High-severity Areas Research Articles

Intensified burn severity in California’s northern coastal mountains by drier climatic condition

The severity of wildfire burns in interior lands of western US ecosystems has been increasing. However, less is known about its coastal mountain ecosystems, especially under extreme weather conditions, raising concerns about the vulnerability of these populated areas to catastrophic fires. Here we examine the fine-scale association between burn severity and a suite of environmental drivers including explicit fuel information, weather, climate, and topography, for diverse ecosystems in California’s northern coastal mountains. Burn severity was quantified using Relative difference Normalized Burn Ratio from Landsat multispectral imagery during 1984–2017. We found a significant increasing trend in burned areas and severity. During low-precipitation years, areas that burned had much lower fuel moisture and higher climatic water deficit than in wetter years, and the percentage of high-severity areas doubled, especially during the most recent 2012–2016 drought. The random forest (RF) machine learning model achieved overall accuracy of 79% in classifying categories of burn severity. Aspect, slope, fuel type and availability, and temperature were the most important drivers, based on both classification and regression RF models. We further examined the importance of drivers under four climatic conditions: dry vs. wet years, and during two extended drought periods (the 2012–2016 warmer drought vs. the 1987–1992 drought). During warm and dry years, the spatial variability of burn severity was a mixed effect of slope, long-term minimum temperature, fuel amount, and fuel moisture. In contrast, climatic water deficit and short-term weather became dominant factors for fires during wetter years. These results suggest that relative importance of drivers for burn severity in the broader domain of California’s northern coastal mountains varied with weather scenarios, especially when exacerbated by warm and extended drought. Our findings highlight the importance of targeting areas with high burn severity risk for fire adaptation and mitigation strategies in a changing climate and intensifying extremes.

Proposal of a flash flood impact severity scale for the classification and mapping of flash flood impacts

Flash floods cause some of the most severe natural disasters around the world. The extensive diversity and discontinuity of flash flood impacts, which are controlled mostly by surface properties, lead to major difficulties in obtaining a holistic appraisal and a realistic overview of flash flood effects and their severity and make predicting future impacts a significant challenge. Current practices of describing flood impact severity use a limited set of criteria and assign a qualitative characterization (e.g. major, catastrophic, etc.) to each event.The present study proposes an approach that provides a coherent overview of these effects through the classification of impact types and severity and for the first time mapping their spatial extent in a continuous way across the floodplain. To this end, the flood effects are grouped into 4 categories depending on the affected elements, namely: (i) impacts on built environment (ii) impacts on man-made mobile objects, (iii) impacts on the natural environment (including vegetation, agriculture, geomorphology, and pollution) and (iv) impacts on the human population (entrapments, injuries, fatalities). Each of the four above categories is classified in a system of 10 severity classes that are defined with a logical order of increasing importance of damages forming a severity scale. The scale’s application is tested on a catastrophic flood event of 2017 in Mandra, Greece, with a well-described and wide range of impacts, including 24 fatalities.The application allowed the development of high-resolution impact-severity maps that revealed interesting damage patterns and highlighted high severity areas. The resulting maps offer insights on future impacts and indicate a potential to provide the groundwork for targeted prevention measures as well as correlations of impact severity with hydrological aspects of flooding.

How do soil microbial communities respond to fire in the intermediate term? Investigating direct and indirect effects associated with fire occurrence and burn severity

