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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 1988 to 2021 using Landsat data from the Google Earth Engine (GEE) cloud computing platform. We built a random forest (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 independent random reference points in two pilot study regions with different climates and landscapes. The model achieved an overall accuracy of 97.05 %
Details
Landsat;
Pasture;
Grasslands;
Remote sensing;
Sensing techniques;
Animal husbandry;
Datasets;
Spatial distribution;
Growing season;
Environmental degradation;
Land cover;
Remote sensing techniques;
Algorithms;
Pastures;
Accuracy;
Plateaus;
Data acquisition;
Pilot projects;
Biodiversity;
Maps;
Landsat satellites;
Vegetation;
Cloud computing;
Climate models;
Livestock;
Cultivation;
Climate change
; Tao, Shengli 3 ; Yang, Tong 2 ; Tang, Yongli 2 ; Ge, Mengshuai 3
; Wang, Hao 4
; Jin, Zhenong 3 ; Dong, Jinwei 5 ; Zhibiao Nan 1 ; Jin-Sheng, He 6
1 State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
2 College of Earth and Environmental Sciences, and Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China
3 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
4 State Key Laboratory of Seed Innovation and Grassland Agro-ecosystems, and College of Ecology, Lanzhou University, Lanzhou 730000, China
5 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
6 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