Estimation of Aboveground Forest Biomass Carbon Stock by Satellite Remote Sensing - A Comparison between k-Nearest Neighbor and Regression Tree Analysis -

Abstract

:Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 2) 연구자료 -6 54- 3) 탄소량 추정방법 Korean Journal of Remote Sensing, Vol.30, No.5, 2014 -6 56- Sensing, Vol.30, No.5, 2014 -6 58- 실험결과 및 분석

Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor

“…Overall, when k is more than 6, RMSE stays more stable, and the variation of relative RMSE that has k between 6 and 20 is less than 1.3% in all cases except for the autumn image (the variation was 3.6%). The accuracy levels also show agreement with an earlier AGB estimation for the Danyang area [ 56 ] and another, previous study in the same area [ 57 ].…”