탄소흡수원으로서 북한 조림 CDM사업이 갖는 효과분석-잎면적지수를 이용한 원격탐사기법을 중심으로

Abstract

The Kyoto Protocol is the result of international efforts to minimize global warming by reducing the greenhouse gas emission and developing CDM (Clean Development Mechanism). LULUCF (Land-Use, and Land-Use Change and Forestry) activities which include Afforestation, Reforestation, Forest Management are proposed for CDM project. Thus LULUCF activities are mainly about the land use/land cover change, and the accurate LU/LC map is extremely important to verify the validity of LULUCF activities. Remote sensing technology can be used to analyze the effect of a landscape change on a global carbon budget by estimating the Leaf-Area-Index (LAI) of satellite imagery because the spectral characteristics of a forest are determined by the leaf shape and arrangement of a tree canopy. As North Korea has experienced intensive deforestation for last few decades, the CDM Afforestation/Reforestation project in North Korea would be a great opportunity to restore the significantly degraded environment of North Korea. The main research objective is to analyze the forest land cover change and the LAI change on the forest cover between 1990 and 2002 Landsat images to test the feasibility of a LAI modeling of the past Landsat images based on the current reference data set (MODIS LAI/fPAR image and in situ LAI) and SOM (Self-Organizing Map)-based geocomputational algorithm as a better monitoring methodology of CDM Afforestation / Reforestation project in North Korea. The results of this study show the continuous destruction of North Korea forest and the applicability of the scaling-up LAI modeling from in situ LAI and the scaling-down LAI modeling from MODIS LAI/fPAR image. The forest cover of the study area, a Landsat image (Path 117, Row 33), have been reduced from 1990 to 2002 and the modeled LAI values of 2002 Landsat are lower than that of 1990 Landsat. The applicability of SOM-based geocomputational method for the scaling-up/-down LAI modeling of Landsat image is proved as all three output images show consistent results. The Landsat imageries used in this study are thoroughly preprocessed to remove the atmospheric/topographic distortions of the spectral reflectance of Landsat imagery by using MODTRAN and SCS+C algorithms. The WDVI comparison for the LAI change analysis is not a direct LAI modeling, but is an indirect LAI modeling based on the theoretical assumption of linear relationship between in situ LAI and WDVI validated in the previous study. The in situ LAI data is collected in Kwang-Neung Forest of South Korea by using LAI-2000 PCA, so the in situ LAI modeling of Landsat image is based on a reference data not collected in North Korea forest. Only the MODIS-based LAI modeling is a direct LAI modeling methodology based on the reference data estimated in the study area. The significant finding of this study is that the methodologies tested in this study show a consistent result, despite of the different theoretical assumption and reference data set. Due to the limited availability of the reference data of the study area and the few sampling points of in situ LAI from Kwang-Neung Forest, this study evaluates the feasibility of the scaling-up/-down LAI modeling based on in situ LAI and MODIS LAI. The next study should elaborate the scaling-up/-down LAI modeling with more comprehensive reference data of North Korea forest and attempt to differentiate deciduous and coniferous forest and then model the expected LAI value separately. In the actual CDM A/R project of North Korea, the full accessibility to North Korea forest is expected, so more accurate LAI modeling would be possible with more on-site measurements of reference data including in situ LAI. This study will help the policy-makers and CDM project managers in the preparation of Afforestation/Reforestation project in North Korea by providing the information of the historical carbon cycle of North Korea forest and empirically tested monitoring methodology of the expected carbon sink from the CDM A/R project of North Korea.