Fires transform soil microbial communities directly via heat-induced mortality and indirectly by altering plant and soil characteristics. Emerging evidence suggests the magnitude of changes to some plant and soil properties increases with burn severity, but the persistence of changes varies among plant and soil characteristics, ranging from months to years post-fire. Thus, which environmental attributes shape microbial communities at intermediate time points during ecosystem recovery, and how these characteristics vary with severity, remains poorly understood. We identified the network of properties that influence microbial communities three years after fire, along a burn severity gradient in Sierra Nevada mixed-conifer forest. We used phospholipid fatty acid (PLFA) analysis and bacterial 16S-rDNA amplicon sequencing to characterize the microbial community in mineral soil. Using structural equation modelling, we applied a systems approach to identifying the interconnected relationships among severity, vegetation, soil, and microbial communities. Dead tree basal area, soil pH, and extractable phosphorus increased with severity, whereas live tree basal area, forest floor mass, and the proportion of the ≥53 μm soil fraction decreased. Forest floor loss was associated with decreased soil moisture across the severity gradient, decreased live tree basal area was associated with increased shrub coverage, and increased dead tree basal area was associated with increases in total and inorganic soil nitrogen. Soil fungal abundance decreased across the severity gradient, despite a slightly positive response of fungi to lower soil moisture in high severity areas. Bacterial phylogenetic diversity was negatively related to severity and was driven by differences in nutrients and soil texture. The abundance of Bacteroidetes increased and the abundance of Acidobacteria decreased across the severity gradient due to differences in soil pH. Overall, we found that the effects of burn severity on vegetation and soil physicochemical characteristics interact to shape microbial communities at an intermediate time point in ecosystem recovery.

Assessing the effect of fire severity on sediment connectivity in central Chile

Chilean territory is recurrently affected by severe wildfires, which drastically reduce the forest cover and promote runoff, soil erosion and slope instabilities. To understand how the geomorphic system responds to wildfires in terms of sediment dynamics, the assessment of sediment connectivity, i.e. the property describing the relationships between compartments of a geomorphic system, is crucial. This study aims to quantify the spatial linkages between fire severity and sediment connectivity to identify common patterns and driving factors. The compound use of field data and open-source satellite imagery helped to apply the Relative differenced Normalized Burn Ratio (RdNBR) and the Index of Connectivity (IC) in the context of two consecutive wildfires, occurred in 2002 and 2015, in the Rio Toro catchment (Chile). The fire severity assessment showed that the 2002 event affected 90% of the catchment, with high severity areas representing around 70%. The 2015 wildfire instead, affected 76% of the catchment with moderate severity around 42%. Accordingly, as result of the sudden reduction in forest cover in severely affected areas, the IC changed after both wildfires with an overall increase of 1.07 and 0.54, respectively. However, only for the second disturbance, it was possible to observe a clear relationship between the RdNBR and the IC variations. The different degree of vegetation cover heterogeneity between the two pre-wildfire scenarios contributed to different fire severity and IC variability between the two disturbances. The use of open-source satellite data and the development of a weighting factor (W), to be used in IC and able to capture the land cover change driven by the wildfires, could make the application of this approach straightforward, promoting its reproducibility in other catchments for land management and risk mitigation purposes.

Initial response of understorey vegetation and tree regeneration to a mixed‐severity fire in old‐growth Araucaria–Nothofagus forests

AbstractQuestionsFire is a key factor influencing Araucaria araucana forests, but the impact of fire severity on the understorey vegetation is not well understood. In this study we seek to answer the following questions: (a) how do initial plant diversity, composition and spatial distribution of the understorey vegetation change in response to different levels of fire severity; and (b) does the abundance of dominant tree species exhibit different patterns across a fire severity gradient shortly after fire?LocationOld‐growth Araucaria araucana–Nothofagus pumilio forests in the Andes of south‐central Chile (38° S, 71° W) burned in 2015.MethodsWe evaluated the post‐fire plant regeneration across a fire severity gradient ranging from unburned forests to areas of high fire severity. One year after fire (in February 2016), we measured woody and herbaceous species richness, abundance, height, origin (native vs exotic species), life forms and the spatial pattern of plant recovery.ResultsPlant species richness and abundance were significantly higher within the unburned forest and low fire severity areas one year after fire, compared to areas of high and moderate fire severity. Overall, nearly 50% of the species present in the unburned forest were not found in areas of high severity, including the tree Nothofagus pumilio. Rapid vegetative resprouting of pioneer species such as Chusquea culeou resulted in an aggregated spatial distribution of plants after fire.ConclusionsPlant diversity and the abundance of Araucaria araucana and Nothofagus pumilio were reduced in areas of high fire severity one year after fire. Exotic species were more abundant within areas of low severity, being likely mediated by cattle browsing. Our research makes clear the potential changes in forest composition and structure if dominant tree species are not capable of recovering after fire. We recommend the exclusion of cattle within fire‐affected areas and planting Nothofagus pumilio in areas of high fire severity.

