Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities

Wulder, Michael A.; Dymond, Caren C.; White, Joanne C.; Leckie, Donald G.; Carroll, Allan L.

doi:10.1016/j.foreco.2005.09.021

Cited by 334 publications

(261 citation statements)

References 32 publications

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“…At present, aerial detection survey maps are the best available information to determine prefire outbreak stage. Finer-resolution data from aerial or satellite imagery (47) or detailed prefire field measurements may aid in assigning beetle outbreak stage to individual trees or patches of trees rather than the whole fire. Third, fire severity and postfire tree regeneration outcomes may differ in forests with more uniformly high outbreak severity (e.g., consistently >50% tree mortality) (24) or in later stages of postoutbreak forests (e.g., >10 y after infestation) when most or all beetle-killed trees have fallen to the ground (14,15,17,30).…”

Section: Discussionmentioning

confidence: 99%

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies

Harvey

Donato

Turner

2014

Proc. Natl. Acad. Sci. U.S.A.

122

123

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Widespread tree mortality caused by outbreaks of native bark beetles (Circulionidae: Scolytinae) in recent decades has raised concern among scientists and forest managers about whether beetle outbreaks fuel more ecologically severe forest fires and impair postfire resilience. To investigate this question, we collected extensive field data following multiple fires that burned subalpine forests in 2011 throughout the Northern Rocky Mountains across a spectrum of prefire beetle outbreak severity, primarily from mountain pine beetle (Dendroctonus ponderosae). We found that recent (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) beetle outbreak severity was unrelated to most field measures of subsequent fire severity, which was instead driven primarily by extreme burning conditions (weather) and topography. In the red stage (0-2 y following beetle outbreak), fire severity was largely unaffected by prefire outbreak severity with few effects detected only under extreme burning conditions. In the gray stage (3-10 y following beetle outbreak), fire severity was largely unaffected by prefire outbreak severity under moderate conditions, but several measures related to surface fire severity increased with outbreak severity under extreme conditions. Initial postfire tree regeneration of the primary beetle host tree [lodgepole pine (Pinus contorta var. latifolia)] was not directly affected by prefire outbreak severity but was instead driven by the presence of a canopy seedbank and by fire severity. Recent beetle outbreaks in subalpine forests affected few measures of wildfire severity and did not hinder the ability of lodgepole pine forests to regenerate after fire, suggesting that resilience in subalpine forests is not necessarily impaired by recent mountain pine beetle outbreaks. disturbance interactions | forest resilience | fire ecology | serotiny | conifer forest

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Section: Discussionmentioning

confidence: 99%

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies

Harvey

Donato

Turner

2014

Proc. Natl. Acad. Sci. U.S.A.

122

123

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“…Hansen & DeFries, 2004). Maps of forest disturbances such as fire (Tansey et al, 2004;Roy et al, 2005) and insect defoliation (Wulder et al, 2006) represent special cases of forest change categorical variables. Percentage or proportion of forest cover (Häme et al, 2001;Hansen et al, 2003;McRoberts, 2006McRoberts, , 2009bMcRoberts, , 2010a and biomass (Dong et al, 2003;Blackard et al, 2008) are examples of continuous variables.…”

Section: Types Of Maps and Productsmentioning

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

112

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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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“…There are at least three major strategies for using remote sensing to assess forest damage: early damage detection, extent mapping, and damage quantification [6]. In forest health, most studies have used remote sensing techniques to map forest conditions at a regional or stand level [9][10][11][12][13][14][15]. Individual tree damage is often investigated for disturbance across stand level extents [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data

Hentz¹,

Strager²

2018

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The periodical cicadas appear in regions of the United States in intervals of 13 or 17 years. During these intervals, deciduous trees are often impacted by the small cuts and eggs they lay in the outer branches which soon die off. Because this is such an infrequent occurrence and it is so difficult to assess the damage across large forested areas, there is little information about the extent of this impact. The use of remote sensing techniques has been proven to be useful in forest health management to monitor large areas. In addition, the use of Unmanned Aerial Vehicles (UAVs) has become a valuable tool for analysis. In this study, we evaluated the impact of the periodical cicada occurrence on a mixed hardwood forest using UAV imagery. The goal was to evaluate the potential of this technology as a tool for forest health monitoring. We classified the cicada impact using two Maximum Likelihood classifications, one using only the high resolution spectral derived from leaf-on imagery (MLC 1), and in the second we included the Canopy Height Model (CHM)-derived from leaf-on Digital Surface Model (DSM) and leaf-off Digital Terrain Model (DTM)-information in the classification process (MLC 2). We evaluated the damage percentage in relation to the total forest area in 15 circular plots and observed a range from 1.03% -22.23% for MLC 1, and 0.02% -10.99% for MLC 2. The accuracy of the classification was 0.35 and 0.86, for MLC 1 and MLC 2, based on the kappa index. The results allow us to highlight the importance of combining spectral and 3D information to evaluate forest health features. We believe this approach can be applied in many forest monitoring objectives in order to detect disease or pest impacts.

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Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities

Cited by 334 publications

References 32 publications

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies

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

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data

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Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities

Cited by 334 publications

References 32 publications

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies

Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies

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

Cicada (&lt;i&gt;Magicicada&lt;/i&gt;) Tree Damage Detection Based on UAV Spectral and 3D Data

Contact Info

Product

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About

Cicada (<i>Magicicada</i>) Tree Damage Detection Based on UAV Spectral and 3D Data