六盘山国家级重点预防区林草植被覆盖度统计表数据集包括甘肃省华亭县和宁夏回族自治区泾源县等区域2013年、2014年、2015年、2016年和2017年的林草植被覆盖度，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＋林草植被覆盖度统计表”的形式，如“××重点治理区××县××年林草植被覆盖度统计表”。植被覆盖度划分为高覆盖度、中高覆盖度、中覆盖度、中低覆盖度、低覆盖度5级。
", "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。\n2．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。\n3．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。\n4．基于遥感影像，结合解译标志，提取土地利用类型。\n5．解译结果复查：抽取不少于总图斑的5%进行核查。\n6．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。\n
", "ds_acq_start_time": "2013-01-01 00:00:00", "ds_acq_end_time": "2017-12-31 00:00:00", "ds_acq_place": "六盘山国家级重点预防区", "ds_acq_lon_east": 106.87888888888888, "ds_acq_lat_south": 35.020833333333336, "ds_acq_lon_west": 106.18666666666667, "ds_acq_lat_north": 35.79333333333333, "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": "xlsx", "ds_space_res": "2.0m", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "高斯—克吕格", "ds_thumbnail": "b3d18e6a-f480-4232-b80c-7fa6d7961bbe.jpg", "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": [], "quality_level": 3, "publish_time": "2021-10-27 14:10:09", "last_updated": "2025-10-15 14:49:23", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.HHSTBC.2020.189", "license": null, "i18n": { "en": { "title": "Statistical table data set of forest and grass vegetation coverage in Liupanshan national key prevention area from 2013 to 2017", "ds_format": "", "ds_source": "The data sources are ZY-3 and GF-1 satellite images, which are mainly obtained from the information center of the Ministry of water resources.
", "ds_quality": "1. The remote sensing images are preprocessed by radiation correction, orthorectification, fusion and mosaic.\n2. 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.\n3. 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.\n4. Based on the remote sensing image, combined with the interpretation signs, extract the land use types.\n5. Review of interpretation results: extract no less than 5% of the total map spots for verification.\n6. 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.\n
", "ds_ref_way": "", "ds_abstract": "The statistical table data set of forest and grass vegetation coverage in Liupanshan national key prevention area includes the forest and grass vegetation coverage of Huating County of Gansu Province and Jingyuan County of Ningxia Hui Autonomous Region in 2013, 2014, 2015, 2016 and 2017. The data is obtained from satellite remote sensing images with spatial resolution of 2 meters, and the format is xlsx In the form of \"Administrative Region + year + statistical table of forest and grass vegetation coverage\", such as \"statistical table of forest and grass vegetation coverage in ×× key treatment area ×× County ××\". The vegetation coverage is divided into five levels: high coverage, medium and high coverage, medium and low coverage, and low coverage.
", "ds_time_res": "", "ds_acq_place": "Liupanshan national key prevention area", "ds_space_res": "2.0m", "ds_projection": "", "ds_process_way": "Based on the remote sensing estimation method, the normalized vegetation index (NDVI) is used to estimate the vegetation coverage by using the pixel binary model method. The method is to first calculate the NDVI of each pixel using the near-infrared 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 and vegetation coverage data obtained from remote sensing interpretation are superimposed to obtain the vegetation coverage information of each pixel. Finally, the vegetation coverage was classified according to the classification rules, and the statistical table of vegetation coverage was obtained.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "林草植被覆盖度统计表", "高、中高、中、中低、低林草不同覆盖度面积及比例，水力侵蚀区，风力侵蚀区，冻融侵蚀区" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "六盘山", "甘肃省", "宁夏回族自治区", "华亭县", "泾源县" ], "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": "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" }