河龙区间多沙粗沙国家级重点治理区土地利用统计表数据集包括内蒙古自治区准格尔旗、伊金霍洛旗、东胜区，陕西省神木县、宝塔区等区域2013年、2014年、2015年、2016年和2017年的土地利用统计表，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＋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”。
", "ds_source": "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_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0.1hm2，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": "2013-01-02 00:00:00", "ds_acq_end_time": "2017-12-31 00:00:00", "ds_acq_place": "河龙区间多沙粗沙国家级重点治理区", "ds_acq_lon_east": 111.44166666666668, "ds_acq_lat_south": 38.20611111111111, "ds_acq_lon_west": 109.00888888888889, "ds_acq_lat_north": 40.323888888888895, "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": "Excel表格", "ds_space_res": "2.0m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "3750a018-3187-4b3b-ae8d-8a228cfd9f93.jpg", "ds_thumb_from": 0, "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.db2388.2022", "subject_codes": [ "170.45", "170.55" ], "quality_level": 3, "publish_time": "2022-08-22 09:51:09", "last_updated": "2025-10-15 14:49:55", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.db2388.2022", "i18n": { "en": { "title": "Dateset of land use between Hekou and Longmen Intermediate Basin from 2013 to 2017", "ds_format": "", "ds_source": "1. Based on the thematic maps of land use, vegetation coverage and slope, the soil erosion intensity was classified according to the classification rules by using 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 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. 3. The vegetation coverage processing method is based on remote sensing estimation, and the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by using the pixel binary model method. Firstly, the NDVI of each pixel is calculated by using the near-infrared band and red band data of multispectral images, and then the vegetation coverage of the whole region is calculated by using the model, 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 estimated based on remote sensing are used for superposition operation to obtain each pixel Vegetation coverage information of each pixel. 4. Slope data processing method is based on 1:50000 DEM extraction.
", "ds_quality": "1. The remote sensing images are preprocessed by radiation correction, orthorectification, fusion and mosaic. 2. The 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. 3. Before remote sensing image interpretation, remote sensing image, typical survey and field comparison were used to establish the remote sensing interpretation marks of forest and grass plot. 4. Based on the remote sensing image, combined with the interpretation signs, extract the land use types. 5. Review of interpretation results: extract no less than 5% of the total map spots for verification. 6. The number of field verification samples and results 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": "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_time_res": "年", "ds_acq_place": "Helong section of sand and coarse sand national key control area", "ds_space_res": "2.0m", "ds_projection": "", "ds_process_way": "1. Based on the thematic maps of land use, vegetation coverage and slope, the soil erosion intensity was classified according to the classification rules by using 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 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. 3. The vegetation coverage processing method is based on remote sensing estimation, and the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by using the pixel binary model method. Firstly, the NDVI of each pixel is calculated by using the near-infrared band and red band data of multispectral images, and then the vegetation coverage of the whole region is calculated by using the model, 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 estimated based on remote sensing are used for superposition operation to obtain each pixel Vegetation coverage information of each pixel. 4. Slope data processing method is based on 1:50000 DEM extraction.
", "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": [ "地理学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "甘肃省定西市安定区，甘肃省庄浪县，宁夏回族自治区西吉县，甘肃省靖远县" ], "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": "1283337@qq.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" }