DOI: 10.1016/j.jag.2017.10.001
Scopus记录号: 2-s2.0-85036538637
论文题名: Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression
作者: Chu H ; -J ; , Kong S ; -J ; , Chang C ; -H
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2018
卷: 65 起始页码: 1
结束页码: 11
语种: 英语
英文关键词: EOF
; GTWR
; GWR
; Spatio-temporal variation
; Turbidity
Scopus关键词: mapping
; regression analysis
; satellite imagery
; spatial variation
; temporal variation
; turbidity
; water quality
英文摘要: The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space–time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis. © 2017 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79913
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; Department of Environmental Engineering, National Cheng Kung University, Tainan, Taiwan
Recommended Citation:
Chu H,-J,, Kong S,et al. Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression[J]. International Journal of Applied Earth Observation and Geoinformation,2018-01-01,65