利用最先进的遥感方法，包括哨兵-1 和哨兵-2 图像、环境和地形数据集，以及使用谷歌地球引擎平台的四个机器学习模型，绘制了一幅 10 米分辨率的 2022 年土地覆被图，其中包含 12 个植被类别和 3 个非植被类别（简称为 TP_LC10-2022）。", "ds_source": "
卫星数据：Sentinel-2 和 Sentinel-1。\n
地形数据：SRTM，用于创建水平精度为 16 m、垂直精度为 6 m 的全球高程模型，空间分辨率为 30 m。\n
降水数据：气候灾害组红外降水与站点数据（CHIRPS），是一个记录 1981 年至今全球降水的综合数据集。CHIRPS 将卫星图像与原位站数据集成在一起，允许以 0.05° 的分辨率生成适用于趋势分析和季节性干旱监测的网格化降雨时间序列。\n
温度数据：ERA5-Land，数据集提供了对土地变量的全面再分析，使用了 2022 年分辨率为 0.1° 的每小时温度数据。", "ds_process_way": "
土地覆盖分类：包括四个主要步骤：（1）采样策略，（2）数据预处理和特征构建，（3）分类模型比较，以及 （4）准确性评估和相互比较。", "ds_quality": "
TP_LC10-2022 在使用 RF 模型时实现了 86.5% 的总体准确率和 0.854% 的 kappa 系数，优于其他分类模型，包括 GTB、MD 和 SVM。TP_LC10-2022 与四种广泛使用的土地覆被产品（GLC_FCS30-2020、FROM_GLC30-2015、FROM_GLC10-2017 和 WorldCover2021）之间的比较表明，TP_LC10-2022 具有更高的整体精度，并反映了植被类型随纬度的局部尺度变化。", "ds_acq_start_time": "2022-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "青藏高原", "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": "login-access", "ds_total_size": 7718355825, "ds_files_count": 58, "ds_format": "excel,tif", "ds_space_res": null, "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "bd99f39f-81a5-4d3a-9054-d067e324cc34.jpg", "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": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-08-29 09:16:15", "last_updated": "2025-06-30 16:18:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6668.2024", "i18n": { "en": { "title": "A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types", "ds_format": "excel,tif", "ds_source": "
Satellite data: Sentinel-2 and Sentinel-1.\n
Terrain data: SRTM, used to create a global elevation model with a horizontal accuracy of 16 meters and a vertical accuracy of 6 meters, with a spatial resolution of 30 meters.\n
Precipitation Data: Climate Hazards Infrared Precipitation and Station Data (CHIRPS) is a comprehensive dataset that records global precipitation from 1981 to the present. CHIRPS integrates satellite imagery with in-situ station data, allowing for the generation of gridded rainfall time series suitable for trend analysis and seasonal drought monitoring at a resolution of 0.05 °.\n
Temperature data: ERA5 Land, the dataset provides a comprehensive reanalysis of land variables, using hourly temperature data with a resolution of 0.1 ° in 2022.", "ds_quality": "
TP_LC10-2022 achieved an overall accuracy of 86.5% and a kappa coefficient of 0.854% when using the RF model, outperforming other classification models including GTB, MD, and SVM. The comparison between TP_LC10-2022 and four widely used land cover products (GLC_FCS30-2020, FROMGLC30-2015, FROMGLC10-2017, and WorldCover2021) shows that TP_LC10-2022 has higher overall accuracy and reflects the local scale variation of vegetation types with latitude.", "ds_ref_way": "", "ds_abstract": "
Using state-of-the-art remote sensing methods, including Sentinel-1 and Sentinel-2 images, environmental and terrain datasets, and four machine learning models using the Google Earth Engine platform, a 10 meter resolution 2022 land cover map was created, which includes 12 vegetation categories and 3 non vegetation categories (referred to as TP_LC10-2022).
", "ds_time_res": "年", "ds_acq_place": "Qinghai Tibet Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Land cover classification: includes four main steps: (1) sampling strategy, (2) data preprocessing and feature construction, (3) classification model comparison, and (4) accuracy evaluation and mutual comparison.", "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": [ "青藏高原", "10m", "土地利用" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 2022 ], "ds_contributors": [ { "true_name": "田丰", "email": "tian.feng@whu.edu.cn", "work_for": "武汉大学遥感信息工程学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "田丰", "email": "tian.feng@whu.edu.cn", "work_for": "武汉大学遥感信息工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "田丰", "email": "tian.feng@whu.edu.cn", "work_for": "武汉大学遥感信息工程学院", "country": "中国" } ], "category": "生态" }