Landscape-scale effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park

Kane, Van R.; Lutz, James A.; Roberts, Susan L.; Smith, Douglas; McGaughey, Robert J.; Povak, Nicholas A.; Brooks, Matthew L.

doi:10.1016/j.foreco.2012.08.044

Cited by 129 publications

(108 citation statements)

References 66 publications

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“…Subplot variability suggests that upscaling plot-based measures to the stand scale (as done to populate a landscape from plots for a 2-dimensional fire model) may be inaccurate and points to the necessity for a remote-sensing derived, landscape-scale ladder fuel metric that quantifies heterogeneity within stands. Our work demonstrates the potential application of LiDAR for quantifying ladder fuels, which has already been used to describe forest structure beneath the canopy [37,39,43]. Despite our fairly successful findings, questions concerning appropriate measurement scale and ladder fuel variability and clumpiness still need to be addressed.…”

Section: Discussionmentioning

confidence: 87%

“…Fusion software was used to extract a total of 53 different metrics accounting for topography (5) and forest structure (48) [61] (described in detail in Appendix Table A1). We also added six additional strata layers (2-4 m, 4-8 m, 8-16 m, 16-32 m, 32-48 m, and >48 m) that accounted for the relative cover in a layer based on hits within and below that layer, as described by Skowronski et al [48] and Kane et al [39]. No points below 2 m were used as the primary metric due to the potential error in differentiating ground points from vegetation, following Kane et al [39] (though point counts below 2 m were used to calibrate the strata layers described above).…”

Section: Lidar Data and Processingmentioning

confidence: 99%

“…We used fuel reduction treatment as a proxy for ladder fuels in order to narrow down the pool of LiDAR metrics to those that likely related to ladder fuels. Our pool of LiDAR metrics did not include strata below 2 m in height due to increased noise in the dataset [39] and fuels below 2 m generally being classified as surface fuel characteristics, leaving out this measure of fuel hazard. Our analysis used only one statistical model (classification trees), which converged on a single metric: 2-4 m cover.…”

Section: Relationship Between Treatments Lidar and Ladder Fuelsmentioning

confidence: 99%

“…Light Detection and Ranging (LiDAR) is a remote sensing technology that has proven well-suited to account for forest structure when collected from the ground or the air [37][38][39][40][41][42][43][44]. Terrestrial LiDAR has been used to estimate canopy height, canopy cover, CBH, and fuel strata gap on the plot scale [44].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Ladder Fuels: A New Approach Using LiDAR

Kramer

Collins

Kelly

et al. 2014

Forests

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Abstract:We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base height, but this has many potential sources of error. LiDAR may be a way forward to better characterize ladder fuels, but has only been used to address this question peripherally and in only a few instances. After establishing that landscape fuel treatments reduced canopy and ladder fuels at our site, we tested which LiDAR-derived metrics best differentiated treated from untreated areas. The percent cover between 2 and 4 m had the most explanatory power to distinguish treated from untreated pixels across a range of spatial scales. When compared to independent plot-based measures of ladder fuel classes, this metric differentiated between high and low levels of ladder fuels. These findings point to several immediate applications for land managers and suggest new avenues of study that could lead to possible improvements in the way that we model wildfire behavior across forested landscapes in the US. OPEN ACCESSForests 2014, 5 1433

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Section: Discussionmentioning

confidence: 87%

Section: Lidar Data and Processingmentioning

confidence: 99%

Section: Relationship Between Treatments Lidar and Ladder Fuelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying Ladder Fuels: A New Approach Using LiDAR

Kramer

Collins

Kelly

et al. 2014

Forests

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“…Por ejemplo, en los bosques tropicales, un solo incendio puede reducir la riqueza de las plantas leñosas de un tercio a dos tercios en función de la severidad (Cochrane, 2003). En cambio, en los bosques de coníferas, los incendios modifican tanto las dimensiones de la vegetación leñosa como la distribución de especies y dimensiones pero no necesariamente modifican su diversidad (Bond & Keeley, 2005;Kane et al, 2013;Wittkuhn et al, 2011). En este contexto, las características adaptativas de las plantas resultan de gran importancia ya que determinan el modo de recuperación post-incendio (Alanís et al, 2010;Bravo et al, 2012;Pausas, Bradstock, Keith, & Keeley, 2004).…”

