Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations
Abstract The Mongolian Plateau (MP), with its unique geographical landscape and nomadic cultural features, is vital to regional ecological security and sustainable development in North Asia. Existing global land cover products often lack the classification specificity and temporal continuity require...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05648-8 |
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| author | Juanle Wang Kai Li Tengfei Han Yifei Sun Mengmeng Hong Yating Shao Zhichen Sun Meng Liu Fengjiao Li Yuhui Su Qilin Jia Yaping Liu Jiazhuo Liu Jiawei Jiang Altansukh Ochir Davaadorj Davaasuren Mengqiong Xu Yamin Sun Shaopu Huang Weihao Zou Feiran Sun |
| author_facet | Juanle Wang Kai Li Tengfei Han Yifei Sun Mengmeng Hong Yating Shao Zhichen Sun Meng Liu Fengjiao Li Yuhui Su Qilin Jia Yaping Liu Jiazhuo Liu Jiawei Jiang Altansukh Ochir Davaadorj Davaasuren Mengqiong Xu Yamin Sun Shaopu Huang Weihao Zou Feiran Sun |
| author_sort | Juanle Wang |
| collection | DOAJ |
| description | Abstract The Mongolian Plateau (MP), with its unique geographical landscape and nomadic cultural features, is vital to regional ecological security and sustainable development in North Asia. Existing global land cover products often lack the classification specificity and temporal continuity required for MP-specific studies, particularly for grassland and bare area subtypes. To address this gap, a new land cover classification was designed for MP, which includes 14 categories: forests, shrubs, meadows, real steppes, dry steppes, desert steppes, wetlands, water, croplands, built-up land, barren land, desert, sand, and ice. Using machine learning and cloud computing, the novel dataset spanning the period of 1990–2020. Random Forest algorithm was employed to integrate training samples with multisource features for landcover classification, and a two-step Random Forest classification strategy to improve detail land cover results in transition regions. This process involved accurately annotating 64,345 sample points within a gridded framework. The resulting dataset achieved an overall accuracy of 83.6%. This land cover product and its approach has potential for application in vast arid and semi-arid areas. |
| format | Article |
| id | doaj-art-b7c7897f55d446678fea6d80c08dc84e |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-b7c7897f55d446678fea6d80c08dc84e2025-08-20T03:04:16ZengNature PortfolioScientific Data2052-44632025-08-0112111510.1038/s41597-025-05648-8Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample AnnotationsJuanle Wang0Kai Li1Tengfei Han2Yifei Sun3Mengmeng Hong4Yating Shao5Zhichen Sun6Meng Liu7Fengjiao Li8Yuhui Su9Qilin Jia10Yaping Liu11Jiazhuo Liu12Jiawei Jiang13Altansukh Ochir14Davaadorj Davaasuren15Mengqiong Xu16Yamin Sun17Shaopu Huang18Weihao Zou19Feiran Sun20State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesEnvironmental Engineering Laboratory, Department of Environment and Forest Engineering, School of Engineering and Technology, National University of MongoliaDepartment of Geography, School of Art and Sciences, National University of MongoliaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesAbstract The Mongolian Plateau (MP), with its unique geographical landscape and nomadic cultural features, is vital to regional ecological security and sustainable development in North Asia. Existing global land cover products often lack the classification specificity and temporal continuity required for MP-specific studies, particularly for grassland and bare area subtypes. To address this gap, a new land cover classification was designed for MP, which includes 14 categories: forests, shrubs, meadows, real steppes, dry steppes, desert steppes, wetlands, water, croplands, built-up land, barren land, desert, sand, and ice. Using machine learning and cloud computing, the novel dataset spanning the period of 1990–2020. Random Forest algorithm was employed to integrate training samples with multisource features for landcover classification, and a two-step Random Forest classification strategy to improve detail land cover results in transition regions. This process involved accurately annotating 64,345 sample points within a gridded framework. The resulting dataset achieved an overall accuracy of 83.6%. This land cover product and its approach has potential for application in vast arid and semi-arid areas.https://doi.org/10.1038/s41597-025-05648-8 |
| spellingShingle | Juanle Wang Kai Li Tengfei Han Yifei Sun Mengmeng Hong Yating Shao Zhichen Sun Meng Liu Fengjiao Li Yuhui Su Qilin Jia Yaping Liu Jiazhuo Liu Jiawei Jiang Altansukh Ochir Davaadorj Davaasuren Mengqiong Xu Yamin Sun Shaopu Huang Weihao Zou Feiran Sun Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations Scientific Data |
| title | Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations |
| title_full | Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations |
| title_fullStr | Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations |
| title_full_unstemmed | Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations |
| title_short | Long-term Land Cover Dataset of the Mongolian Plateau Based on Multi-source Data and Rich Sample Annotations |
| title_sort | long term land cover dataset of the mongolian plateau based on multi source data and rich sample annotations |
| url | https://doi.org/10.1038/s41597-025-05648-8 |
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