气候、植被和地形对大兴安岭林火烈度空间格局的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 气候、植被和地形对大兴安岭林火烈度空间格局的影响 DOI: 10.5846/stxb201902140264 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31570462）；江西省教育厅科学技术研究项目（GJJ160275） Effects of climate, vegetation, and topography on spatial patterns of burn severity in the Great Xing'an Mountains Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在北方森林中火干扰是森林景观变化的主导因素。林火烈度作为衡量林火动态的重要指标，较为直观地反映了火干扰对森林生态系统的破坏程度，其空间格局深刻地影响着森林景观中的多种生态过程（如树种组成、种子扩散以及植被的恢复）。解释林火烈度空间格局有助于揭示林火干扰后森林景观格局的形成机制，对预测未来林火烈度空间格局以及制定科学合理林火管理策略均有重要意义。基于LandsatTM/ETM遥感影像，将2000-2016年大兴安岭呼中林区的36场火的林火烈度划分为未过火、轻度、中度、重度4个等级。采用FRAGSTAT景观格局分析软件从类型水平上计算了斑块所占景观面积比、面积加权平均斑块面积、面积加权平均斑块分维数、面积加权边缘面积比、斑块密度5个景观指数，以对林火烈度空间格局进行了定量化描述。并且采用随机森林模型，分析了气候、地形、植被对林火烈度空间格局的影响及其边际效应。通过研究得出以下结果：（1）相对于未过火、轻度、以及中度火烧斑块，重度火烧斑块的面积更大、形状更简单；（2）海拔对重度火烧斑块的空间格局起着至关重要的作用，其次是坡向、坡度、植被覆盖度、相对湿度、温度等；（3）随着海拔的升高，面积加权平均斑块面积和面积加权平均斑块分维数的边际效应曲线呈上升趋势，而面积加权边缘面积比和斑块密度呈下降趋势；除了面积加权平均斑块面积外，都受到火前植被覆盖度的影响，且植被覆盖度为0.2-0.3范围内，重度火烧斑块在景观中所占比例最大。总的来看，2000-2016年大兴安岭呼中森林景观中重度火烧斑块与未过火、轻度以及中度火烧斑块存在显著差异性。相对于气候，地形和植被对于塑造重度火烧斑块空间格局具有重要作用。因此，应针对重度火烧区域进行可燃物处理，从景观层面上合理配置森林斑块，从而降低高烈度森林大火发生的风险。 Abstract:Fire is a major driver of forest landscape change in boreal forests. Burn severity is one of the main indexes for measuring the damage degree of fire on forest ecosystems. Spatial patterns of burn severity affect numerous ecological processes (e.g., species composition, seed dispersal, and vegetation restoration). Explaining spatial patterns of burn severity is conducive to reveal the formation mechanism of forest landscape patterns after fire, which is of great significance for predicting spatial patterns of burn severity in the future and formulating scientific fire management strategies. Based on Landsat TM/ETM remote sensing images, we mapped the burn severity of 36 fires that occurred between 2000 and 2016 in Huzhong forest region of the Great Xing'an Mountains by calculating the post-fire Normalized Burn Ratio index (NBR) and classified the fires into unburned, low, moderate and high severity classes. For each fire, we calculated five landscape metrics to quantitatively describe spatial patterns of burn severity at the class level using the FRAGSTATS program. The landscape pattern metrics were percentage of landscape (PLAND), area-weighted mean patch size (AREA_AM), area-weighted mean fractal dimension index (FRAC_AM), perimeter-area ratio (PARA_AM), and patch density (PD). Using Random Forest models, we analyzed the relative importance and marginal effects of weather, topography, and vegetation variables on determining spatial patterns of burn severity. The results showed that:1) compared with unburned, low-, and moderate-severity patches, the high-severity patches were more larger and simpler in shape. 2) Elevation played an important role in shaping spatial patterns of burn severity, followed by aspect, slope, vegetation coverage, relative humidity, and temperature. 3) With the increase in elevation, the marginal effect curve of area-weighted mean patch area and area-weighted mean patch fractal dimension showed an obvious increasing trend, whereas area-weighted perimeter-area ratio and patch density exhibited a decreasing trend. In addition to area-weighted mean patch area, all of them were affected by pre-fire vegetation coverage. When pre-fire vegetation coverage ranged fom 0.2 to 0.3, the proportion of high-severity patches in the landscape were the largest. In general, the high-severity patches differed significantly from unburned, low- and moderate-severity patches for five spatial pattern metrics. Topography and vegetation were more important in shaping the spatial pattern of high-severity patches than climate. Therefore, it would be urgent to implement forest fuel treatment in high-severity areas. It is necessary to allocate different forest patches reasonably from the landscape level, then to reduce the risk of high-severity forest large fires. 参考文献 相似文献 引证文献

