대한원격탐사학회지 (Korean Journal of Remote Sensing)

  • 제30권4호
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  • Pages.505-510
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  • 2014
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  • 1225-6161(pISSN)
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  • 2287-9307(eISSN)
대한원격탐사학회 (Korean Society of Remote Sensing)
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시계열 패턴 반응형 Low-peak 탐지 기법을 통한 NDVI 보정방법 개선

An improved method of NDVI correction through pattern-response low-peak detection on time series

  • 이경상 (부경대학교 공간정보시스템공학과) ;
  • 한경수 (부경대학교 공간정보시스템공학과)
  • Lee, Kyeong-Sang (Dept. of Spatial Information Engineering, Pukyong National University) ;
  • Han, Kyung-Soo (Dept. of Spatial Information Engineering, Pukyong National University)
  • 투고 : 2014.08.18
  • 심사 : 2014.08.22
  • 발행 : 2014.08.31
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초록

NDVI는 기후변화 모니터링과 식생 변화 탐지 모니터링을 위한 주요한 지표이다. NDVI를 산출하기 전에 cloud masking, 대기보정과 같은 전처리 과정을 거침에도 불구하고 강수, 적설이나 구름의 영향이 완전히 제거되지 않아 NDVI가 현저히 낮게 관측되는 noise가 불규칙적으로 발생한다. 이러한 noise를 보정하기 위해서 국내외로 활발한 연구가 진행되고 있다. 기존의 다중 다항 회귀식을 이용한 방법에서는 과대추정이나 low peak를 잘 탐지하지 못하는 등 문제점이 나타나고 있으므로 보다 정확하게 noise를 보정하는 방법이 요구된다. 본 연구에서는 이동평균을 이용하여 noise를 보정하였고, 기존의 다중 다항 회귀식을 이용하여 산출한 NDVI 시계열과 비교를 해보았다. 그 결과 이동평균을 이용한 방법이 이전의 방법보다 NDVI noise를 잘 보정하는 것으로 보여진다.

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

키워드

참고문헌

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피인용 문헌

  1. 도시 녹지공간 식생 모니터링을 위한 무인항공기 활용방안 vol.22, pp.1, 2014, https://doi.org/10.13087/kosert.2019.22.1.61