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Estimation of Aboveground Biomass Carbon Stock Using Landsat TM and Ratio Images - $k$NN algorithm and Regression Model Priority  

Yoo, Su-Hong (연세대학교 토목.환경공학과)
Heo, Joon (연세대학교 토목.환경공학과)
Jung, Jae-Hoon (연세대학교 토목.환경공학과)
Han, Soo-Hee (연세대학교 토목.환경공학과)
Kim, Kyoung-Min (국립산림과학원 산림자원정보과)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.19, no.2, 2011 , pp. 39-48 More about this Journal
Abstract
Global warming causes the climate change and makes severe damage to ecosystem and civilization Carbon dioxide greatly contributes to global warming, thus many studies have been conducted to estimate the forest biomass carbon stock as an important carbon storage. However, more studies are required for the selection and use of technique and remotely sensed data suitable for the carbon stock estimation in Korea In this study, the aboveground forest biomass carbon stocks of Danyang-Gun in South Korea was estimated using $k$NN($k$-Nearest Neighbor) algorithm and regression model, then the results were compared. The Landsat TM and 5th NFI(National Forest Inventory) data were prepared, and ratio images, which are effective in topographic effect correction and distinction of forest biomass, were also used. Consequently, it was found that $k$NN algorithm was better than regression model to estimate the forest carbon stocks in Danyang-Gun, and there was no significant improvement in terms of accuracy for the use of ratio images.
Keywords
$k$NN; regression model; NFI; carbon stock; ratio image;
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Times Cited By KSCI : 3  (Citation Analysis)
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