Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

Leckie, Donald G.; Cloney, Ed; Joyce, Steve

doi:10.1016/j.jag.2004.12.002

Cited by 39 publications

(25 citation statements)

References 39 publications

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“…A spectral mixture analysis approach was employed with Landsat TM imagery acquired at pre-defoliation and peak-defoliation time periods, from which actual jack pine budworm defoliation was determined with high accuracy in pure stands of jack pine and in mixed stands with oak (Quercus Linnaeus; Fagaceae) as a deciduous component (Radeloff et al 1999). Leckie et al (2005) used 2.5-m spatial resolution multispectral imagery for mapping current-year jack pine budworm defoliation (expressed as red discolouration in tree crowns) and obtained accuracy results of 84% across four classes. As for hemlock looper, Franklin (1989) undertook a supervised classification of SPOT 20 m multispectral imagery acquired when defoliation was in the red and brown stages, achieving good results (90% accuracy with two defoliation classes).…”

Section: S319mentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

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Section: S319mentioning

confidence: 99%

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

et al. 2016

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“…To date, no rigorous accuracy assessment has been conducted for this data source; however, there are issues related to positional and attribution accuracy (Aldrich et al 1958, Nelson et al 2004. Errors that may occur in aerial overview survey data have been attributed to offnadir viewing, distortions due to lighting conditions, interpreter fatigue, and level of experience (Aldrich et al 1958;Leckie et al 2005;Wulder et al 2006a, b). Interpreter variability has been tested by conducting a comparison between the numbers of infested trees observed from the aerial overview surveys against the number of infested trees counted on aerial photographs (Harris and Dawson 1979).…”

Section: Regional Scalementioning

confidence: 99%

Linking survey detection accuracy with ability to mitigate populations of mountain pine beetle

Coggins¹,

Wulder²,

Coops³

et al. 2008

The Forestry Chronicle

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In 2007, the mountain pine beetle impacted an estimated 10.1 million hectares of pine forest in British Columbia, Canada. Surveys to detect the location, size, and impact of infestations are conducted from field, airborne, and satellite perspectives. Importantly, the differing survey approaches characterize the infestation over dissimilar spatial scales (i.e., trees, stands, landscapes), and with varying levels of detection accuracy. In this communication, we provide background for understanding differing survey approaches, the nature of the information generated, the resultant detection accuracies that may be expected, and the link between survey accuracy and the ability to mitigate a given mountain pine beetle infestation. A detection accuracy of 100% implies that all infested trees could be mitigated; however, no survey method achieves this level of detection accuracy, and therefore some residual infestation will persist, facilitating further population expansion if other environmental factors are conducive. Based upon this understanding, we model the number of years of mitigation effort required to maintain endemic beetle population levels, as a function of the survey approach used and the expected detection accuracy.Key words: mountain pine beetle, survey, detection, remote sensing, accuracy, mitigation, insect, Landsat, QuickBird, aerial overview survey, heli-GPS RÉSUMÉ Au cours de la période allant de 1999 à 2006, le dendroctone du pin ponderosa (Dendroctonus ponderosae Hopk.) a infesté plus de 10,1 millions d'hectares de pinède en Colombie-Britannique au Canada. Les sondages effectués pour détecter la localisation, l' étendue et le niveau d'infestation sont entrepris à partir du sol, des airs et de l' espace. À noter cependant, ces approches distinctes de sondage répertorient l'infestation en fonction d' échelles spatiales différentes (par ex., arbres, peuplements, écosystèmes) et selon des niveaux variables de précision de détection. Dans cet article, nous présentons les raisons permettant de comprendre les approches distinctes de sondage, la nature de l'information générée, la précision résultante en matière de détection à laquelle on peut s'attendre, et le lien entre la précision du sondage et la capacité de mitiger les effets d'une infestation donnée de dendroctone du pin. Une précision de détection de 100% implique que tous les arbres attaqués peuvent être récupérés; cependant aucune méthode de sondage ne permet d'atteindre ce niveau de pré-cision de détection et, par conséquent, une certaine infestation résiduelle demeurera en place, facilitant l' expansion accrue des populations dans le cas où les autres facteurs environnementaux s'y prêteraient. En fonction de la compréhension de ces faits, nous avons modélisé le nombre d'années requis d' efforts de mitigation pour maintenir les populations de dendroctones à un niveau endémique, sous forme de fonction du mode de sondage utilisé et de la précision de la détection attendue.

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“…Helicopter movement, shadow, view angle, and weather conditions may result in either overestimates or underestimates of attack severity and position (Leckie et al, 2005;Wulder et al, 2006a). Helicopter GPS surveys are considered the benchmark for operational accuracy in the detection and mapping of mountain pine beetle impacts at the local scale, with a horizontal positional accuracy of approximately 20 m and a low error of commission (< 5%) (British Columbia Ministry of Forests, 2004).…”

Section: Mountain Pine Beetle Survey Datamentioning

confidence: 99%

Calculating the Risk of Mountain Pine Beetle Attack: a Comparison of Distance- and Density-Based Estimates of Beetle Pressure

Wulder

2006

J ENV INFORM

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ABSTRACT. An established model for risk rating of Pinus contorta stands for potential mortality caused by mountain pine beetle (Dendroctonus ponderosae) combines information on stand susceptibility and beetle pressure. Susceptibility is determined using attributes in the forest inventory data, while beetle pressure is calculated based on the size and distance to existing infestation locations (distance-based model). An alternate model for calculating beetle pressure is presented in this paper, which uses Voronoi polygons to incorporate size and distance, while emphasizing the density of existing infestation locations (density-based model), in combination with empirical knowledge of beetle dispersal and forest inventory data. Survey data of existing beetle damage were collected using a helicopter mounted global positioning system (GPS) at a study site in central British Columbia, Canada in 1999, 2000, and 2001. These data facilitated the estimation of beetle pressure, and the comparison of risk ratings to actual attack locations. Using the distance-based model, 18 and 27% of areas identified as having a risk rating of greater than 50 in 1999 and 2000 were actually found to be attacked by beetles in surveys conducted in 2000 and 2001. Conversely, 39 and 49% of areas identified as having risk greater than 50 in 1999 and 2000 with the density-based model were attacked in 2000 and 2001. The results suggest that the density-based model of beetle pressure produced risk ratings that had a greater correspondence with actual infestation occurrence than risk ratings generated from the distance-based model. Using data that is typically collected to monitor beetle populations, novel methods of spatial processing may be applied in a transparent manner, generating results that incorporate knowledge of mountain pine beetle dynamics under certain population conditions, into calculations of the risk of mountain pine beetle attack.

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Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

Cited by 39 publications

References 39 publications

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Linking survey detection accuracy with ability to mitigate populations of mountain pine beetle

Calculating the Risk of Mountain Pine Beetle Attack: a Comparison of Distance- and Density-Based Estimates of Beetle Pressure

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Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

Cited by 39 publications

References 39 publications

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

Linking survey detection accuracy with ability to mitigate populations of mountain pine beetle

﻿Calculating the Risk of Mountain Pine Beetle Attack: a Comparison of Distance- and Density-Based Estimates of Beetle Pressure

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Calculating the Risk of Mountain Pine Beetle Attack: a Comparison of Distance- and Density-Based Estimates of Beetle Pressure