Spatial-Temporal Modeling of Forest Gaps Generated by Colonization From Below- and Above-Ground Bark Beetle Species

Zhu, Jun; Rasmussen, Jakob Gulddahl; Møller, Jesper; Aukema, Brian H.; Raffa, Kenneth F.

doi:10.1198/016214507000000842

Cited by 28 publications

(45 citation statements)

References 4 publications

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“…The greatest classification accuracy of insect disturbance was 72.1% user's and 60.8% producer's in Path 26, Row 28, Sawyer County, where mortality had occurred, leaving a long-term spectral signal that was marked in both air-photos and Landsat as compared to other Landsat stacks, where forest tent caterpillar defoliation was the dominant agent. This outbreak of jack pine budworm that caused mortality and increased forest fragmentation in the early 1990s has been studied thoroughly in the past [44,88,89], and insect disturbance has continued to be a problem to forest productivity in this region [90]. Forest tent caterpillar did not cause mortality and was identified over an outbreak period from 2000 to 2006 with aerial surveys.…”

Section: Landsat Disturbance Classificationmentioning

confidence: 99%

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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Section: Landsat Disturbance Classificationmentioning

confidence: 99%

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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“…Declines are often multi-causal, with the various agents exhibiting strong spatiotemporal covariation (Minorsky, 2003;Wardle et al, 2004), so their delineation poses special challenges from both mechanistic and epidemiological perspectives (Rasmussen et al, 2007;Zhu et al, 2008). Similarly, interactions between below-and aboveground herbivory are widely recognized as important yet poorly understood drivers of terrestrial ecosystems at multiple levels of scale (Foggo and Speight, 1993;Maron, 1998;Strong et al, 1999;Bonello et al, 2001;Bardgett et al, 1998;Vanderputten and Peters, 1997), so continual observations over long time series can improve our understanding of underlying complex dynamics.…”

Section: Discussionmentioning

confidence: 97%

Predisposition to bark beetle attack by root herbivores and associated pathogens: Roles in forest decline, gap formation, and persistence of endemic bark beetle populations

Aukema

Zhu

Møller

et al. 2010

Forest Ecology and Management

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“…For example, Heikkinen and Högmander (1994) approximated the likelihood function by a pseudolikelihood function, but there seems a lack of theoretical justification (see also Hoeting et al 2000). More recently, Berthelsen and Møller (2003) used path sampling to approximate the ratio of unknown normalizing constants in spatial point processes, but the amount of computation involved can be rather large (see also Gelman and Meng 1998;Zhu et al 2008).…”

Section: Introductionmentioning

confidence: 97%

Markov chain Monte Carlo for a Spatial-Temporal Autologistic Regression Model

Zheng

Zhu

2008

Journal of Computational and Graphical Statistics

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An existing spatial-temporal autologistic regression model relates a binary response variable to potential covariates while accounting for both dependence on a spatial lattice and dependence over discrete time points. This approach is useful for analyzing spatial-temporal binary data. However, the existing statistical inference is via maximum pseudo-likelihood and may be statistically inefficient especially when the spatial and temporal dependence is strong. This article proposes a fully Bayesian approach for both model parameter inference and prediction at future time points using Markov chain Monte Carlo (MCMC). A Metropolis-Hastings algorithm combined with a Gibbs sampler or perfect simulation are developed for obtaining the posterior distributions of model parameters as well as the posterior predictive distributions. We demonstrate the methodology and compare the results with maximum pseudolikelihood and MCMC maximum likelihood approaches via a real data example concerning southern pine beetle outbreak.

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Spatial-Temporal Modeling of Forest Gaps Generated by Colonization From Below- and Above-Ground Bark Beetle Species

Cited by 28 publications

References 4 publications

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

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

Predisposition to bark beetle attack by root herbivores and associated pathogens: Roles in forest decline, gap formation, and persistence of endemic bark beetle populations

Markov chain Monte Carlo for a Spatial-Temporal Autologistic Regression Model

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