青藏高原拥有多种植被类型，从低海拔和中海拔地区的阔叶林和针叶林到高海拔和旱地的高山草原。准确详细地绘制青藏高原上的植被分布图对于更好地了解气候变化对陆地生态系统的影响至关重要。然而，现有的青藏高原土地覆被数据集要么空间分辨率较低，要么植被类型不足以表征某些独特的青藏高原生态系统，例如高山碎石。利用最先进的遥感方法，包括 Sentinel-1 和 Sentinel-2 图像、环境和地形数据集，以及使用 Google Earth Engine 平台的机器学习模型，制作了 2022 年（称为 TP_LC10-2022）的 10 m 分辨率 TP 土地覆盖图，其中包含 12 个植被类别和 3 个非植被类别。", "ds_source": "
数据源为Sentinel-1和Sentinel-2遥感数据、环境和地形数据集。", "ds_process_way": "
利用最先进的遥感方法，包括 Sentinel-1 和 Sentinel-2 图像、环境和地形数据集，以及使用 Google Earth Engine 平台的机器学习模型，制作了 2022 年（称为 TP_LC10-2022）的 10 m 分辨率 TP 土地覆盖图，其中包含 12 个植被类别和 3 个非植被类别。", "ds_quality": "
数据集TP_LC10-2022 的整体分类准确率为 86.5%，Kappa 系数为 0.854。通过与全球现有的4种土地覆盖产品进行比较，TP_LC10-2022在反映青藏高原东南部地区局部尺度的垂直变化方面表现出显著改善。此外，我们发现高寒碎石占据了青藏高原区域的13.99%，这在现有的土地覆盖数据集中被忽略了，灌木区占据了青藏高原区域的4.63%，其特征是落叶灌木和常绿灌木的形态不同，主要由地形决定，在现有的土地覆盖数据集中被遗漏。", "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": 105.0, "ds_acq_lat_south": 25.0, "ds_acq_lon_west": 65.0, "ds_acq_lat_north": 40.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 7826317190, "ds_files_count": 113, "ds_format": "tif、shp", "ds_space_res": "10m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "055a07de-e347-4353-8c58-4c3973421a6e.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": "2024-04-26 15:23:08", "last_updated": "2025-06-30 16:18:07", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6442.2024", "i18n": { "en": { "title": "10 meter resolution land cover map of the Qinghai Tibet Plateau (2022)", "ds_format": "tif、shp", "ds_source": "
The data sources are Sentinel-1 and Sentinel-2 remote sensing data, as well as environmental and terrain datasets.", "ds_quality": "
The overall classification accuracy of dataset TP_LC10-2022 is 86.5%, with a Kappa coefficient of 0.854. Compared with four existing land cover products worldwide, TP_LC10-2022 shows significant improvement in reflecting local scale vertical changes in the southeastern region of the Qinghai Tibet Plateau. In addition, we found that high-altitude gravel accounts for 13.99% of the Qinghai Tibet Plateau region, which has been overlooked in existing land cover datasets. The shrub area accounts for 4.63% of the Qinghai Tibet Plateau region, characterized by the different shapes of deciduous and evergreen shrubs, mainly determined by terrain, which have been overlooked in existing land cover datasets.", "ds_ref_way": "", "ds_abstract": "
The Qinghai Tibet Plateau has multiple vegetation types, ranging from broad-leaved and coniferous forests in low and medium altitude areas to high-altitude and arid alpine grasslands. Accurately and detailedly drawing vegetation distribution maps on the Qinghai Tibet Plateau is crucial for better understanding the impact of climate change on terrestrial ecosystems. However, existing land cover datasets on the Qinghai Tibet Plateau either have low spatial resolution or insufficient vegetation types to characterize certain unique ecosystems, such as high-altitude gravel. Using state-of-the-art remote sensing methods, including Sentinel-1 and Sentinel-2 images, environmental and terrain datasets, and machine learning models using the Google Earth Engine platform, a 10 m resolution TP land cover map for the year 2022 (referred to as TP_LC10-2022) was created, which includes 12 vegetation categories and 3 non vegetation categories.
", "ds_time_res": "年", "ds_acq_place": "The Qinghai Tibet Plateau", "ds_space_res": "10m", "ds_projection": "", "ds_process_way": "Using state-of-the-art remote sensing methods, including Sentinel-1 and Sentinel-2 images, environmental and terrain datasets, and machine learning models using the Google Earth Engine platform, a 10 m resolution TP land cover map for the year 2022 (referred to as TP_LC10-2022) was created, which includes 12 vegetation categories and 3 non vegetation categories.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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", "土地覆盖", "分类精度", "植被类型" ], "ds_subject_tags": [ "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" }