本数据集是第一个由CLP上的检查坝形成的淤泥土地的矢量化数据集，将高分辨率和易于访问的Google Earth图像与基于对象的分类方法相结合。该不仅为准确评估拦水坝生态系统服务功能提供了基础信息，还有助于解读当前黄河输沙变化情况，规划未来水土保持工程。
\n坝地的矢量化数据集格式为 shapefile （.shp），其中每条记录都描绘为一个面，并包含后续属性：经度、纬度、坝地面积、坝地周长、沉积物量和沉积物质量。在属性表中，字段 Area （unit： m2） 和 Shape_Leng （单位：m） 表示坝地面的面积和周长，以WGS_1984_EASE_Grid_Global坐标计算；场容积（单位：m2）和质量（单位：m）表示检查坝的沉积物滞留量和质量。
", "ds_source": "本数据集基于谷歌地球上所有可用的 2016 年至 2020 年中国黄土高原五月份图像生成。
", "ds_process_way": "在本研究中，我们结合高分辨率且易于获取的谷歌地球图像，采用基于对象的分类方法，制作了第一个中原河谷拦河坝矢量化数据集。我们首先调查和分析了拦河坝的主要特征，并获得了最佳提取期的 0.3-1.0 米分辨率谷歌地球图像。然后，我们通过多尺度分割、阈值分类和河网叠加等方法初步获得了粗略的拦河坝图层。最后，利用自主开发的人机交互程序，结合辅助数据、可视化解释和专家知识，提高了拦河坝的分类精度。
", "ds_quality": "汇总了这两个区域的坝地层，并通过实地调查和谷歌地球获得的验证样本验证了其准确性。通过1947个采集的试验样本验证了数据集的准确性，坝地生产者准确率和用户准确率分别为88.9%和99.5%。
", "ds_acq_start_time": "2016-05-01 00:00:00", "ds_acq_end_time": "2020-05-31 00:00:00", "ds_acq_place": "黄土高原 ", "ds_acq_lon_east": 114.0, "ds_acq_lat_south": 33.0, "ds_acq_lon_west": 100.0, "ds_acq_lat_north": 41.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 282116593, "ds_files_count": 2, "ds_format": "shp", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c0c7b62c-d488-49f5-958b-6aad5b5d77f8.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "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": "2024-05-22 09:29:58", "last_updated": "2026-01-14 10:55:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6470.2024", "i18n": { "en": { "title": "Vectorization dataset of Chinese Loess Plateau dam extracted based on object classification method", "ds_format": "shp", "ds_source": "This dataset is generated based on all available images of the Loess Plateau in China from May 2016 to 2020 on Google Earth.
", "ds_quality": "The dam strata of these two regions were summarized and their accuracy was verified through field investigations and validation samples obtained from Google Earth. The accuracy of the dataset was verified through 1947 collected experimental samples, with producer accuracy and user accuracy of 88.9% and 99.5%, respectively.
", "ds_ref_way": "", "ds_abstract": "This dataset is the first vectorized dataset of silt soil formed by inspection dams on CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. This not only provides basic information for accurately assessing the ecosystem service functions of water retaining dams, but also helps to interpret the current changes in sediment transport in the Yellow River and plan future soil and water conservation projects.
\nThe vectorized dataset format for dam sites is shapefile (. shp), where each record is depicted as a face and includes subsequent attributes such as longitude, latitude, dam site area, dam site perimeter, sediment quantity, and sediment quality. In the attribute table, the fields Area (unit: m2) and Shape_Leng (unit: m) represent the area and perimeter of the dam surface, calculated in WGS1984-EASE-GrideGlobal coordinates; The field volume (unit: m2) and mass (unit: m) represent the sediment retention and mass of the inspection dam.
", "ds_time_res": "", "ds_acq_place": "loess plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "In this study, we combined high-resolution and easily accessible Google Earth images and used object-based classification methods to create the first vectorized dataset of the Central Plains River Valley Barrage. We first investigated and analyzed the main features of the dam, and obtained Google Earth images with a resolution of 0.3-1.0 meters for the optimal extraction period. Then, we obtained a rough layer of the dam using methods such as multi-scale segmentation, threshold classification, and river network overlay. Finally, utilizing self-developed human-computer interaction programs, combined with auxiliary data, visual interpretation, and expert knowledge, the classification accuracy of the dam was improved.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "面向对象分类", "黄土高原", "拦河坝", "阈值分割" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄土高原" ], "ds_time_tags": [ 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "方怒放", "email": "fnf@ms.iswc.ac.cn", "work_for": "中国科学院水土保持与生态环境研究中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "方怒放", "email": "fnf@ms.iswc.ac.cn", "work_for": "中国科学院水土保持与生态环境研究中心", "country": "中国" } ], "ds_managers": [ { "true_name": "方怒放", "email": "fnf@ms.iswc.ac.cn", "work_for": "中国科学院水土保持与生态环境研究中心", "country": "中国" } ], "category": "水土保持" }