Section: Interacción Espacial Del Arbolado Con La Variable Especieunclassified

Efecto De La Severidad Del Fuego en Las Características De La Estructura Forestal en Rodales De Coníferas

Ávila-Flores¹,

Tagle²,

Jiménez-Pérez³

et al. 2014

Rev Cha Se Cie For y del Amb

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RESUMENE l objetivo del presente trabajo fue realizar un análisis exploratorio de la distribución espacial del arbolado en rodales mixtos de coníferas de la Sierra Madre Oriental, afectados por un incendio en el verano de 1998, con el fin de conocer el grado en que la severidad de un incendio modifica las características estructurales de los mismos. El área seleccionada comprendió tres rodales mixtos de coníferas con tres grados de severidad de incendio (bajo, medio y alto). Los patrones de distribución espacial del arbolado se analizaron a través de la función K de Ripley. Asimismo, se analizó la interacción espacial de los árboles en relación con sus dimensiones y especies mediante una función de correlación de marca k mm (r). Se observó que conforme la severidad del incendio aumenta, la distribución espacial del arbolado tiende a la agregación y a la formación de rodales dimensionalmente heterogéneos, mientras que la composición de especies no fue afectada. El fuego influyó en estos bosques como un promotor de la diversidad dimensional, a pesar de que la mezcla espacial de especies tuvo una tendencia a la homogeneidad. ABSTRACT

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Mapping and exploring variation in post‐fire vegetation recovery following mixed severity wildfire using airborne LiDAR

Gordon

Price

Tasker³

2017

Ecological Applications

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There is a public perception that large high-severity wildfires decrease biodiversity and increase fire hazard by homogenizing vegetation composition and increasing the cover of mid-story vegetation. But a growing literature suggests that vegetation responses are nuanced. LiDAR technology provides a promising remote sensing tool to test hypotheses about post-fire vegetation regrowth because vegetation cover can be quantified within different height strata at fine scales over large areas. We assess the usefulness of airborne LiDAR data for measuring post-fire mid-story vegetation regrowth over a range of spatial resolutions (10 × 10 m, 30 × 30 m, 50 × 50 m, 100 × 100 m cell size) and investigate the effect of fire severity on regrowth amount and spatial pattern following a mixed severity wildfire in Warrumbungle National Park, Australia. We predicted that recovery would be more vigorous in areas of high fire severity, because park managers observed dense post-fire regrowth in these areas. Moderate to strong positive associations were observed between LiDAR and field surveys of mid-story vegetation cover between 0.5-3.0 m. Thus our LiDAR survey was an apt representation of on-ground vegetation cover. LiDAR-derived mid-story vegetation cover was 22-40% lower in areas of low and moderate than high fire severity. Linear mixed-effects models showed that fire severity was among the strongest biophysical predictors of mid-story vegetation cover irrespective of spatial resolution. However much of the variance associated with these models was unexplained, presumably because soil seed banks varied at finer scales than our LiDAR maps. Dense patches of mid-story vegetation regrowth were small (median size 0.01 ha) and evenly distributed between areas of low, moderate and high fire severity, demonstrating that high-severity fires do not homogenize vegetation cover. Our results are relevant for ecosystem conservation and fire management because they: indicate that native vegetation are responsive and resilient to high-severity fire, and show the usefulness of remote sensing tools such as LiDAR to monitor post-fire vegetation recovery over large areas in situ.

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Landscape-scale effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park

Cited by 129 publications

References 66 publications

Quantifying Ladder Fuels: A New Approach Using LiDAR

Quantifying Ladder Fuels: A New Approach Using LiDAR

Efecto De La Severidad Del Fuego en Las Características De La Estructura Forestal en Rodales De Coníferas

Mapping and exploring variation in post‐fire vegetation recovery following mixed severity wildfire using airborne LiDAR

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