method of vegetation coverage is based on remote sensing estimation, and the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by pixel dichotomy model. First, the NDVI of each pixel is calculated by using the near-infrared and red band data of multispectral images. Then, the model is used to calculate the vegetation coverage of the whole region, and the vegetation coverage is classified according to the classification rules. Finally, the land use type data obtained from remote sensing interpretation and the vegetation coverage data obtained from remote sensing estimation are used for superposition operation, The vegetation coverage information of each pixel was obtained. 4. Slope data processing method is based on 1:50000 DEM extraction.
    2. \n
", "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": [ "土壤侵蚀统计表，8个一级类要素，25个二级类要素" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "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": "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" }