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The estimation of forest biomass using remotely sensed data and machine learning techniques

첫 페이지 보기
  • 발행기관
    강원대학교 산림과학연구소 바로가기
  • 간행물
    강원대학교 산림과학연구소 학술대회 바로가기
  • 통권
    2022 International symposium of Institute of Forest Science for the 40th Anniversary of College of Forest and Environment Science (2022.10)바로가기
  • 페이지
    pp.139-139
  • 저자
    WooJin Cho, YongKyu Lee, JungSoo Lee
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A450507

원문정보

초록

영어
The purpose of this study was to estimate the forest biomass using satellite imagery and machine learning techniques. In this study, Random Forest, XGBoost, SVM, Multiple Linear Regression were used for forest biomass estimation. Research Forest management plan(8th) data and Sentinel-2 imagery information were used to analysis. As the dependent variables, forest biomass was calculated using volume information, and the biomass expansion factor. The 10 bands of Sentinel-2 were used as independent variable. The optimal forest biomass estimation model was selected by comparing the calculated value based on the Research Forest management plan data and the estimate based on the machine learning techniques. MAE, RMSE, and R2 were calculated for comparison of estimated biomass statistics. As a result, the XGBoost model showed the highest RMSE(61.63ton/ha), MAE(44.16ton/ha), and the highest R2(0.48) value, and was evaluated as the optimal biomass estimation model. The average amount of biomass for sub compartments estimated using the XGBoost model was 225.3tons/ha, which was underestimated by 3.4 tons/ha compared to the average amount of biomass calculated using the Research Forest management plan.

키워드

Forest biomass estimation Remote sensing Machine learning Random Forest XGBoost SVM

저자

  • WooJin Cho [ College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea ]
  • YongKyu Lee [ College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea ]
  • JungSoo Lee [ College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea ] Corresponding author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    강원대학교 산림과학연구소 [Institute of Forest Science Kangwon National University]
  • 설립연도
    1975
  • 분야
    농수해양>임학
  • 소개
    강원대학교부설산림과학연구소(이하 “연구소”라 한다)는 산림에 관한 제반 학술적 연구를 통하여 산림자원의 효용을 밝히고 임업 및 임산업의 발전에 기여함을 목적으로 한다.

간행물

  • 간행물명
    강원대학교 산림과학연구소 학술대회
  • 간기
    부정기
  • 수록기간
    2017~2024
  • 십진분류
    KDC 526 DDC 634

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