Histogram Matching of Sentinel-2 Spectral Information to Enhance Planetscope Imagery for Effective Wildfire Damage Assessment

In abrupt fire disturbances, high quality images suitable for wildfire damage assessment can be difficult to acquire. Quantifying wildfire burn area and severity are essential measures for quick short-term disaster response and efficient long-term disaster restoration. Planetscope (PS) imagery offers 3 m spatial and daily temporal resolution, which can overcome the spatio-temporal resolution tradeoff of conventional satellites, albeit at the cost of spectral resolution. This study investigated the potential of augmenting PS imagery by integrating the spectral information from Sentinel-2 (S2) differenced Normalized Burn Ratio (dNBR) to PS differenced Normalized Difference Vegetation Index (dNDVI) using histogram matching, specifically for wildfire burn area and severity assessment of the Okgye wildfire which occurred on April 4th, 2019. Due to the difficulty in acquiring reference data, the results of the study were compared to the wildfire burn area reported by Ministry of the Interior and Safety. The burn area estimates from this study demonstrated that the histogram-matched (HM) PS dNDVI image produced more accurate burn area estimates and more descriptive burn severity intervals in contrast to conventional methods using S2. The HM PS dNDVI image returned an error of only 0.691% whereas the S2 dNDVI and dNBR images overestimated the wildfire burn area by 5.32% and 106%, respectively. These improvements using PS were largely due to the higher spatial resolution, allowing for the detection of sparsely distributed patches of land and narrow roads, which were indistinguishable using S2 dNBR. In addition, the integration of spectral information from S2 in the PS image resolved saturation effects in areas of low and high burn severity.

Spatial and temporal dynamics of 20th century carbon storage and emissions after wildfire in an old-growth forest landscape

Both fire exclusion and subsequent wildfires have strongly affected carbon storage in fire-prone dry forests, with implications for how carbon storage will change in the future. Using a reconstruction of forest structure in 1899 and pre- and post-fire field data, we quantified changes in carbon stocks in a 2125-ha old-growth mixed conifer forest landscape over a century of fire exclusion and emissions due to a 2013 wildfire. From 1899 to 2002 aboveground carbon storage in live trees increased 2.5-fold from 97 Mg/ha to 263 Mg/ha. Despite burning in an uncharacteristically severe wildfire, the forest still contained 169 Mg/ha of live aboveground tree carbon in 2014. Direct fire emissions were 72 Mg/ha and did not vary with canopy cover loss because emissions were largely driven by consumption of accumulated surface fuels. Areas that burned at low, moderate and high severity in the wildfire contained similar amounts of carbon in 1899, when the forest was still experiencing frequent low severity fire. By 2002 the low severity areas contained 80 and 86 Mg/ha more aboveground live tree carbon than moderate and high severity areas respectively. The wildfire reinforced and amplified these differences in carbon storage that arose during fire exclusion, such that carbon storage following the wildfire was more variable across the landscape. Additionally, the proportion of carbon stored in shade-intolerant, more fire-sensitive species increased. These changes in where and in what tree species carbon is stored, due to the combination of fire exclusion and wildfire, have implications for the potential future stability of these carbon stocks.

