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A statistical framework for near-real time detection of beetle infestation in pine forests using MODIS data

Citation

Anees, A and Aryal, J, A statistical framework for near-real time detection of beetle infestation in pine forests using MODIS data, IEEE Geoscience and Remote Sensing Letters, 11, (10) pp. 1717-1721. ISSN 1545-598X (2014) [Refereed Article]


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Copyright Statement

Copyright 2014 IEEE

DOI: doi:10.1109/LGRS.2014.2306712

Abstract

Beetle infestations have caused significant damage to the pine forest in North America. Early detection of beetle infestation in near-real time is crucial, in order to take appropriate steps to control the damage. In this letter, we consider nearreal time detection of beetle infestation in North American pine forests using high temporal resolution, coarse spatial resolution MODerate resolution Imaging Spectroradiometer (MODIS 8- days 500m) remotely sensed data. We show that the parameter sequence of a stationary vegetation index time-series, derived by fitting an underlying triply modulated cosine model over a sliding window using Nonlinear Least Squares (NLS), resembles a martingale sequence. The advantage of such properties of the parameter sequence is that standard martingale central limit (MCLT) theorem and well-known Gaussian distribution statistics can be used effectively to detect any non-stationarity in the vegetation index time-series with high accuracy. The proposed method exploits these properties of the parameter time-series , and hence does not require threshold tuning. The threshold is selected based on well-documented procedure of z-value selection from table of Gaussian distribution, depending upon the percentage of the distribution considered as outlier. The proposed framework is tested on different vegetation index datasets derived from MODIS 8-days 500 m image time-series of beetle infestations of North America (Colorado and British Columbia). The results show that the proposed framework can detect non-stationarities in the vegetation index time-series accurately, and performs the best on Red Green Index (RGI).

Item Details

Item Type:Refereed Article
Keywords:change detection algorithms, time-series, MODIS, nonlinear least squares approximations, time series analysis
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Geomatic Engineering not elsewhere classified
Objective Division:Environment
Objective Group:Land and Water Management
Objective Field:Forest and Woodlands Land Management
Author:Anees, A (Mr Asim Anees)
Author:Aryal, J (Dr Jagannath Aryal)
ID Code:88828
Year Published:2014
Web of Science® Times Cited:6
Deposited By:Geography and Environmental Studies
Deposited On:2014-02-18
Last Modified:2017-10-25
Downloads:2 View Download Statistics

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