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

高原(数学) 卫星图像 卫星 系列(地层学) 植被(病理学) 遥感 地质学 气候学 环境科学 自然地理学 地理 医学 数学分析 古生物学 数学 病理 工程类 航空航天工程
作者
Guangsheng Zhou,Hongrui Ren,Lei Zhang,Xiaomin Lv,Mengzi Zhou
出处
期刊:Earth System Science Data [Copernicus Publications]
卷期号:17 (2): 773-797 被引量:2
标识
DOI:10.5194/essd-17-773-2025
摘要

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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