{
    "created": "2025-07-04 16:42:57",
    "updated": "2026-04-12 09:00:45",
    "id": "1c40f5a3-2b4a-46e7-8e5b-7b6be101278c",
    "version": 0,
    "ds_topic": null,
    "title_cn": "2024年祁连山黑河国家级重点预防区土壤侵蚀数据集",
    "title_en": "Soil erosion data set of Heihe national key prevention area in Qilian Mountains in 2024",
    "ds_abstract": "<p>2024年祁连山黑河国家级重点预防区土壤侵蚀数据集包括内蒙古自治区额济纳旗，甘肃省永登县、天祝县、甘州区、肃南县、民乐县、临泽县、高台县、金塔县，青海省门源回族自治县、祁连县2024年的土壤侵蚀统计表和土壤侵蚀图，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>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。</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.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_acq_start_time": "2024-01-01 00:00:00",
    "ds_acq_end_time": "2024-12-31 00:00:00",
    "ds_acq_place": "祁连山黑河国家级重点预防区",
    "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": 1674077,
    "ds_files_count": 2,
    "ds_format": "xlsx",
    "ds_space_res": null,
    "ds_time_res": "",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "1c40f5a3-2b4a-46e7-8e5b-7b6be101278c.png",
    "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": "",
    "subject_codes": [],
    "quality_level": 3,
    "publish_time": "2025-08-27 11:22:00",
    "last_updated": "2025-10-13 09:45:21",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.HHSTBC.DB6953.2025",
    "i18n": {
        "en": {
            "title": "Soil erosion data set of Heihe national key prevention area in Qilian Mountains in 2024",
            "ds_format": "",
            "ds_source": "<pre><code>                                              &lt;ol&gt;\n</code></pre>\n<li>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.</li>\n</ol>",
            "ds_quality": "<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_way": "",
            "ds_abstract": "<pre><code> &lt;p&gt;The soil erosion data set of Heihe national key prevention area in Qilian Mountain in 2024 includes the soil erosion statistical tables of Ejina Banner, Inner Mongolia Autonomous Region, Yongdeng County, Tianzhu County, Ganzhou District, Sunan County, Minle County, Linze County, Gaotai County, Jinta County, Menyuan Hui Autonomous County and Qilian County in Gansu Province in 2024, based on the processing of satellite remote sensing image with spatial resolution of 2m, The storage format is xlsx, and 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 2023. 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 prevention area of Heihe River in Qilian Mountain",
            "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",
    "ds_topic_tags": [
        "土壤侵蚀统计表，土壤侵蚀面积，各级土壤侵蚀强度面积及比例，风力侵蚀"
    ],
    "ds_subject_tags": [],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "祁连山黑河国家级重点预防区，内蒙古自治区，甘肃省，青海省"
    ],
    "ds_time_tags": [],
    "ds_contributors": [
        {
            "true_name": "黄河流域水土保持生态环境监测中心",
            "email": "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_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"
}