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.

Fingerprint

forest fire
land cover
Fires
modeling
filter
vegetation
regrowth
Time series
seasonal variation
time series
Detectors
experiment
Experiments
parameter
particle

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

Cite this

@article{8bc26cb0bfe247ada8b123e79205f7c8,
title = "Time-Varying Modeling of Land Cover Change Dynamics Due to Forest Fires",
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.",
keywords = "Computational modeling, Estimation, Fires, Land cover change detection, Mathematical model, particle filter (PF), Time-frequency analysis, time-varying frequency, Vegetation mapping",
author = "Srija Chakraborty and Ayan Banerjee and Sandeep Gupta and Philip Christensen and Antonia Papandreou-Suppappola",
year = "2018",
month = "5",
day = "11",
doi = "10.1109/JSTARS.2018.2818060",
language = "English (US)",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
issn = "1939-1404",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Time-Varying Modeling of Land Cover Change Dynamics Due to Forest Fires

AU - Chakraborty, Srija

AU - Banerjee, Ayan

AU - Gupta, Sandeep

AU - Christensen, Philip

AU - Papandreou-Suppappola, Antonia

PY - 2018/5/11

Y1 - 2018/5/11

N2 - 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.

AB - 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.

KW - Computational modeling

KW - Estimation

KW - Fires

KW - Land cover change detection

KW - Mathematical model

KW - particle filter (PF)

KW - Time-frequency analysis

KW - time-varying frequency

KW - Vegetation mapping

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U2 - 10.1109/JSTARS.2018.2818060

DO - 10.1109/JSTARS.2018.2818060

M3 - Article

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JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

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