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http://dx.doi.org/10.7780/kjrs.2009.25.4.311

Estimation of Forest Biomass based upon Satellite Data and National Forest Inventory Data  

Yim, Jong-Su (Department of Forest Resources, College of Forest Science, Kookmin University)
Han, Won-Sung (Department of Forest Resources, College of Forest Science, Kookmin University)
Hwang, Joo-Ho (Department of Forest Resources, College of Forest Science, Kookmin University)
Chung, Sang-Young (Department of Forest Resources, College of Forest Science, Kookmin University)
Cho, Hyun-Kook (Division of Forest Resource Information, Korea Forest Research Institute)
Shin, Man-Yong (Department of Forest Resources, College of Forest Science, Kookmin University)
Publication Information
Korean Journal of Remote Sensing / v.25, no.4, 2009 , pp. 311-320 More about this Journal
Abstract
This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.
Keywords
Forest biomass estimation; t-Nearest Neighbor; Regression model; Landsat TM; National Forest Inventory;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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