Preprints
https://doi.org/10.5194/essd-2023-327
https://doi.org/10.5194/essd-2023-327
19 Sep 2023
 | 19 Sep 2023
Status: this preprint is currently under review for the journal ESSD.

A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types

Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian

Abstract. The Tibetan Plateau (TP) hosts a variety of vegetation types ranging from broadleaved and needle-leaved forests at the lower altitudes and mesic areas to alpine grassland at the higher altitudes and xeric areas. Accurate and detailed mapping of the vegetation distribution on TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets of TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred as TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and 4 machine learning models using Google Earth Engine platform. Our dataset TP_LC10-2022 achieved an overall classification accuracy of 86.5 % with a Kappa coefficient of 0.854. By comparing with 4 existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree occupied 13.99 % of the TP region which was ignored in existing land cover datasets, and that shrublands occupied 4.63 % of the TP region characterized by distinct forms of deciduous shrublands and evergreen shrublands largely determined by topography and missed in existing land cover datasets. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in TP. The TP_LC10-2022 dataset and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8228112 and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023a) respectively. Additionally, the classification map can be viewed through https://cold-classifier.users.earthengine.app/view/tplc10-2022.

Xingyi Huang et al.

Status: open (until 04 Nov 2023)

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Xingyi Huang et al.

Data sets

A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, Feng Tian https://doi.org/10.5281/zenodo.8228112

A dataset of land cover samples over the Tibetan Plateau Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, Feng Tian https://doi.org/10.5281/zenodo.8227942

Xingyi Huang et al.

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Short summary
The Tibetan Plateau, with its diverse vegetation from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the TP_LC10-2022 map. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.