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
N1 - Funding Information:
Dr. Christensen is a Fellow of the American Geophysical Union and Geological Society of America. He was the recipient of the Geological Society of America’s G. K. Gilbert Award, NASA’s Exceptional Scientific Achievement Medal, and NASA’s Public Service Medal.
Publisher Copyright:
© 2008-2012 IEEE.
PY - 2018/6
Y1 - 2018/6
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 - Land cover change detection
KW - particle filter (PF)
KW - time-varying frequency
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U2 - 10.1109/JSTARS.2018.2818060
DO - 10.1109/JSTARS.2018.2818060
M3 - Article
AN - SCOPUS:85046721044
SN - 1939-1404
VL - 11
SP - 1769
EP - 1776
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
IS - 6
ER -