The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau  

作　　者：MA Dujuan WU Xiaodan WANG Jingping MU Cuicui 

机构地区：[1]College of Earth and Environmental Sciences,Lanzhou University,Lanzhou,730000,China [2]Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai,Guangdong,611930,China [3]University Cooperation of Polar Research,Beijing,100875,China

出　　处：《Journal of Geographical Sciences》2023年第5期924-944,共21页地理学报（英文版）

基　　金：The Second Tibetan Plateau Scientific Expedition and Research Program(STEP),No.2019QZKK0605;National Natural Science Foundation of China,No.42071296。

摘　　要：The trend estimate of vegetation change is essential to understand the change rule of the ecosystem.Previous studies were mainly focused on quantifying trends or analyzing their spatial distribution characteristics.Nevertheless,the uncertainties of trend estimates caused by spatiotemporal scale effects have rarely been studied.In response to this challenge,this study aims to investigate spatiotemporal scale effects on trend estimates using Moderate-Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Vegetation Index(NDVI)and Gross Primary Productivity(GPP)products from 2001 to 2019 in the Qinghai-Tibet Plateau(QTP).Moreover,the possible influencing factors on spatiotemporal scale effect,including spatial heterogeneity,topography,and vegetation types,were explored.The results indicate that the spatial scale effect depends more on the dataset with a coarser spatial resolution,and temporal scale effects depend on the time span of datasets.Unexpectedly,the trend estimates on the 8-day and yearly scale are much closer than that on the monthly scale.In addition,in areas with low spatial heterogeneity,low topography variability,and sparse vegetation,the spatiotemporal scale effect can be ignored,and vice versa.The results in this study help deepen the consciousness and understanding of spatiotemporal scale effects on trend detection.

关 键 词：spatiotemporal scale effect satellite dataset trend estimates NDVI influencing factors GPP 

分 类 号：X87[环境科学与工程—环境工程] Q948[生物学—植物学]