Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Application study of random forest method based on Sentinel-2 imagery for surface cover classification in rivers - A case of Naeseong Stream -

하천 내 지표 피복 분류를 위한 Sentinel-2 영상 기반 랜덤 포레스트 기법의 적용성 연구 - 내성천을 사례로 -

  • An, Seonggi (Deepartment of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Chanjoo (International Cooperation & Public Relations Division, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Yongmin (Geography Major of Department of Social Studies Education, Seoul National University) ;
  • Choi, Hun (Social Education, Chonnam National University)
  • 안성기 (한국건설기술연구원 수자원하천연구본부) ;
  • 이찬주 (한국건설기술연구원 대외협력홍보본부) ;
  • 김용민 (서울대학교 사회교육학과 지리전공) ;
  • 최훈 (전남대학교 사회교육학과)
  • Received : 2024.03.18
  • Accepted : 2024.05.03
  • Published : 2024.05.31
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Abstract

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

하천 공간의 지표 피복 현황 파악은 하천 관리 및 홍수 재해 예방에 필수적이다. 기존 조사 방법은 전문가에 의한 식생 판독을 통한 식생도 작도 방법이나 식생지수를 활용하는 방법이 활용되어 왔으나, 역동적으로 변화하는 하천 환경을 반영하기에 한계가 있다. 이러한 배경에서 본 연구는 내성천을 대상으로 위성영상 자료를 활용한 랜덤 포레스트 기법을 활용하여 다수 연도의 하천 내 식생 분포를 파악하고, 적용성을 검토하였다. 원격탐사 자료 Sentinel-2 위성 영상을 사용하였으며, 지상 참값(ground truth)은 2016년 내성천 지표 피복 자료를 활용하였다. 랜덤 포레스트 머신러닝 알고리듬을 활용하여 미리 선정된 10개 샘플링 영역으로부터 분류군 별로 1,000개의 표본을 추출하여 훈련 및 검증하였으며, 민감도 분석, 연도별 지표 피복 분석, 정확도 분석을 통하여 적용성을 평가하였다. 연구 결과, 검증 자료 기반의 정확도는 85.1%로 나타났다. 트리 수, 샘플 수, 하천 구역에 대한 민감도 분석 결과, 각각 30개, 800개, 하류에서 효율성이 높았다. 지표 분류 유형은 6개 항목에서 높은 정확도를 보여 지표 피복 분류 결과가 실제 하천 환경을 잘 반영하는 것으로 나타났다. 정확도 분석 결과, 전체 샘플 중 14.9%의 경계오류와 내부오류를 확인하였으며, 지표 피복 분류 중 산발 식생과 초본 식생을 제외한 항목들은 높은 정확도를 보였다. 본 연구에서는 단일 하천을 대상으로 적용하였지만, 보다 정확하고 많은 자료의 구축을 위해서는 다수의 하천에 대해 지표 피복 분류 기법의 적용이 요구된다.

Keywords

Acknowledgement

이 논문은 한국건설기술연구원 주요사업(디지털뉴딜 기반 통합물관리 기술 융합 플랫폼(IWRM-K) 개발(202401-66))의 연구비 지원으로 수행되었습니다.

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