Semi-Empirical Modelling of Erosion Phenomena for Ice Crystal Icing Numerical Simulation

<div class="section abstract"><div class="htmlview paragraph">The aim of this work is to develop a semi-empirical model for erosion phenomena under ice crystal condition, which is one of the major phenomena for ice crystal accretion. Such a model would be able to calculate the erosion rate caused by impinging ice crystals on accreted ice layer.</div><div class="htmlview paragraph">This model is based on Finnie [<span class="xref">1</span>] and Bitter [<span class="xref">2</span>] [<span class="xref">3</span>] solid/solid collision theory which assumes that metal erosion due to sand impingement is driven by two phenomena: cutting wear and deformation wear. These two phenomena are strongly dependent on the particle density, velocity and shape, as well as on the surface physical properties such as Young modulus, Poisson ratio, surface yield strength and hardness. Moreover, cutting wear is mostly driven by tangential velocity and is more effective for ductile eroded body, whereas deformation wear is driven by normal velocity and is more effective for brittle eroded body. Several researchers based their erosion modelling on these two phenomena such as Hutchings et al. [<span class="xref">4</span>] for deformation erosion, or Huang et al. [<span class="xref">5</span>] and Arabnejad et al. [<span class="xref">6</span>] for cutting and deformation erosion.</div><div class="htmlview paragraph">The main work of this paper is to develop an erosion model for ice crystal impingement based on these two phenomena, and to show its capability to predict accretion shape by simulating experimental cases from the National Research Council of Canada (NRC). NRC’s Currie et al. ice crystal experiments [<span class="xref">7</span>] [<span class="xref">8</span>] realized in warm aerodynamic conditions, such as the one encountered in high icing severity areas of a turbofan engine, show accretion severity for a large range of liquid water content to total water content. In order to validate the erosion model based on solid/solid collision, this paper presents the simulation of the lower melting rate experiment. Results show fair agreement with experimental data and allow us to propose pertinent further work.</div></div>

Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining

Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.

Predicting increasing high severity area burned for three forested regions in the western United States using extreme value theory

More than 70 years of fire suppression by federal land management agencies has interrupted fire regimes in much of the western United States. The result of missed fire cycles is a buildup of both surface and canopy fuels in many forest ecosystems, increasing the risk of severe fire. The frequency and size of fires has increased in recent decades, as has the area burned with high severity in some ecosystems. A number of studies have examined controls on high severity fire occurrence, but none have yet determined what controls the extent of high severity fire. We developed statistical models predicting high severity area burned for the western United States and three sub-regions—the Northern Rocky Mountains, Sierra Nevada Mountains, and Southwest. A simple model with maximum temperature the month of fire, annual normalized moisture deficit and location explains area burned in high severity fire in our west-wide model, with the exception of years with especially large areas burned with high severity fire: 1988, 2002. With respect to mitigation or management of high severity fire, understanding what drives extreme fire years is critical. For the sub-regional models, topography, spring temperature and snowpack condition, and vegetation condition class variables improved our prediction of high severity burned area in extreme fire years. Fire year climate is critical to predicting area burned in high severity fire, especially in extreme fire years. These models can be used for scenario analyses and impact assessments to aid management in mitigating negative impacts of high severity fire.

Soil carbon pools and fluxes vary across a burn severity gradient three years after wildfire in Sierra Nevada mixed-conifer forest

Carbon (C) storage in soils contributes to the strength and stability of total ecosystem C sinks, but both aboveground and belowground C is vulnerable to loss during fire. The distribution of soil C and nitrogen (N) among various defined pools – e.g., active, slow and resistant C, and ammonium and nitrate as forms of inorganic N – determines the C storage capacity of forests and the nutrient availability for plant communities recovering from wildfires. Projections of increased wildfire severity due to a warming climate and frequent droughts raise concerns about parallel increases in fire's impacts on the sizes and mineralization kinetics of soil C and N pools, with potentially long-lasting effects on the strength of the forest C sink and on the ability of forests to recover from disturbance. Therefore, we sought to determine how the sizes and mineralization rates of soil C and N pools vary across a gradient of fire severity three years after the Chips Fire burned 30,500 ha of Sierra Nevada mixed-conifer forest. We measured total C and N in forest floor and mineral soil (0–5 cm), the pool sizes and mean residence times of the active, slow, and resistant C in mineral soil, and the pool sizes and mineralization rates of inorganic N in mineral soil. Forest floor total C was lower in areas that experienced high severity fire than in unburned reference areas, an effect likely attributable to greater combustion of forest floor material in high severity areas. Mineral soil C content did not vary with fire severity. Over a 300-day lab incubation, mineral soil CO2-C efflux rates were consistently lower in soils from areas that experienced high severity fire relative to unburned reference areas and were associated with longer mean residence times of the slow C pool. Forest floor N content was lower in high severity areas than unburned areas, whereas mineral soil total N did not vary with fire severity. Mineral soil ammonium and total inorganic N concentrations increased significantly with fire severity in field-fresh soils, but this trend was no longer apparent after a 300-day lab incubation, indicating that site-specific factors control N availability among fire severity levels. Our results indicate that future increases in wildfire severity in mixed-conifer forest may alter the strength of the forest C sink by impacting the amount C stored in forest floor, the stability of mineral soil C, and the availability of N to recovering plant communities.

