{ "created": "2021-06-24 07:06:56", "updated": "2026-04-28 12:14:52", "id": "1e524c3c-b735-4167-abf5-f0fec02a2734", "version": 4, "ds_topic": null, "title_cn": "2019年阴山北麓国家级重点预防区水土保持措施数据集", "title_en": "Data set of soil and water conservation measures for national key prevention areas in north foot of Yinshan Mountain in 2019", "ds_abstract": "

2019年阴山北麓国家级重点预防区水土保持措施数据集包括内蒙古自治区达尔罕茂明安联合旗、乌拉特中旗、乌拉特后旗、四子王旗、苏尼特左旗、苏尼特右旗2019年的水土保持措施统计表。

", "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": "阴山北麓国家级重点预防区", "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": 129598, "ds_files_count": 2, "ds_format": "xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1e524c3c-b735-4167-abf5-f0fec02a2734.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": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2021-10-27 14:08:15", "last_updated": "2025-10-15 09:19:28", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.db2504.2022", "i18n": { "en": { "title": "Data set of soil and water conservation measures for national key prevention areas in north foot of Yinshan Mountain in 2019", "ds_format": "", "ds_source": "
                     <p>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.</p>\n
", "ds_quality": "
                         <ol>\n
\n
  • 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.
    • \n", "ds_ref_way": "", "ds_abstract": "
     <p>The data set of soil and water conservation measures in the national key prevention area at the northern foot of Yinshan Mountain in 2019 includes the statistical table of soil and water conservation measures in dahanmaoming'an United banner, Wulate Middle Banner, Wulate rear banner, Siziwang Banner, Sunite Left Banner and Sunite Right Banner of Inner Mongolia Autonomous Region in 2019</p>\n
    ", "ds_time_res": "", "ds_acq_place": "National key prevention area at the northern foot of Yinshan Mountain", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
                         <p>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.</p>\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": [ "国家级重点预防区", "阴山北麓", "水土保持措施" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "阴山北麓国家级重点预防区" ], "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" }