An annual 30 m cultivated-pasture dataset of the Tibetan Plateau from 1988 to 2021

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Izdano u:Earth System Science Data vol. 17, no. 6 (2025), p. 2933
Glavni autor: Han, Binghong
Daljnji autori: Bi, Jian, Tao, Shengli, Yang, Tong, Tang, Yongli, Ge, Mengshuai, Wang, Hao, Jin, Zhenong, Dong, Jinwei, Zhibiao Nan, Jin-Sheng, He
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Copernicus GmbH
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100 1 |a Han, Binghong  |u State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China 
245 1 |a An annual 30 m cultivated-pasture dataset of the Tibetan Plateau from 1988 to 2021 
260 |b Copernicus GmbH  |c 2025 
513 |a Journal Article 
520 3 |a Cultivated pastures have rapidly developed across the Tibetan Plateau over the past several decades, raising concerns about grassland degradation. Accordingly, considerable attention is paid to the protection of Tibetan grassland ecosystems. However, high-resolution spatial distribution of cultivated pastures on the Tibetan Plateau remains poorly understood, primarily due to the difficulty in discriminating cultivated pastures from other land cover types using remote sensing techniques. The absence of such information hinders efficient agricultural and livestock husbandry management, making it challenging to support ecological protection and restoration efforts. Here, we mapped the cultivated pastures on the Tibetan Plateau at a 30 m resolution for the years&#xa0;1988 to&#xa0;2021 using Landsat data from the Google Earth Engine (GEE) cloud computing platform. We built a random forest&#xa0;(RF) binary classification model with inputs of the spectral–temporal metrics of Landsat data acquired in the growing season, as well as ancillary topographic data. The model was trained using carefully selected training samples and was validated against 2000&#xa0;independent random reference points in two pilot study regions with different climates and landscapes. The model achieved an overall accuracy of 97.05 % <inline-formula>±</inline-formula> 0.4 % and an <inline-formula>F1</inline-formula>&#xa0;spatial consistency score of 82.51 % <inline-formula>±</inline-formula> 14.22 % (precision: 90.04 % <inline-formula>±</inline-formula> 6.18 %; recall: 76.74 % <inline-formula>±</inline-formula> 9.91 %), suggesting high confidence in mapping the distribution of cultivated pastures on the plateau. Using the RF&#xa0;model, we then produced a dataset of cultivated-pasture maps for the years from&#xa0;1988 to&#xa0;2021 for Qinghai Province and the Tibet Autonomous Region on the Tibetan Plateau, covering 77 % of the plateau. At both the province and county levels, the cultivated-pasture areas estimated in this study matched well with government statistics for recent years. The area of cultivated pastures on the Tibetan Plateau experienced a significant expansion from 0.46 Mha in&#xa0;1988 to 1.57 Mha in&#xa0;2021, with an average annual growth of <inline-formula>33.5±2.5</inline-formula> Kha. To our knowledge, we are the first to map cultivated pastures on the Tibetan Plateau, and our RF&#xa0;binary classification approach holds promise in identifying cultivated pastures in other regions of the world, which could prove to be invaluable for scientists, policymakers, ecological conservation practitioners, and herdspeople. The dataset is available on Zenodo at 10.5281/zenodo.14271782 (Han et al., 2024). 
651 4 |a China 
651 4 |a Qinghai China 
651 4 |a Canada 
651 4 |a Tibetan Plateau 
653 |a Classification 
653 |a Landsat 
653 |a Pasture 
653 |a Grasslands 
653 |a Remote sensing 
653 |a Sensing techniques 
653 |a Animal husbandry 
653 |a Datasets 
653 |a Spatial distribution 
653 |a Growing season 
653 |a Environmental degradation 
653 |a Land cover 
653 |a Remote sensing techniques 
653 |a Algorithms 
653 |a Pastures 
653 |a Accuracy 
653 |a Plateaus 
653 |a Data acquisition 
653 |a Pilot projects 
653 |a Biodiversity 
653 |a Maps 
653 |a Landsat satellites 
653 |a Vegetation 
653 |a Cloud computing 
653 |a Climate models 
653 |a Livestock 
653 |a Cultivation 
653 |a Climate change 
653 |a Environmental 
700 1 |a Bi, Jian  |u College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China 
700 1 |a Tao, Shengli  |u Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China 
700 1 |a Yang, Tong  |u College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China 
700 1 |a Tang, Yongli  |u College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China 
700 1 |a Ge, Mengshuai  |u Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China 
700 1 |a Wang, Hao  |u State Key Laboratory of Seed Innovation and Grassland Agro-ecosystems, and College of Ecology, Lanzhou University, Lanzhou 730000, China 
700 1 |a Jin, Zhenong  |u Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China 
700 1 |a Dong, Jinwei  |u Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 
700 1 |a Zhibiao Nan  |u State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China 
700 1 |a Jin-Sheng, He  |u State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China; Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China 
773 0 |t Earth System Science Data  |g vol. 17, no. 6 (2025), p. 2933 
786 0 |d ProQuest  |t Engineering Database 
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