Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data

Abstract

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 -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 (NLSbased) 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.

Development of a method for monitoring of insect induced forest defoliation – limitation of MODIS data in Fennoscandian forest landscapes

“…Czerepanovii N.I. Orlova) forests in northern Fennoscandia; Eklundh et al (2009) successfully mapped defoliation by the European pine sawfly in southeastern Norway; and Anees and Aryal (2014) developed methods based on MODIS data for near real-time detection of bark beetle infestations on pine forest in North America. Insect disturbances have also been included as a class in general forest disturbance monitoring methods based on MODIS data (Sulla-Menashe et al 2014).…”

Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

“…The anomaly regions in satellite image time series are detected by comparing the parameters of the fitted model for different parts of the time series data. The second group consists of the methods that monitor anomalies in satellite time series data using some forecasting model, such as Extended Kalman Filter (Kleynhans et al, 2011), Gaussian Process (Chandola and Vatsavai, 2011), harmonic model (Verbesselt et al, 2012;Zhou et al, 2014;Zhu et al, 2012), nonlinear least square or finite impulse response filter (Anees and Aryal, 2014a), and Martingale theory and martingale central limit theorem (Anees and Aryal, 2014b), etc. In general, these monitoring methods consist of two main steps, i.e., model-fitting of historical data and anomaly detection by comparing the new observations to the predictions from the fitted model.…”

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

“…In the future, we would extend the proposed method to discover the damage triggered by an earthquake [33], or to detect changes by fusing multiple temporal images [34,35].…”