고해상도 원격탐사 자료와 기계학습을 이용한 한국 산림의탄소 저장량 산정
Estimation of Forest Carbon Stock in South Korea Using Machine Learning with High-Resolution Remote Sensing Data
0
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
Accurate estimation of forest carbon stocks is important in establishing greenhousegas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocksusing machine learning techniques based on high-resolution remote sensing data and detailedfield survey data. The high-resolution remote sensing data used in this study are Landsat indices(EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission(SRTM) data for describing topography. We also used the forest growing stock data from theNational Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built amodel based on machine learning methods and optimized for Korean forest types to calculatethe forest carbon stocks per grid unit. With the newly developed estimation model, we createdforest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result,forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore,we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 usingthe forest carbon stock map in this study. The surrounding forest destroyed around the fire areawas estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbonstocks and facilitates accounting of real-time changes in the carbon balance as well as managingthe LULUCF part of greenhouse gas inventories.
Abstract
Accurate estimation of forest carbon stocks is important in establishing greenhousegas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocksusing machine learning techniques based on high-resolution remote sensing data and detailedfield survey data. The high-resolution remote sensing data used in this study are Landsat indices(EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission(SRTM) data for describing topography. We also used the forest growing stock data from theNational Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built amodel based on machine learning methods and optimized for Korean forest types to calculatethe forest carbon stocks per grid unit. With the newly developed estimation model, we createdforest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result,forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore,we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 usingthe forest carbon stock map in this study. The surrounding forest destroyed around the fire areawas estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbonstocks and facilitates accounting of real-time changes in the carbon balance as well as managingthe LULUCF part of greenhouse gas inventories.
참고문헌 (19)
- Dixon, R. K., "Carbon pools and flux of global forest ecosystems", Science, 263, pp.185~190 (1994)[DOI]
- Eggleston, H., "2006 IPCC guidelines for national greenhouse gas inventories", ~ (2006)
- Friedlingstein, P., "Global carbon budget 2020", Earth Syst. Sci. Data, 12, pp.3269~3340 (2020)[DOI]
- Gallaun, H., "EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements", Forest ecology and management, 260, pp.252~261 (2010)[DOI]
- Harris, N. L., "Global maps of twenty-first century forest carbon fluxes", Nature Climate Change, 11, pp.234~240 (2021)[DOI]
- 정재훈, "위성영상을 활용한 지상부 산림바이오매스 탄소량 추정- k-Nearest Neighbor 및 Regression Tree Analysis 방법의 비교 분석 -", 대한원격탐사학회지, 30(5), pp.651~664 (2014), 대한원격탐사학회[DOI]
- 이정희, "다시기 Landsat TM 영상과 기계학습을 이용한 토지피복변화에 따른 산림탄소저장량 변화 분석", 한국지리정보학회지, 18(4), pp.81~99 (2015), 한국지리정보학회
- Olivier, J., "Trends in global CO2 and total greenhouse gas emissions", PBL Netherlands Environmental Assessment Agency, 5, pp.1~11 (2017)
- Sim, W., "A study on the utilization plan of national geospatial information for land use change matrix", Journal of Forest Economics, 24, pp.41~50 (2017)
- Yu, S. C., "A study on constriction plan of the statistics for national green house gas inventory (LULUCF Sector)", Journal of Korea Spatial Information Society, 23, pp.67~77 (2015)
- National Greenhouse Gas Inventory Report of Korea, "GIR (Greenhouse Gas Inventory & Research Center of Korea)", ~
- IPCC, "IPCC guidelines for national greenhouse gas inventories", ~ (2006)
- Korea Forest Resarch Institute, "Survey Manual for Forest Biomass and Soil Carbon", ~ (2007)
- Korea Forest Resarch Institute, "Forest Biomass Assessment in Korea", ~ (2010)
- The 7th National Forest Inventory and Forest Health Monitoring Guidebook, "Korea Forest Servise", UNFCCC, ~ (2017)
- "Korea Forest Servise" (2023)[URL]
- "Guidelines for preparing land cover maps" (2021)[URL]
- "Fire Information for Resource Management System" (2023)[URL]
- "Global Monitering Laboratory" (2023)[URL]