The changing landscape of wildfire: burn pattern trends and implications for California’s yellow pine and mixed conifer forests

Wildfire spatial patterns drive ecological processes including vegetation succession and wildlife community dynamics. Such patterns may be changing due to fire suppression policies and climate change, making characterization of trends in post-fire mosaics important for understanding and managing fire-prone ecosystems. For wildfires in California’s yellow pine and mixed-conifer forests, spatial pattern trends of two components of the post-fire severity matrix were assessed for 1984–2015: (1) unchanged or very low-severity and (2) high-severity, which represent remnant forest and stand-replacing fire, respectively. Trends were evaluated for metrics of total and proportional burned area, shape complexity, aggregation, and core area. Additionally, comparisons were made between management units where fire suppression is commonly practiced and those with a history of managing wildfire for ecological/resource benefits. Unchanged or very low-severity area per fire decreased proportionally through time, and became increasingly fragmented. High-severity area and core area increased on average across most of California, with the high-severity component also becoming simpler in shape in the Sierra Nevada. Compared to suppression units, managed wildfire units lack an increase in high-severity area, have less aggregated post-fire mosaics, and more high-severity spatial complexity. Documented changes in severity patterns have cascading ecological effects including increased vegetation type conversion risk, habitat availability shifts, and remnant forest fragmentation. These changes likely benefit early-seral-associated species at the expense of mature closed-canopy forest-associated species. Managed wildfire appears to moderate some effects of fire suppression, and may help buy time for ecosystems and managers to respond to a changing climate.

Composition and Structure of Forest Fire Refugia: What Are the Ecosystem Legacies across Burned Landscapes?

Locations within forest fires that remain unburned or burn at low severity—known as fire refugia—are important components of contemporary burn mosaics, but their composition and structure at regional scales are poorly understood. Focusing on recent, large wildfires across the US Pacific Northwest (Oregon and Washington), our research objectives are to (1) classify fire refugia and burn severity based on relativized spectral change in Landsat time series; (2) quantify the pre-fire composition and structure of mapped fire refugia; (3) in forested areas, assess the relative abundance of fire refugia and other burn severity classes across forest composition and structure types. We analyzed a random sample of 99 recent fires in forest-dominated landscapes from 2004 to 2015 that collectively encompassed 612,629 ha. Across the region, fire refugia extent was substantial but variable from year to year, with an annual mean of 38% of fire extent and range of 15–60%. Overall, 85% of total fire extent was forested, with the other 15% being non-forest. In comparison, 31% of fire refugia extent was non-forest prior to the most recent fire, highlighting that mapped refugia do not necessarily contain tree-based ecosystem legacies. The most prevalent non-forest cover types in refugia were vegetated: shrub (40%), herbaceous (33%), and crops (18%). In forested areas, the relative abundance of fire refugia varied widely among pre-fire forest types (20–70%) and structural conditions (23–55%). Consistent with fire regime theory, fire refugia and high burn severity areas were inversely proportional. Our findings underscore that researchers, managers, and other stakeholders should interpret burn severity maps through the lens of pre-fire land cover, especially given the increasing importance of fire and fire refugia under global change.

