Abstract

Seasonal variations in land cover are commonly represented using a constant frequency cosine model with time-varying parameters. As frequency represents the constant annual vegetation growth cycle, the model is not adequate to represent dynamics such as sudden changes in land cover and subsequent regrowth. In this paper, we present a new model to capture time-varying changes in the vegetation growth cycle and detect abrupt changes in land cover due to forest fires. We also design a sequential Monte Carlo estimation approach of the time-varying frequency in the proposed nonlinear model using the particle filter (PF). We further propose a binary hypothesis land cover change detector that is based on a dissimilarity measure between windowed time-series observed during the same month of consecutive years. Experiments show that the PF estimation can detect change with lower delay than the existing approaches. Unsupervised mapping of the fire severity from the model parameter estimates is also developed.

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Keywords

  • Computational modeling
  • Estimation
  • Fires
  • Land cover change detection
  • Mathematical model
  • particle filter (PF)
  • Time-frequency analysis
  • time-varying frequency
  • Vegetation mapping

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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