위성영상을 활용한 지상부 산림바이오매스 탄소량 추정 - k-Nearest Neighbor 및 Regression Tree Analysis 방법의 비교 분석 - = Estimation of Aboveground Forest Biomass Carbon Stock by Satellite Remote Sensing - A Comparison between k-Nearest Neighbor and Regression Tree Analysis -

국문 초록 (Abstract)

최근 주기적이고 정확한 산림바이오매스 탄소저장량 추정에 대한 필요성이 한국에서도 점차 증가하고 있다. 본 연구에서는 k-Nearest Neighbor (kNN) 및 Regression Tree Analysis (RTA) 알고리즘을 대상으로 공주 및 세종시를 대상으로 한 탄소량 변화 탐지를 통해 그 효용성을 비교·분석 하고자 하였다. 현장 자료로는 제 3차 및 제 5, 6차 국가산림자원조사 자료를 이용하였으며, 위성영상자료는 1992년, 2010년에 취득된 Landsat TM과 2009년에 취득된 Aster 영상을 이용하였다. 또한, 추정정확도를 향상시키기 위해 각 영상으로부터 다양한 식생지수를 생성하였다. 두 방법론의 비교를 위해 RMSE 및 평균편의(mean bias)를 포함한 각종 탄소통계량을 계산하였으며, 대상지역에 대한 탄소분포지도를 생성하고 비교를 수행하였다. 그 결과, kNN 알고리즘은 영상에 상관없이 보다 안정적인 추정결과를 나타낸 반면, 스무딩 효과로 인해 탄소의 공간분포가 뚜렷하지 않은 단점이 발견되었다. RTA의 경우 평균편의 결과 및 탄소의 공간분포가 명확히 나타나는 장점이 있으나, 위성영상에 따라 탄소추정량에서 큰 차이를 나타내었다. 최종적으로 2009년 및 2010년 탄소지도에서 1992년 탄소지도를 차분한 탄소차분지도를 생성을 통해 공주시 및 세종시 지역의 산림 탄소저장량이 급격히 증가했음을 확인하였다.

다국어 초록 (Multilingual 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.

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