Validación de los índices de teledetección dNBR y RdNBR para determinar la severidad del fuego en el incendio forestal de Oia-O Rosal (Pontevedra) en 2013

<p>Fire severity evaluation and mapping following wildfire is an essential task for post-fire rehabilitation activities and forest management planning. For that purpose, some spectral indexes are used to quantify the changes caused by fire, being Landsat satellite one of the most frequently used. Even though Galicia is the Spanish region with the highest number of fires in the country, the information on fire severity estimation through satellite imagery is scarce. In the present study, the capacity of dNBR (differenced Normalized Burn Ratio) and RdNBR (Relative difference Normalized Burn Ratio), through Landsat 8 imagery processing, are compared for the first time in Galicia to test both indexes with field data following the methodology from CBI (Composite Burn Index) in Oia-O Rosal (Pontevedra) wildfire occurred in the summer of 2013. The results indicate that the models for dNBR and RdNBR estimation according to CBI were similar, explaining a 69 and 73% of variability, respectively. These models allow to obtain a new fire severity thresholds for dNBR and RdNBR for the burned area. Although, both indexes showed a similar and quite high overall accuracy in the classification of the different fire severity classes (75% y 83% for dNBR and RdNBR, respectively), RdNBR was slightly more accurate than dNBR. Additionally, the dNBR-based fire severity map significantly underestimated the high fire severity area, compared with RdNBR. Those preliminary results can be useful to evaluate fire severity spatial distribution, in wildfires in Galicia although new data will be necessary before an operational tool to be available.</p>

Tortoise or hare: Will resprouting oaks or reseeding pines dominate following severe wildfire?

Abstract Post-fire shifts in species composition and growth are driven in part by alterations in resource availability. However, less is known about the extent to which differences in fire severity will affect leaf-level physiological responses in co-occurring species. Therefore, this study tested loblolly pine (Pinus taeda L.) saplings’ and resprouting oaks’ (Quercus stellata Wangenh. (post oak) and Q. marilandica Muenchh. (blackjack oak)) responses to various stresses following a catastrophic wildfire in 2011 that occurred in the Lost Pines region in Texas, USA. General plant responses (height, stem number, and plant density) and leaf-level stress indicators [gas exchange (photosynthesis (A), transpiration (E) and stomatal conductance (gs), pressure-volume parameters, and leaf biochemistry] were monitored along resource availability gradients (two burn severities and three soil types) in the summer of 2016. Nearly 5 years after the fire, there were 180% more pines in moderate severity transects than high severity transects. However, pines in high severity areas were similar in height to those growing in moderate severity areas. Pines consistently had low A, E, and gs rates when compared to co-occurring oaks. Pine osmotic potential at full turgor (ΠFT), turgor loss point (ΠTLP), and bulk modulus of elasticity (e) were 33, 30, and 58% greater, respectively, than those of oaks. Loblolly pine leaf-level gas exchange and pressure-volume results suggest this species adopts a drought-avoidance strategy for survival. Low gas exchange rates in pine saplings may reflect greater stomatal control, which could reduce cavitation risks, or may have arisen from reduced access to water resources by saplings’ shallow roots. Gas exchange and pressure-volume parameters of the oaks reflect greater drought tolerance than pine, with post oak being the more tolerant of the two oak species. Leaf-level gas exchange and pressure-volume measurements were coordinated, and results suggest that post oak utilized osmotic adjustment to maintain gas exchange in the more exposed, high severity areas. Additionally, differences in foliar nutrient and phenolic contents between oaks and pines may reflect differences in their leaf-level gas exchange capabilities. Results suggest loblolly pine regeneration may be slower in high burn-severity areas, and that differences in physiological strategies to cope with water stress among the three species may shift stand composition to oak dominance in parts of this region following wildfire.

