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Near-real time detection of beetle infestation in pine forests using MODIS data


Anees, A and Aryal, J, Near-real time detection of beetle infestation in pine forests using MODIS data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, (9) pp. 3713-3723. ISSN 1939-1404 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 IEEE

DOI: doi:10.1109/JSTARS.2014.2330830


This paper considers near-real time detection of beetle infestation in North American pine forests using MODIS 8-days 500 m data. Two methods are considered, both using a single time series for detection of beetle infestation by analyzing the statistics of the trend component of the signal. The first method estimates the trend component of the vegetation index time series by fitting an underlying triply modulated cosine model over a sliding window, using nonlinear least squares (NLS), and the second method uses a T-point moving average finite impulse response (FIR) filter. Both the methods perform well and show similar performance on simulated datasets. The methods are also tested on many difference and ratio-indices of a real-world dataset with change and no-change examples taken from the Rocky Mountain region of the United States and of British Columbia in Canada. The results suggest that both the methods detect beetle infestation reliably in almost all the vegetation index datasets. However, the model-based method (NLS-based) performs better in terms of the detection delay. Red Green Index (RGI), when used with the model-based method, provides the best tradeoff between the detection delay and accuracy. Furthermore, 90%, 50%, and 25% cross-validations are also performed for the threshold selection on RGI dataset, and it is shown that the selected threshold works well on the test data. In the end, it is also shown that the model-based method outperforms a recently published method for near-real time disturbance detection in MODIS data, in both accuracy and detection delay.

Item Details

Item Type:Refereed Article
Keywords:change detection, model fitting, MODIS, time series
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Terrestrial biodiversity
UTAS Author:Anees, A (Mr Asim Anees)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:92460
Year Published:2014
Web of Science® Times Cited:26
Deposited By:Geography and Environmental Studies
Deposited On:2014-06-18
Last Modified:2017-10-25
Downloads:87 View Download Statistics

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