植被覆盖度是评估区域生态环境质量、水源涵养功能及土地利用变化的重要指标。本研究基于欧空局哨兵二号（Sentinel-2）遥感影像获取研究区 2019–2024 年的植被覆盖度数据。数据通过 NDVI（归一化植被指数）反演获得，并结合数字高程模型（DEM）、土地利用数据及气象资料进行综合分析。遥感影像空间分辨率可达 10 m，经过大气校正、几何校正和时序拼接处理，保证了数据的精度与连续性。该数据集能够全面反映 2019–2024 年研究区植被覆盖动态变化特征，为区域生态修复、水源涵养评估及可持续发展研究提供可靠支撑。
\n植被覆盖度数据通常通过遥感技术、无人机监测、方式获得。遥感卫星影像（如MODIS、Landsat等）常用于获取大范围的植被覆盖信息。数据的获取时间和频率可能不同，通常会选择在特定的季节（如生长季节）或者一年四季进行监测，以便对比分析植被变化。", "ds_source": "
本研究所用植被覆盖度数据基于欧空局哨兵二号（Sentinel-2）遥感影像获取，研究时间为 2020–2024 年。Sentinel-2 卫星搭载多光谱仪（MSI），覆盖 13 个光谱波段，其中红光与近红外波段空间分辨率为 10 m，能够满足植被指数反演的精度需求。遥感数据经过大气校正、几何校正及拼接处理，结合数字高程模型（DEM）、土地利用数据及气象资料作为辅助数据源。最终生成的植被覆盖度数据集具有空间分辨率高、时序连续性强的特点，为研究区植被动态变化与水源涵养功能评估提供可靠数据支撑。
", "ds_process_way": "本研究利用欧空局哨兵二号（Sentinel-2）遥感影像反演植被覆盖度。首先，对原始影像进行大气校正、几何校正，并进行云掩膜处理，以保证数据的准确性。接着，基于红光与近红外波段计算归一化植被指数（NDVI），并利用 NDVI 反演植被覆盖度。为了提高数据精度和处理效率，使用 ArcGIS 软件进行影像拼接、投影转换与空间分析最终得到研究区的植被覆盖度时序数据。该方法有效揭示了 2020–2024 年期间植被覆盖度的动态变化特征，为区域水源涵养与生态修复研究提供了精确的数据支持。
", "ds_quality": "本研究所用植被覆盖度数据基于欧空局哨兵二号（Sentinel-2）遥感影像获取，研究时间为 2020–2024 年。影像数据经过大气校正、几何校正和云掩膜处理，确保了数据的准确性与稳定性。植被覆盖度的计算基于 NDVI 反演，并使用 ArcGIS 进行数据拼接与空间分析，以提高数据的精度和完整性。
", "ds_acq_start_time": "2019-08-14 00:00:00", "ds_acq_end_time": "2024-06-25 00:00:00", "ds_acq_place": "木里煤田七号坑", "ds_acq_lon_east": 99.35, "ds_acq_lat_south": 38.166666666666664, "ds_acq_lon_west": 99.18333333333334, "ds_acq_lat_north": 38.266666666666666, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 94996468, "ds_files_count": 0, "ds_format": "*.tif,*.xlsx,*.docx", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c22539a3-b786-409c-bda3-8e3ccd99ec1d.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "5b99d600-008a-4069-8fc3-7adb9c3f2f8b", "ds_serv_man": "孙新建", "ds_serv_phone": "13997064591", "ds_serv_mail": "sunxj@qhu.edu.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-07-06 16:07:16", "last_updated": "2026-07-06 16:07:16", "protected": false, "protected_to": "2027-10-01 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "Vegetation coverage dataset of Juhugeng No. 7 Well in Muli Kuangqu, Qinghai Province (2019-2024)", "ds_format": "*.tif,*.xlsx,*.docx", "ds_source": "The vegetation coverage data used in this study was obtained from Sentinel-2 remote sensing images of the European Space Agency, and the research period was from 2020 to 2024. The Sentinel-2 satellite is equipped with a Multi Spectral Spectrometer (MSI), covering 13 spectral bands, with a spatial resolution of 10 meters for the red and near-infrared bands, which can meet the accuracy requirements for vegetation index inversion. Remote sensing data undergoes atmospheric correction, geometric correction, and stitching processing, combined with digital elevation models (DEM), land use data, and meteorological data as auxiliary data sources. The final generated vegetation coverage dataset has the characteristics of high spatial resolution and strong temporal continuity, providing reliable data support for the assessment of vegetation dynamic changes and water conservation functions in the study area.
", "ds_quality": "The vegetation coverage data used in this study was obtained from Sentinel-2 remote sensing images of the European Space Agency, and the research period was from 2020 to 2024. The image data has undergone atmospheric correction, geometric correction, and cloud masking processing to ensure the accuracy and stability of the data. The calculation of vegetation coverage is based on NDVI inversion, and ArcGIS is used for data stitching and spatial analysis to improve the accuracy and completeness of the data.
", "ds_ref_way": "", "ds_abstract": "Vegetation coverage is an important indicator for assessing regional ecological environment quality, water conservation functions and land use changes. This study uses ESA Sentinel-2 remote sensing images to obtain vegetation coverage data for the study area from 2019 to 2024. The data was obtained through NDVI (normalized vegetation index) inversion and combined with digital elevation model (DEM), land use data and meteorological data for comprehensive analysis. The spatial resolution of remote sensing images can reach 10 m. After atmospheric correction, geometric correction and time series splicing processing, the accuracy and continuity of the data are ensured. This dataset can fully reflect the dynamic changes in vegetation cover in the study area from 2019 to 2024, and provide reliable support for regional ecological restoration, water conservation assessment and sustainable development research.
\r\nVegetation coverage data is usually obtained through remote sensing technology, drone monitoring, and methods. Remote sensing satellite images (such as MODIS, Landsat, etc.) are often used to obtain large-scale vegetation cover information. The time and frequency of data acquisition may vary, and monitoring is usually selected in specific seasons (such as growing seasons) or all year round to compare and analyze vegetation changes.", "ds_time_res": "", "ds_acq_place": "No. 7 Pit in Muli Coalfield", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This study used Sentinel-2 remote sensing images from the European Space Agency to invert vegetation coverage. Firstly, atmospheric and geometric corrections are applied to the original image, followed by cloud masking processing to ensure the accuracy of the data. Next, calculate the Normalized Difference Vegetation Index (NDVI) based on the red and near-infrared bands, and use NDVI to invert vegetation coverage. In order to improve data accuracy and processing efficiency, ArcGIS software was used for image stitching, projection conversion, and spatial analysis to obtain time-series data of vegetation coverage in the study area. This method effectively reveals the dynamic changes in vegetation coverage from 2020 to 2024, providing accurate data support for regional water conservation and ecological restoration research.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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, "belong_to_nieer": false, "ds_topic_tags": [ "遥感技术", "植被覆盖度", "NDVI", "地理信息系统（GIS）" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "青海省", "天峻县", "木里煤田七号坑" ], "ds_time_tags": [ 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "孙新建", "email": "sunxj@qhu.edu.cn", "work_for": "青海大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "孙新建", "email": "sunxj@qhu.edu.cn", "work_for": "青海大学", "country": "中国" } ], "ds_managers": [ { "true_name": "孙新建", "email": "sunxj@qhu.edu.cn", "work_for": "青海大学", "country": "中国" } ], "category": "生态" }