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Data Details

Spatial Distribution Dataset of Planted Forest Based on TS2-ViT Model in the “Two Rivers and Four Streams” Region of Xizang, China (2021)


PU Ping1FU Yingchun*1CHEN Ye1YU Muyan1HU Siyu1ZHANG Dongdong2,3SUN Siwen1YOU Wenqian1LI Linchun1ZHONG Dongping1
1 School of Geographic Sciences，South China Normal University，Guangzhou 510631，China2 Xizang Autonomous Region Climate Center，Lhasa 850000，China3 School of Ecology and Environment，Xizang University，Lhasa 850012，China

DOI:10.3974/geodb.2026.06.09.V1

Published:June 2026

Visitors：185       Data Files Downloaded：2      
Data Downloaded：1.74 MB      Citations：

Key Words：

Two Rivers and Four Streams,planted forests,remote sensing, Temporal-Spectral-Spatio Vision Transformer

Abstract：

The valley region along the “Two Rivers and Four Streams” in the Xizang Autonomous Region is a core area for human settlement, ecological restoration, and desertification control in the cold, high-altitude valleys of the Qinghai-Xizang Plateau; afforestation projects are of great significance to the construction of an ecological security barrier on the plateau. The Spatial Distribution Dataset of Planted Forest Based on TS2-ViT Model in the “Two Rivers and Four Streams” Region of Xizang, China (2021) was developed using Temporal-Spectral-Spatio Vision Transformer (TS2-ViT) model, integrated with Sentinel-1/2 images, forest age and planted forest sample data. The dataset includes: (1) spatial distribution of afforestation across the entire “Two Rivers and Four Streams” region in Xizang (30-m resolution); (2) afforestation data for the key study areas along the Nyangchu River, Lhasa River, and Yalong River (10-m resolution). The dataset is archived in .tif format, and consist of 26 data files with data size of 23.8 MB (compressed into one file with 892 KB).

Foundation Item：

National Natural Science Foundation of China (42571390, 42071399), Xizang Autonomous Region (XZ202501ZY0091, XZ202301ZY0021G.); Department of Science and Technology of Guangdong Province (2025A1515011807)；

Data Citation：

PU Ping, FU Yingchun*, CHEN Ye, YU Muyan, HU Siyu, ZHANG Dongdong, SUN Siwen, YOU Wenqian, LI Linchun, ZHONG Dongping. Spatial Distribution Dataset of Planted Forest Based on TS2-ViT Model in the “Two Rivers and Four Streams” Region of Xizang, China (2021)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.06.09.V1.
.

References：


     [1] Cheng, K., Yang, H., Guan, H., et al. Unveiling China’s natural and planted forest spatial-temporal dynamics from 1990 to 2020 [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 209: 37-50.
     [2] Cheng, K., Su, Y., Guan, H., et al. Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 196: 356-371.
     [3] Xiao, Y., Wang, Q., Zhang, H.K. Global natural and planted forests mapping at fine spatial resolution of 30 m [J]. Journal of Remote Sensing, 2024, 4: 0204.
     [4] Du, Z., Yu, L., Yang, J., et al. A global map of planting years of plantations [J]. Scientific Data, 2022, 9(1): 141.
     [5] John, A., Allotey, S., Koebe, T., et al. A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts [J]. Scientific Data, 2025, 12(1): 1714.
     [6] Naboureh, A., Li, A., Bian, J., et al. Accuracies, discrepancies, and challenges of the 10 m global land cover products in mountains [J]. GIScience, Remote Sensing, 2025, 62(1): 2556064.
     [7] G, Zhou., H, Ren., T, Liu., et al. A new regional vegetation mapping method based on terrain-climate- remote sensing and its application on the Qinghai-Tibet Plateau [J]. Science China Earth Sciences, 2023, 66(2), 237-246.
     [8] Clark, A., Phinn, S., Scarth, P. Optimised U-Net for land use-land cover classification using aerial photography [J]. PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023, 91(2): 125-147.
     [9] Tarasiou, M., Chavez, E., Zafeiriou, S. Vits for sits: Vision transformers for satellite image time series; proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, F [C]. 2023: 10418-10428.
     [10] Heidarianbaei, M., Kanyamahanga, H., Dorozynski, M. Temporal ViT-U-Net tandem model: enhancing multi-sensor land cover classification through transformer-based utilization of satellite image time series [J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2024, X-3-2024: 169-177.
     [11] Zhu, Z., Woodcock, C.E. Continuous change detection and classification of land cover using all available Landsat data [J]. Remote sensing of Environment, 2014, 144: 152-171.
     [12] Tolan, J., Yang, H.-.I., Nosarzewski, B., et al. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar [J]. Remote Sensing of Environment, 2024, 300: 113888.
     [13] Shang, R., Lin, X., Chen, J.M., et al. China's annual forest age dataset at 30 m spatial resolution from 1986 to 2022 [J]. Earth System Science Data Discussions, 2025, 2025: 1-31.
     [14] Running, S.W., Nemani, R.R., Heinsch, F.A., et al. A continuous satellite-derived measure of global terrestrial primary production [J]. Bioscience, 2004, 54(6): 547-560.

Data Product:

ID Data Name Data Size Operation
1 TRFS_PlantedForest_2021.rar 892.69KB
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