Spatial Information Research

Korea Spatial Information Society (대한공간정보학회)
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The Application of InSAR Signature Time Series for Landcover Classification

InSAR Signature 시계열 분석을 통한 토지피복분류

  • Received : 2014.01.09
  • Accepted : 2014.02.24
  • Published : 2014.02.28
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Abstract

Considering the wide coverage, the transparency from climate condition, Interferometric Synthetic Aperture Radar (InSAR) possesses a great potential for the landcover classification as shown in many precedent researches. In addition to the merits of InSAR products for the landcover classification, the time series analysis of InSAR pairs can provide a highly reliable basis to interpret landcover. We applied such idea with the test site in Mountain Baekdu located on the border between North Korea and China. Since it is recently noted as the potential volcanic activation site, the landcover especially the vegetation distribution information is highly essential to validate the reliability of Differential Interferometric Synthetic Aperture Radar (DInSAR) over Mt. Baekdu. The algorithms combining the auxiliary information from Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the phase coherence and backscatter coefficient of Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) was established. The results using InSAR signatures from two polarization modes of ALOS PALSAR showed high reliability for mining landcover and spatial distribution.

SAR 영상은 관측시간과 기상현상 등의 외부 환경 영향을 받지 않고 수시로 데이터 취득이 가능하며 광학영상보다 광범위한 관측 영역을 포함하기 때문에 레이더간섭기법 (InSAR)을 이용한 토지피복분류 기법은 큰 이점을 갖는다. 본 연구에서는 L밴드 ALOS PALSAR의 후방산란계수와 긴밀도를 이용한 새로운 토지피복분류 기법을 개발하고 최근 화산 폭발 가능성으로 인해 주목받고 있는 백두산 지역에 시험 적용하였다. 새로운 토지피복분류 체계는 ALOS PALSAR의 HH, HV편광 모드의 영상을 InSAR 시계열 상에서 패킷의 형태로 재구성하고 주성분 분석을 도입하여 분류의 신뢰도 향상을 시도하였다. 또한 MODIS 등 광학 영상 기반 분류와 상호 검증하여 정확도를 확인하였다.

Keywords

References

  1. Austin, J. M; Mackey, B. G; Van, Niel K. P. 2003, Estimating Forest Biomass Using Satellite Radar: An Exploratory Study in a Temperate Austrian Eucalyptus Forest, Forest Ecol. Manage, 176(1-3):575-583. https://doi.org/10.1016/S0378-1127(02)00314-6
  2. Jung, M. H; Lee, S. H; Chang, E. M; Hong S. W. 2013, Method of Monitoring Forest Vegetation Change based on Change of MODIS NDVI Time Series Pattern, Journal of Korea Spatial Information Society, 20(4):47-55. https://doi.org/10.12672/ksis.2012.20.4.047
  3. Luckman, A; Baker, J; Honzak, M. el al. 1998, Tropical Forest Biomass Density Estimation Using JERS-1 SAR: Seasonal Variation, Confidence Limits, and Application to Image Mosaics, Remote Sens. Environ, 63(2):126-139. https://doi.org/10.1016/S0034-4257(97)00133-8
  4. NASA JPL, MOD 13-Gridded 429 Vegetation Indices (NDVI & EVI). http://modis.gsfc.nasa.gov/data/dataprod/dataproducts.php?MOD_NUMBER=13, accessed at 28 June 2012.
  5. Park, J. J; Ku, J. Y; Kim, B. S. 2007, Improvement of the Level-2 Land Cover Map with Satellite Image, Journal of Korea Spatial Information Society, 15(1):67-80.
  6. Strozzi, T; Dammert, P. B. G; Wegmuller, U. et al. 2000, Landuse mapping with ERS SAR interferometry, IEEE Trans. Geosci. Remote Sens, 38(2):766-77. https://doi.org/10.1109/36.842005
  7. Wegmuller, U; Werner, C. 1997, Retrieval of vegetation parameters with SAR interferometry, IEEE Trans. Geosci. Remote Sens, 35(1):18-24. https://doi.org/10.1109/36.551930
  8. Wegmuller, U; Strozzi. T; Farr, T. el al. 2000, Arid land surface characterization with repeat-pass SAR interferometry, IEEE Trans. Geosci. Remote Sens, 38(2):776-781. https://doi.org/10.1109/36.842006

Cited by

  1. Investigation of Potential Volcanic Risk from Mt. Baekdu by DInSAR Time Series Analysis and Atmospheric Correction vol.9, pp.2, 2017, https://doi.org/10.3390/rs9020138