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2024 Vol.34, Issue 4 Preview Page

Research Article

Landslide Detection Using the Normalized-Difference Vegetation Index (NDVI) and a Clustering Algorithm

정규식생지수(NDVI)와 군집화 알고리즘을 활용한 산사태 탐지

Ho-Jin Jeong1
,
Hyuck-Jin Park2*
,
Jung-Hyun Lee3
정 호진1
,
박 혁진2*
,
이 정현3
1Senior Manager, Mountain Land Disaster Investigation Team, Korea Association of Forest Enviro-Conservation Technology
2Professor, Department of Energy Resources and Geosystem Engineering, Sejong University
3Post-Doctoral Fellow, Department of Energy Resources and Geosystem Engineering, Sejong University
1한국치산기술협회 산지재해조사실 실장
2세종대학교 지구자원시스템공학과 교수
3세종대학교 지구자원시스템공학과 박사후 연구원
*Corresponding Author
31 December 2024. pp. 549-561
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Abstract

Previous investigations of landslide occurrence have relied mainly on reports of local residents or on-site surveys, requiring significant manpower and time, with limitations such as the omission of occurrence and uncertainty in timing. In this study, points of vegetation index change were analyzed using the normalized-difference vegetation index (NDVI) before and after heavy rainfall. Landslide-affected areas were detected by comparison these points with the results of clustering by the ISODATA algorithm, an unsupervised classification method. As a result, 513 of 644 points with NDVI changes matched clusters of landslide occurrences. In addition, visual classification by aerial imagery detected 505 landslide occurrences (97.7%). Although over- or under-segmentation occurred in some areas, the method is expected to be useful in generating foundational data for landslide research, especially in constraining the timing of damage occurrence.

Keywords
landslide
NDVI
cluster
ISODATA
satellite image
Sentinel-2

기존의 산사태 발생지 조사는 대부분 지역주민의 신고나 현장 탐문에 의존해 많은 인력과 시간이 소요되고 발생지 누락과 시기 불명확성 등의 한계가 존재했다. 본 연구에서는 집중호우 이전 ‧ 이후의 정규식생지수(NDVI)를 활용하여 식생지수 변화가 발생한 지점을 분석하고, 비지도분류 방법인 ISODATA 알고리즘을 활용하여 군집화된 결과물과 비교하여 산사태 발생지를 탐지하였다. 그 결과, NDVI 변화 지점 644개소 중 513개소가 산사태가 발생한 군집과 일치하였으며, 항공영상을 이용한 육안분류 결과 505개소(97.7%)의 산사태 발생지를 탐지하였다. 일부 구역에서 과대 또는 과소 구획이 발생했으나, 피해발생 시기를 파악할 수 있다는 점에서 본 방법은 산사태 연구의 기초자료로 활용도가 높을 것으로 기대된다.

키워드
산사태
NDVI
군집화
ISODATA
위성영상
Sentinel-2
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Information
  • Publisher :Korean Society of Engineering Geology
  • Publisher(Ko) :대한지질공학회
  • Journal Title :The Journal of Engineering Geology
  • Journal Title(Ko) :지질공학
  • Volume : 34
  • No :4
  • Pages :549-561
  • Received Date : 2024-11-11
  • Revised Date : 2024-11-24
  • Accepted Date : 2024-11-25
  • DOI :https://doi.org/10.9720/kseg.2024.4.549
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