Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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A noise reduction method for MODIS NDVI time series data based on statistical properties of NDVI temporal dynamics

MODIS NDVI 시계열 자료의 통계적 특성에 기반한 NDVI 데이터 잡음 제거 방법

  • 정명희 (안양대학교 디지털미디어학과) ;
  • 장석우 (안양대학교 디지털미디어학과)
  • Received : 2017.08.23
  • Accepted : 2017.09.15
  • Published : 2017.09.30
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Abstract

Multitemporal MODIS vegetation index (VI) data are widely used in vegetation monitoring research into environmental and climate change, since they provide a profile of vegetation activity. However, MODIS data inevitably contain disturbances caused by the presence of clouds, atmospheric variability, and instrument problems, which impede the analysis of the NDVI time series data and limit its application utility. For this reason, preprocessing to reduce the noise and reconstruct high-quality temporal data streams is required for VI analysis. In this study, a data reconstruction method for MODIS NDVI is proposed to restore bad or missing data based on the statistical properties of the oscillations in the NDVI temporal dynamics. The first derivatives enable us to examine the monotonic properties of a function in the data stream and to detect anomalous changes, such as sudden spikes and drops. In this approach, only noisy data are corrected, while the other data are left intact to preserve the detailed temporal dynamics for further VI analysis. The proposed method was successfully tested and evaluated with simulated data and NDVI time series data covering Baekdu Mountain, located in the northern part of North Korea, over the period of interest from 2006 to 2012. The results show that it can be effectively employed as a preprocessing method for data reconstruction in MODIS NDVI analysis.

Multitemporal MODIS 식생 지수 (VI) 자료는 식생 활동의 프로파일을 제공하기 때문에 환경 및 기후 변화에 대한 식생 모니터링 연구에 널리 사용되고 있다. 그러나 MODIS 데이터에는 구름이나 대기 변동성 및 계측기 문제로 인해 노이즈가 발생하여 NDVI 시계열 데이터 분석과 애플리케이션 응용에 있어서 자료 정확성에 문제가 생기게 된다. 이러한 이유로, NDVI 자료를 이용한 VI 분석을 위해서는 잡음을 줄이고 고품질의 시계열 데이터 스트림을 재구성하기위한 전 처리가 필요하다. 본 연구에서는 NDVI 시계열 자료의 통계적 특성을 기반으로 불량 데이터 또는 미관측 데이터를 복원하기 위해 MODIS NDVI에 대한 데이터 재구성 방법을 제안하고 있다. 데이터 스트림 함수의 속성을 검사하면 급격한 증가나 감소와 같은 비정상적인 변화를 감지 할 수 있다. 본 연구에 제안하고 있는 방법은 정상적인 자료의 세부적 특징은 그대로 유지하면서 노이즈 자료만 수정하는 방향으로 자료를 복원할 수 있다. 제안된 기법은 시뮬레이션 데이터와 2006년부터 2012년까지의 북한지역 백두산을 대상으로 NDVI 시계열 자료를 사용하여 테스트하였고 시뮬레이션 테스트에서는 기존 wavelet이나 Gaussian 방법에 비해 본 방법이 에러율을 평균 70% 이상 줄일 수 있어 제안된 방법이 노이스가 있는 시계열 자료의 데이터 재구성에 있어 효과적임을 입증하였다.

Keywords

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