Characterizing the heterogeneous trends of land surface temperature at a local scale using Ensemble Empirical Mode Decomposition

Authors: Huimin Liu*, School of Urban Design, Wuhan University, Wuhan 430072, China; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China, Qingming Zhan, School of Urban Design, Wuhan University, Wuhan 430072, China; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China, Chen Yang, School of Urban Design, Wuhan University, Wuhan 430072, China; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China, Yinghui Xiao, School of Urban Design, Wuhan University, Wuhan 430072, China; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
Topics: Environment, Environmental Science, Human-Environment Geography
Keywords: trend, heterogeneity, TSLST, EEMD, local
Session Type: Poster
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download



Despite of the numerous studies regarding global warming, the temporal characteristics of temperature at a local scale is essential for the understanding of how urban thermal environment response to heterogeneous urbanization types. This study presents a workflow to extract the patterns and dynamics of heterogeneous trends from nonlinear and non-stationary Time Series Land Surface Temperature (TSLST) data by taking Wuhan, China as case study. The 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products from 2003 to 2017 are used to generate a TSLST dataset with continuous and smooth surfaces on the monthly basis through the non-parametric Multi-Task Gaussian Process Modeling (MTGP). k-means is then employed to segment the study area into multiple time series clusters so as to bridge with urban planning in terms of research and implementation scale. At last, the overall trends of the time series clusters are identified based on the residuals decomposed by the adaptive Ensemble Empirical Mode Decomposition (EEMD) method. The overall trends are grouped into three types by shape. The considerable heterogeneity of the trends is potentially caused by the inconsistent levels of localized urbanization, afforestation or circular economy development. This study facilitates the understanding of human-environment interactions. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.

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