Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery

Abstract. The Qinghai–Tibet Plateau (QTP), known as the Third Pole of the Earth and the “water tower of Asia”, plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there are very limited data on vegetation types and their geographical distributions on the QTP due to the harsh natural environment. Currently, land cover and surface vegetation data are typically obtained using traditional classification methods for each period's product based on remote sensing information. These approaches do not consider the temporal continuity of vegetation presence, leading to a gradual increase in misclassified pixels and uncertainty in their locations, consequently decreasing the interpretability of the long-time-series remote sensing products. To address this issue, this study developed a new method for long-time continuous annual vegetation mapping based on reference vegetation maps and annual updates and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 83.27 %, with the reference annual 2020 data reaching an accuracy of 83.32 % and a kappa coefficient of 0.82. This study supports the use of remote sensing data for long-term continuous annual vegetation mapping. The 500 m annual vegetation maps are available at https://doi.org/10.11888/Terre.tpdc.301205 (Zhou et al., 2024).

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Publication Details

Journal
Earth system science data
Published
2025-02-28
DOI
https://doi.org/10.5194/essd-17-773-2025
Citations
9
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
15.45

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article

Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery

Lei Zhang, Mengzi Zhou, Guangsheng Zhou, Hongrui Ren et al.
9 citations
Earth system science data
Remote Sensing in Agriculture
15.45
article

Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery

Lei Zhang, Mengzi Zhou, Guangsheng Zhou, Hongrui Ren, Xiaomin Lv
article en
9 citations

Abstract

Abstract. The Qinghai–Tibet Plateau (QTP), known as the Third Pole of the Earth and the “water tower of Asia”, plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there are very limited data on vegetation types and their geographical distributions on the QTP due to the harsh natural environment. Currently, land cover and surface vegetation data are typically obtained using traditional classification methods for each period's product based on remote sensing information. These approaches do not consider the temporal continuity of vegetation presence, leading to a gradual increase in misclassified pixels and uncertainty in their locations, consequently decreasing the interpretability of the long-time-series remote sensing products. To address this issue, this study developed a new method for long-time continuous annual vegetation mapping based on reference vegetation maps and annual updates and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 83.27 %, with the reference annual 2020 data reaching an accuracy of 83.32 % and a kappa coefficient of 0.82. This study supports the use of remote sensing data for long-term continuous annual vegetation mapping. The 500 m annual vegetation maps are available at https://doi.org/10.11888/Terre.tpdc.301205 (Zhou et al., 2024).

Earth system science dataVol. 17(2)
Chinese Academy of Meteorological Sciences (CN), Taiyuan University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Remote Sensing in Agriculture
15.45
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