Prediction of Carbon Accumulation within Semi-Mangrove Ecosystems Using Remote Sensing and Artificial Intelligence Modeling in Jeju Island,  South Korea

Cheolho Lee; Jongsung Lee; Chaebin Kim; Yeounsu Chu; Bora Lee

doi:10.17820/eri.2023.10.4.161

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2023 Vol.10, Issue 4 Preview Page Next Page

Original Article

Prediction of Carbon Accumulation within Semi-Mangrove Ecosystems Using Remote Sensing and Artificial Intelligence Modeling in Jeju Island, South Korea 원격탐사와 인공지능 모델링을 활용한 제주도 지역의 준맹그로브 탄소 축적량 예측

31 December 2023. pp. 161-170

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Abstract

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semi- mangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha – 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

Keywords

Aboveground biomass

Blue carbon

Global ecosystem dynamics investigation

Random forest

본 연구에서는 제주도에서 자생하는 준맹그로브인 황근 (Hibiscus hamabo)과 갯대추나무 (Paliurus ramosissimus)의 탄소 저장량을 원격탐사로 추정하고 기후요인에 의하여 공간변이를 예측하는 인공지능 모델을 구축하고자 하였다. 준맹그로브의 지상부 탄소 축적량은 Global Ecosystem Dynamics Investigation (GEDI) 라이다에 의하여 제공되는 지상부 생물량 밀도 (aboveground biomass density, AGBD)를 Sentinel-2 영상으로부터 추출한 normalized difference vegetation index (NDVI)으로 해상도를 상향하여 추정하였다. 제주도에서 단위면적당 탄소 축적량은 황근이 16.6 t C/ha, 갯대추나무가 21.1 t C/ha이었다. 제주도 전 해안에서 준맹그로브의 탄소 축적량은 11.5 t C로 추정되었다. 환경요인에 따른 준맹그로브의 탄소 축적량을 예측하기 위하여 랜덤 포레스트 기술을 적용하였다. 제주도 준맹그로브림의 분포면적 대비 지상부 생물량의 잔차를 계산하였다. 이 잔차에 영향을 미치는 주요 환경요인으로는 가장 습한 달의 강수량, 가장 더운 달의 최고온도, 등온성 및 가장 습한 달의 평균 온도가 선정되었다. 제주도에서 랜덤 포레스트 분석으로 예측된 준맹그로브의 탄소 축적량은 12.0 t C/ha – 27.6 t C/ha 범위의 공간적 변이를 나타내었다. 본 연구에서 개발된 탄소 축적량의 원격탐사 추정법과 환경요인에 따른 인공지능 예측법은 한반도에서 탄소흡수원으로서 맹그로브의 보전과 조성에 필요한 기초자료로 활용할 수 있을 것이다.

키워드

지상부 생물량

탄소흡수원

글로벌 생태계 역학 조사

랜덤 포레스트

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Information

Publisher :Korean Society of Ecology and Infrastructure Engineering
Publisher(Ko) :응용생태공학회
Journal Title :Ecology and Resilient Infrastructure
Journal Title(Ko) :응용생태공학회 논문집
Volume : 10
No :4
Pages :161-170
Received Date : 2023-10-30
Revised Date : 2023-11-09
Accepted Date : 2023-11-09
DOI :https://doi.org/10.17820/eri.2023.10.4.161

Ecology and Resilient InfrastructureISSN:2288-8527(Online)응용생태공학회

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