Estimation of insect infestation dynamics using a temporal sequence of Landsat data

Goodwin, Nicholas; Coops, Nicholas C.; Wulder, Michael A.; Gillanders, Steve N.; Schroeder, Todd A.; Nelson, Trisalyn A.

doi:10.1016/j.rse.2008.05.005

Cited by 165 publications

(125 citation statements)

References 35 publications

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“…Other studies have been based on defining thresholds in two-date spectral space to classify change using a variety of statistical methods based on image subtraction, image division and principal components analysis, or by defining spectral change vectors having direction and magnitude (Muchoney & Haack, 1994;Coppin et al, 2004). These methods have been extended to multiple two-date sets of imagery to classify forest changes associated with harvest , fire (Eidenshink et al, 2007), insects (Goodwin et al, 2008) and forest recovery (Hayes & Sader, 2001). Recently, interval approaches have been extended to very broad areas, a strong indication of their continued utility.…”

Section: Past Change Detection Approachesmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

110

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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Section: Past Change Detection Approachesmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

110

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“…With the opening of Landsat data archive on the long-term data accumulation, there has been increasing interest in applying dense time series Landsat data on change detection (Wulder, Masek, Cohen, Loveland, & Woodcock, 2012;Zhu & Woodcock, 2014). Because of the advantage of high temporal frequency, many quite subtle disturbance events of forest, such as defoliation, diseases, insect pests and regeneration, can be captured based on the change of vegetation spectral attribution (Goodwin et al, 2008;Hermosilla, Wulder, White, Coops, & Hobart, 2015;Zhu, Woodcock, & Olofsson, 2012). In addition to its wide applications in forest ecosystems, such method has also been applied to quantify changes of impervious surfaces in urban environments (Powell, Cohen, Yang, Pierce, & Alberti, 2008;Schneider, 2012), coral reef health (Palandro et al, 2008) and fire events (Röder, Hill, Duguy, Alloza, & Vallejo, 2008).…”

Section: Introductionmentioning

confidence: 99%

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Zhou

Qian

et al. 2016

Remote Sensing of Environment

153

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“…Many anthropogenic land cover changes occur at less than a 250-m resolution [17]. The Landsat series of instruments at a 30-m resolution provide a critical tool to understand forest changes relevant to the C-cycle from harvest [18], wildfire [19,20] and insect outbreaks [21] by extending observational studies to areas that have poor records [22]. North American studies have used the decadal epoch of Landsat data, GeoCover or the Global Land Survey (GLS) [23] to quantify forest disturbances [24,25], while others have used dense Landsat time-series stack (LTSS) data focused on specific regions to evaluate change vectors to understand forest disturbance dynamics [20,[26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Given the difficulty of discriminating background populations from outbreak events, few have classified insect, pest and pathogen events in Landsat data [21,36]. Widespread insect infestations have occurred in North America over the past 30+ years, which have potentially large impacts on standing C stocks [37,38].…”

Section: Introductionmentioning

confidence: 99%

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An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

et al. 2014

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Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a OPEN ACCESS Remote Sens. 2014, 6 2783 spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km 2 ). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

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Estimation of insect infestation dynamics using a temporal sequence of Landsat data

Cited by 165 publications

References 35 publications

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

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