Estimation of multi-scale urban vegetation coverage based on multi-source remote sensing images - Details

author：

Gao, Y.-G. (Gao, Y.-G..) ^[1] | Xu, H.-Q. (Xu, H.-Q..) ^[2]

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Scopus PKU CSCD

Abstract：

The　vegetation　coverage　from　multi-source　at　multi-scale　and　multi-source　at　the　same　scale　in　urban　area　was　studied.　The　Landsat　7　ETM+,　SPOT　5　and　IKONOS　remote　sensing　image　data　were　taken　as　the　data　source.　The　vegetation　coverage　with　different　spatial　resolutions　derived　from　a　1:500　topographic　map　as　the　reference　map　by　grid　method　was　taken　as　reference.　The　accuracies　of　fraction　vegetation　coverage　extracted　from　the　images,　wich　were　radiometrically　corrected　using　different　models,　were　compared.　An　optimal　radiometric　correction　model　for　the　extraction　of　fraction　vegetation　coverage　in　urban　areas　was　proposed.　The　results　show　that　ICM　model　is　the　best　radiometric　correction　model　for　estimating　fraction　vegetation　coverage　in　urban　area.　NDVI　is　the　best　vegetation　index　for　fraction　vegetation　coverage　estimation　for　high　resolution　remote　sensing　images,　while　the　best　vegetation　indices　for　estimating　fraction　vegetation　coverage　from　moderate　spatial　resolution　images　are　the　RVI　and　MSAVI.　For　the　studies　area,　the　GI　model　is　more　accurate　than　the　CR　model　in　estimating　the　vegetation　coverage.　©　2017,　Science　Press.　All　right　reserved.

Keyword：

Fraction vegetation cover; Multi scale; Radiometric correction model; Vegetation index

Community：

[ 1 ] [Gao, Y.-G.]College of Environment and Resources, Fuzhou University, Fuzhou, 350116, China
[ 2 ] [Gao, Y.-G.]Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou, 350116, China
[ 3 ] [Gao, Y.-G.]Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou, 350116, China
[ 4 ] [Gao, Y.-G.]Fujian Provincial Universities Engineering Research Center of Geological Engineering, Fuzhou, 350116, China
[ 5 ] [Xu, H.-Q.]College of Environment and Resources, Fuzhou University, Fuzhou, 350116, China
[ 6 ] [Xu, H.-Q.]Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou, 350116, China
[ 7 ] [Xu, H.-Q.]Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address：

[Gao, Y.-G.]College of Environment and Resources, Fuzhou UniversityChina

Email：

yggao@fzu.edu.cn

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Source ：

Journal of Infrared and Millimeter Waves

ISSN： 1001-9014

Year： 2017

Issue： 2

Volume： 36

Page： 225-234

0 . 3 8 7

JCR@2017

0 . 6 0 0

JCR@2023

ESI HC Threshold：170

JCR Journal Grade：4

CAS Journal Grade：4

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ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 0

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