气候变化领域集成服务门户(CCPortal): Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau

CCPortal

DOI	10.1016/j.ecolind.2023.110020
	Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau
	Lou, Peiqing; Wu, Tonghua; Yang, Sizhong; Wu, Xiaodong; Chen, Jianjun; Zhu, Xiaofan; Chen, Jie; Lin, Xingchen; Li, Ren; Shang, Chengpeng; Wang, Dong; La, Yune; Wen, Amin; Ma, Xin
发表日期	2023
ISSN	1470-160X
EISSN	1872-7034
卷号	148
英文摘要	Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988-2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 x 104 km2) and from alpine grassland to alpine meadow (17.43 x 104 km2) during 1988-2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influ-encing factors affecting vegetation greening on the QTP are precipitation (q -statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human develop-ment strategies.
关键词	Greening Land use cover change Deep learning Google Earth Engine Landsat Qinghai-Tibet Plateau
英文关键词	LAND-COVER; ALPINE GRASSLAND; NEURAL-NETWORKS; CLASSIFICATION; PERMAFROST; CHINA; INDEX; WATER; AGRICULTURE; DYNAMICS
WOS研究方向	Biodiversity Conservation ; Environmental Sciences
WOS记录号	WOS:000948489200001
来源期刊	ECOLOGICAL INDICATORS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282746
作者单位	Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Guilin University of Technology
推荐引用方式 GB/T 7714	Lou, Peiqing,Wu, Tonghua,Yang, Sizhong,et al. Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau[J],2023,148.
APA	Lou, Peiqing.,Wu, Tonghua.,Yang, Sizhong.,Wu, Xiaodong.,Chen, Jianjun.,...&Ma, Xin.(2023).Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau.ECOLOGICAL INDICATORS,148.
MLA	Lou, Peiqing,et al."Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau".ECOLOGICAL INDICATORS 148(2023).