{
    "created": "2022-08-23 09:43:57",
    "updated": "2026-04-29 00:57:27",
    "id": "c38d250b-29f3-4869-bdac-b375393556fe",
    "version": 5,
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    "title_cn": "2021年黄河多沙粗沙国家级重点治理区土壤侵蚀数据集",
    "title_en": "Data set of soil erosion in the national key control area of the Yellow River in 2021",
    "ds_abstract": "<p>2021年黄河多沙粗沙国家级重点治理区土壤侵蚀数据集包括山西省古交市、娄烦县、右玉县、静乐县、神池县、五寨县、岢岚县、河曲县、保德县、偏关县、吉县、乡宁县、大宁县、隰县、永和县、蒲县、汾西县、离石区、兴县、临县、柳林县、石楼县、岚县、方山县、中阳县、交口县， 内蒙古托克托县、和林格尔县、清水河县、东胜区、达拉特旗、准格尔旗、鄂托克前旗、鄂托克旗、杭锦旗、乌审旗、伊金霍洛旗、磴口县、凉城县，陕西省韩城市、宝塔区、安塞县、延长县、延川县、子长县、志丹县、吴起县、宜川县、榆阳区、神木市、府谷县、横山区、靖边县、定边县、绥德县、米脂县、佳县、吴堡县、清涧县、子洲县，甘肃省泾川县、灵台县、西峰区、庆城县、环县、华池县、合水县、宁县、镇原县，宁夏回族自治区盐池县70个县2021年的土壤侵蚀统计表，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。</p>",
    "ds_source": "<p>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。</p>",
    "ds_process_way": "<p>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。</p>",
    "ds_quality": "<p>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。</p>",
    "ds_acq_start_time": "2021-01-01 00:00:00",
    "ds_acq_end_time": "2021-12-31 00:00:00",
    "ds_acq_place": "黄河多沙粗沙国家级重点治理区",
    "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": 8911689,
    "ds_files_count": 2,
    "ds_format": "xlsx",
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    "ds_time_res": "",
    "ds_coordinate": "无",
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    "ds_thumbnail": "c38d250b-29f3-4869-bdac-b375393556fe.png",
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    "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": "2022-11-22 11:32:20",
    "last_updated": "2025-10-15 14:41:38",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.ncdc.hhstbc.db2578.2022",
    "i18n": {
        "en": {
            "title": "Data set of soil erosion in the national key control area of the Yellow River in 2021",
            "ds_format": "",
            "ds_source": "<pre><code>                                                                                                &lt;p&gt;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.&lt;/p&gt;\n</code></pre>",
            "ds_quality": "<pre><code>                                                                                                                &lt;ol&gt;\n</code></pre>\n<li>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.</li>\n</ol>",
            "ds_ref_way": "",
            "ds_abstract": "<pre><code> &lt;p&gt;In 2021, the soil erosion data set of the Yellow River Sandy and coarse sand national key control areas includes Gujiao City, Loufan County, Youyu County, jingle County, Shenchi County, Wuzhai County, Kelan County, Hequ County, Baode County, Pianguan County, Ji County, Xiangning County, Daning County, Xi county, Yonghe County, Pu County, Fenxi County, Lishi District, Xingxian County, Linxian County, Liulin County, Shilou County, Lan county Fangshan County, Zhongyang County, Jiaokou County, Inner Mongolia Tuoketuo County, Helingeer County, Qingshuihe County, Dongsheng District, Dalate Banner, Zhungeer banner, Etuokeqian banner, Etuoke Banner, Hangjin Banner, Wushen Banner, Yijinhuoluo banner, Dengkou County, Liangcheng County, Shaanxi Province, Hancheng City, Baota District, Ansai County, Yanchang County, Yanchuan County, Zichang County, Zhidan County, Wuqi County, Yichuan County Statistical table of soil erosion of Yuyang District, Shenmu City, Fugu County, Hengshan District, Jingbian County, Dingbian County, Suide County, Mizhi County, Jia County, Wubao County, Qingjian County, Zizhou County, Jingchuan County, Lingtai County, Xifeng District, Qingcheng County, Huan County, Huachi County, Heshui County, Ning County, Zhenyuan County of Gansu Province, and 70 counties of Yanchi County of Ningxia Hui Autonomous Region in 2021, Based on the satellite remote sensing image with a spatial resolution of 2 meters, the data are stored in xlsx format. The data are named in the form of \"key control 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.&lt;/p&gt;\n</code></pre>",
            "ds_time_res": "",
            "ds_acq_place": "National key control area of Yellow River with abundant and coarse sediment",
            "ds_space_res": "",
            "ds_projection": "",
            "ds_process_way": "<pre><code>                                                                                                &lt;ol&gt;\n</code></pre>\n<li>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.</li>\n</ol>",
            "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": [
        "黄河多沙粗沙国家级重点治理区，山西省，内蒙古，陕西省，甘肃省，宁夏回族自治区 项目支持信息"
    ],
    "ds_time_tags": [
        2021
    ],
    "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"
}