Fire Severity and Regeneration Strategy Influence Shrub Patch Size and Structure Following Disturbance

Climate change is increasing the frequency and extent of high-severity disturbance, with potential to alter vegetation community composition and structure in environments sensitive to tipping points between alternative states. Shrub species display a range of characteristics that promote resistance and resilience to disturbance, and which yield differential post-disturbance outcomes. We investigated differences in shrub patch size and stem density in response to variations in fire severity, vegetation community, and post-disturbance reproductive strategies in Sky Island forested ecosystems in the southwestern United States. Patterns in shrub structure reflect the effects of fire severity as well as differences among species with alternate post-fire reproductive strategies. Increased fire severity correlates with larger patch sizes and greater stem densities; these patterns are observed across multiple fire events, indicating that disturbance legacies can persist for decades. High severity fire produces the largest shrub patches, and variance in shrub patch size increases with severity. High severity fire is likely to promote expansion of shrub species on the landscape, with implications for future community structure. Resprouting species have the greatest variability in patch structure, while seeding species show a strong response to disturbance: resprouting species dominate at low disturbance severities, and obligate seeders dominate high severity areas. Differential post-fire reproductive strategies are likely to generate distinct patterns of vegetation distribution following disturbance, with implications for community composition at various scales. Shrub species demonstrate flexible responses to wildfire disturbance severity that are reflected in shrub patch dynamics at small and intermediate scales.

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

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 (10×10m, 30×30m, 50×50m, 100×100m 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 0.5-3.0m. 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 0.01ha) 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 areasinsitu.

Fire-climate history and landscape patterns of high burn severity areas on the California southern and central coast

There is mounting evidence that fire size and severity have been growing on the central and southern California coastal landscape over the past several decades. Landsat satellite data was analyzed for the 20 largest fires on the Central California coast since 1984 to determine the relationships between climate/weather conditions at the time of ignition and the size of high burn severity (HBS) areas. The study also examined the relationship between area burned and landscape patterns of HBS coverage, including patch size, edge complexity, perimeter-to-area ratio, and aggregation metrics. Results showed that climate conditions at the time of ignitions have been significant controllers of the total area of HBS and the complexity of HBS patches on the fire landscape. As maximum air temperatures for the month of ignition approached 40o C, the percentage of HBS to total area burned frequently exceeded 20%. The percentage of HBS to total area burned also exceed 20% when the precipitation total recorded during the previous 12 months was less than 25% of the annual average precipitation. Landscape analysis results showed that, as the total area burned in fires on the Central California coast grows, the edge lengths and areas of HBS patches also grows at a rapid rate. At the same time, the perimeter-to-area ratio of HBS patches decreases gradually and the HBS patches become more aggregated as total burned area grows.

Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia

ABSTRACTRemote-sensing methods for fire severity mapping have traditionally relied on multispectral imagery captured by satellite platforms carrying passive sensors such as Landsat Thematic Mapper /Enhanced Thematic Mapper Plus or Moderate Resolution Imaging Spectroradiometer. This article describes the analysis of high spatial resolution Unmanned Aerial Vehicle (UAV) imagery to assess fire severity on a 117 ha experimental fire conducted on coal mine rehabilitation in an open woodland environment in semi-arid Central Queensland, Australia. Three band indices, Excess Green Index, Excess Green Index Ratio, and Modified Excess Green Index, were used to derive differenced (d) fire severity maps from UAV data. Fire severity data sets derived from aerial photograph interpretation were used to assess the utility of employing UAV technology to determine fire severity impacts. The dEGI was able to separate high severity, low severity, and unburnt areas with an overall classification accuracy of 58% and Kappa statistic of 0.37; outperforming the dEGIR (overall accuracy 55%, Kappa 0.31) and the dMEGI (overall accuracy 38%, Kappa 0.06). Classification accuracy increased for all indices when canopy shadows were masked, with dEGI improving to an overall accuracy of 68% and 0.48 Kappa. The McNemar’s test indicated that there was no significant difference between the classification accuracies for dEGI and dEGIR (p < 0.05). The test also demonstrated that dMEGI was significantly lower in accuracy compared to dEGI and dEGIR (p < 0.05). We quantified the proportion of burnt area within each severity class and calculated that 32% of the site was burnt at high severity, 34% was burnt at low severity, and 34% of the block was unburnt due to the patchy nature of the fire. We discuss the UAV-specific errors associated with fire severity mapping, and the potential for UAVs to assist land managers to assess the extent and severity of fire and subsequent recovery of burnt ecosystems at local scales (104m2–1 km2).