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Combining wavelets and linear spectral mixture model for MODIS satellite sensor time-series analysis

doi: 10.6062/jcis.2008.01.01.0005(Free PDF)

Authors

Ramon M. Freitas and Yosio E. Shimabukuro

Abstract

This work presents a methodology that uses digital fraction images derived from Linear Spectral Mixture Model and wavelets transform from MODIS satellite sensor time-series for land cover change analysis. Our approach uses MODIS surface reflectance images acquired from 2000 to 2006 time period. For this study, a test site was selected in the Mato Grosso State, Brazilian Amazonia. This site has shown high deforestation rates in the last years. The samples of land cover classes were collected during four field campaigns (2003, 2004, 2005 and 2006) to be used as ground truth. The linear spectral mixture model was applied to the MODIS surface reflectance images of red surface reflectance band (620-670 nm bandwidth), near infrared surface reflectance band (NIR, 841-876 nm bandwidth) and medium infrared surface reflectance band (MIR, 2105-2155 nm bandwidth). This model generated the vegetation, shade, and soil fraction images. In the next step, the Meyer orthogonal Discrete Wavelets Transform was used for filtering the time-series of MODIS fraction images. The filtered signal was reconstructed excluding high frequencies for each pixel in the fraction images (soil, vegetation, and shade) of the time-series. This computational procedure allows to observe the original signal without clouds and other noises. The results show that wavelets transform can provide a gain in multitemporal analysis and visualization on inter-annual fraction images variability patterns.

Keywords

MODIS, Wavelets, Fractions Images, Time-Series Analysis.

References

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[12] SHIMABUKURO YE, BATISTA GT, MELLO EMK, MOREIRA JC & DUARTE V. 1998. Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon region. International Journal of Remote Sensing, 19(3): 535-541. doi: 10.1080/014311698216152

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