Using GIS to analyse a severe forest blowdown in the Southern Rocky Mountains

Lindemann, Jeremiah D.; Baker, William L.

doi:10.1080/13658810210136069

Cited by 24 publications

(16 citation statements)

References 31 publications

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“…Physical factors seemed to be most influential, especially wind exposure, elevation, and aspect (Lindemann and Baker 2002). Unexpectedly, soil permeability, soil water-holding capacity, and distance to natural edges were not significantly correlated with the blowdown pattern.…”

Section: Windmentioning

confidence: 99%

Historic range of variability for upland vegetation in the Medicine Bow National Forest, Wyoming

Dillon¹,

Knight²,

Meyer³

2005

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An approach for synthesizing the results of ecological research pertinent to land management is the analysis of the historic range of variability (HRV) for key ecosystem variables that are affected by management activities. This report provides an HRV analysis for the upland vegetation of the Medicine Bow National Forest in southeastern Wyoming. The variables include live tree density, dead tree (snag) density, canopy cover, abundance of coarse woody debris, species diversity, fire return intervals, the abundance of various diseases, the proportion of the landscape in different land cover types, and the degree of patchiness in the landscape. The variables were examined at the stand and landscape scales, using information available in the literature and USFS databases. High-elevation landscapes were considered separately from low-elevation landscapes. Much of the report pertains to forests dominated by lodgepole pine, subalpine fir, and Engelmann spruce at high elevations, and by ponderosa pine and aspen at lower elevations. We defined the HRV reference period for the MBNF as approximately 1600 to 1860.Keywords: aspen, bark beetles, canopy gaps, coarse woody debris, comandra blister rust, dwarf mistletoe, Engelmann spruce, fire effects, disease in Rocky Mountain forests, forest fragmentation, insects in forests, interior forest, landscape ecology, lodgepole pine, mistletoe, mountain pine beetle, ponderosa pine, roads, Rocky Mountain forests, snags, spruce beetle, subalpine fir, subalpine forests, timber harvesting effects, white pine blister rust, wind in forests Executive SummaryThe challenges of sustainable land management have led to an increased emphasis on incorporating the results of science in the decision making process. One approach for accomplishing this objective is through the analysis of the historic range of variability (HRV) for key ecosystem variables that are affected by management activities. The rationale for HRV analyses is that the chances of sustainable forest management are greater if the variation in managed ecosystems includes the range of conditions that are expected at various scales in ecosystems relatively uninfluenced by humans. This report provides an HRV analysis for the upland vegetation of the Medicine Bow National Forest (MBNF).By definition, HRV analyses require the identification of specific variables and an estimation of how those variables fluctuated, at more than one scale, prior to the advent of resource extraction and management by EuropeanAmericans. A complete list of the variables that we examined is found in table 7. Examples include live tree density, dead tree (snag) density, canopy cover, abundance of coarse woody debris, species diversity, fire return intervals, the abundance of various diseases, the proportion of the landscape in different land cover types, and the degree of patchiness in the landscape. We examined variables at two scales-the stand and the landscape-and we separated high-elevation landscapes from low-elevation landscapes. Much of the repo...

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Section: Windmentioning

confidence: 99%

Historic range of variability for upland vegetation in the Medicine Bow National Forest, Wyoming

Dillon¹,

Knight²,

Meyer³

2005

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“…Besides fitting logistic regression models (e.g. Jalkanen and Mattila 2000; Mitchell et al 2001;Hanewinkel 2005;Scott and Mitchell 2005), multiple linear regression (Schütz et al 2006), neural networks (Hanewinkel et al 2004;Hanewinkel 2005), classification and regression trees (Lindemann andBaker 2002, Dobbertin 2002), and generalised additive model (Schmidt et al 2006) approaches have been used in previous studies to assess the P DAM in forests. All these statistical models are based on the assumption that future wind damage events will occur under circumstances similar to those found for past events.…”

Section: Introductionmentioning

confidence: 99%

Modelling the wind damage probability in forests in Southwestern Germany for the 1999 winter storm ‘Lothar’

et al. 2009

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The wind damage probability (P (DAM)) in the forests in the federal state of Baden-Wuerttemberg (Southwestern Germany) was calculated using weights of evidence (WofE) methodology and a logistic regression model (LRM) after the winter storm 'Lothar' in December 1999. A geographic information system (GIS) was used for the area-wide spatial prediction and mapping of P (DAM). The combination of the six evidential themes forest type, soil type, geology, soil moisture, soil acidification, and the 'Lothar' maximum gust field predicted wind damage best and was used to map P (DAM) in a 50 x 50 m resolution grid. GIS software was utilised to produce probability maps, which allowed the identification of areas of low, moderate, and high P (DAM) across the study area. The highest P (DAM) values were calculated for coniferous forest growing on acidic, fresh to moist soils on bunter sandstone formations-provided that 'Lothar' maximum gust speed exceeded 35 m s(-1) in the areas in question. One of the most significant benefits associated with the results of this study is that, for the first time, there is a GIS-based area-wide quantification of P (DAM) in the forests in Southwestern Germany. In combination with the experience and expert knowledge of local foresters, the probability maps produced can be used as an important tool for decision support with respect to future silvicultural activities aimed at reducing wind damage. One limitation of the P (DAM)-predictions is that they are based on only one major storm event. At the moment it is not possible to relate storm event intensity to the amount of wind damage in forests due to the lack of comprehensive long-term tree and stand damage data across the study area.

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“…They attempted to analyze risk factor at landscape, stand, and single-tree scales. Lindemann and Baker (2002) used GIS with CART (Classification and Regression Tree) and logistic regression to analyze a severe forest blowndown in the Southern Rocky Mountains and found that the blowdown was most influenced by the factors pertaining to the physical setting. However, McMaster (2005) suggested that detailed site-specific factors such as average stem diameter, species, canopy height, and stand age are critical for improved accuracy of forest blowdown prediction.…”

Section: The Role Of the Predictive Models For Evaluating Wind Impactsmentioning

confidence: 99%

The Complexity of Catastrophic Wind Impacts on Temperate Forests

Xi¹,

Peet²

2011

Recent Hurricane Research - Climate, Dynamics, and Societal Impacts

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Using GIS to analyse a severe forest blowdown in the Southern Rocky Mountains

Cited by 24 publications

References 31 publications

Historic range of variability for upland vegetation in the Medicine Bow National Forest, Wyoming

Historic range of variability for upland vegetation in the Medicine Bow National Forest, Wyoming

Modelling the wind damage probability in forests in Southwestern Germany for the 1999 winter storm ‘Lothar’

The Complexity of Catastrophic Wind Impacts on Temperate Forests

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