Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change

This paper presents the non-stationarity and autocorrelation (with a Moran’s I index score of 0.75) of the MODISretrieved aerosol optical depth (AOD) of the Wuhan agglomeration (WHA) in Central China, using geographically weighted regression (GWR) to identify the spatial relationships between AOD an...

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Main Authors: ZHANG, Wenting, HE, Qingqing, WANG, Haijun, CAO, Kai, HE, Sanwei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/5421
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6424&context=sis_research
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spelling sg-smu-ink.sis_research-64242020-12-11T06:23:58Z Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change ZHANG, Wenting HE, Qingqing WANG, Haijun CAO, Kai HE, Sanwei This paper presents the non-stationarity and autocorrelation (with a Moran’s I index score of 0.75) of the MODISretrieved aerosol optical depth (AOD) of the Wuhan agglomeration (WHA) in Central China, using geographically weighted regression (GWR) to identify the spatial relationships between AOD and its impact factors. In addition to the socio-economic factors, i.e., GDP and population, vegetation cover, elevation, land-use density and landscape metrics are also considered. Faced with the rapid process of urbanization and the impact of landuse change on AOD, which has been confirmed in previous studies, we propose an AOD prediction method, combining a land-use change simulation model, a cellular automata and Markov chain (CA-Markov) model, and spatial relationships built by GWR to represent the spatial distribution of AOD in 2030. The results suggest that the GWR model is able to address the spatially varying relationships, with an R-squared value, corrected Akaike’s information criterion (AICc), and standard residual better than those of the ordinary least squares (OLS) model. Land-use simulation, with an accuracy of 89.76%, indicates that an increase in the built-up area and a decrease in the forest area will be the major trends of land-use change and will lead to increased AOD. The AOD simulation results indicate that the most developed areas, i.e., the cities of Wuhan and Huangshi, will be the AOD increase hot spots in the WHA. This study provides an alternative method to identify the varying spatial relationships between AOD and its impact factors. A spatial prediction method for AOD is developed from the perspective of land-use change, which will help land-use planners in decision making. 2018-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5421 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6424&context=sis_research http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Aerosol optical depth Wuhan agglomeration Geographically weighted regression Land use Databases and Information Systems Demography, Population, and Ecology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Aerosol optical depth
Wuhan agglomeration
Geographically weighted regression
Land use
Databases and Information Systems
Demography, Population, and Ecology
spellingShingle Aerosol optical depth
Wuhan agglomeration
Geographically weighted regression
Land use
Databases and Information Systems
Demography, Population, and Ecology
ZHANG, Wenting
HE, Qingqing
WANG, Haijun
CAO, Kai
HE, Sanwei
Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
description This paper presents the non-stationarity and autocorrelation (with a Moran’s I index score of 0.75) of the MODISretrieved aerosol optical depth (AOD) of the Wuhan agglomeration (WHA) in Central China, using geographically weighted regression (GWR) to identify the spatial relationships between AOD and its impact factors. In addition to the socio-economic factors, i.e., GDP and population, vegetation cover, elevation, land-use density and landscape metrics are also considered. Faced with the rapid process of urbanization and the impact of landuse change on AOD, which has been confirmed in previous studies, we propose an AOD prediction method, combining a land-use change simulation model, a cellular automata and Markov chain (CA-Markov) model, and spatial relationships built by GWR to represent the spatial distribution of AOD in 2030. The results suggest that the GWR model is able to address the spatially varying relationships, with an R-squared value, corrected Akaike’s information criterion (AICc), and standard residual better than those of the ordinary least squares (OLS) model. Land-use simulation, with an accuracy of 89.76%, indicates that an increase in the built-up area and a decrease in the forest area will be the major trends of land-use change and will lead to increased AOD. The AOD simulation results indicate that the most developed areas, i.e., the cities of Wuhan and Huangshi, will be the AOD increase hot spots in the WHA. This study provides an alternative method to identify the varying spatial relationships between AOD and its impact factors. A spatial prediction method for AOD is developed from the perspective of land-use change, which will help land-use planners in decision making.
format text
author ZHANG, Wenting
HE, Qingqing
WANG, Haijun
CAO, Kai
HE, Sanwei
author_facet ZHANG, Wenting
HE, Qingqing
WANG, Haijun
CAO, Kai
HE, Sanwei
author_sort ZHANG, Wenting
title Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
title_short Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
title_full Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
title_fullStr Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
title_full_unstemmed Factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
title_sort factor analysis for aerosol optical depth and its prediction from the perspective of land-use change
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/5421
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6424&context=sis_research
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