Knowing fire incidence through fuel phenology: A remotely sensed approach

Fire is a basic ecological factor that contributes to determine vegetation diversity and dynamics in time and space. Fuel characteristics play an essential role in fire ignition and propagation; at the landscape scale fuel availability and flammability are closely related to the vegetation phenology that directly affects wildfire pattern in time and space. In this view, the annual normalized difference vegetation index (NDVI) profiles derived from high temporal resolution satellites, like SPOT Vegetation, represent an effective tool for monitoring the coarse-scale vegetation seasonal timing. The objective of this study thus consists in quantifying the explanatory power of multitemporal NDVI profiles on the fire regime characteristics of the potential natural vegetation (PNV) types of Sardinia (Italy) over a 5-year period (2000-2004). The results obtained show a good association between the NDVI temporal dynamics of the PNV of Sardinia and the corresponding fire regime characteristics, emphasizing the role of the bioclimatic timing of the vegetation in controlling the coarse-scale wildfire spatio-temporal distribution of Sardinia. By providing a sound phytogeographical framework for describing different wildfire regimes, PNV maps can thus be considered helpful cartographic documents for fire management strategies at the landscape scale.     

A spatially explicit model to predict future landscape composition of aspen woodlands under various management scenarios

Quaking aspen (Populus tremuloides) is declining across the western United States. Aspen habitats are among the most diverse plant communities in this region and loss of these habitats can result in shifts in biodiversity, productivity, and hydrology across a range of spatial scales. Western aspen occurs on the majority of sites seral to conifer species, and long-term maintenance of these aspen woodlands requires periodic fire. Over the past century, fire intervals, extents, and intensities have been insufficient to regenerate aspen stands at historic rates; however the effects of various fire regimes and management scenarios on aspen vegetation dynamics at broad spatial and temporal scales are unexplored. Here we use field data, remotely sensed data, and fire atlas information to develop a spatially explicit landscape simulation model to assess the effects of current and historic wildfire regimes and prescribed burning programs on landscape vegetation composition across two mountain ranges in the Owyhee Plateau, Idaho. Model outputs depict the future structural makeup and species composition of the landscape at selected time steps under simulated management scenarios. We found that under current fire regimes and in the absence of management activities, loss of seral aspen stands will continue to occur over the next two centuries. However, a return to historic fire regimes (burning 12–14% of the modeled landscape per decade) would maintain the majority of aspen stands in early and mid seral woodland stages and minimizes the loss of aspen. A fire rotation of 70–80 years was estimated for the historic fire regime while the current fire regime resulted in a fire rotation of 340–450 years, underscoring the fact that fire is currently lacking in the system. Implementation of prescribed burning programs, treating aspen and young conifer woodlands according to historic fire occurrence probabilities, are predicted to prevent conifer dominance and loss of aspen stands.     

Rapid assessment of postfire plant invasions in coniferous forests of the western United States.

Fire is a natural part of most forest ecosystems in the western United States, but its effects on nonnative plant invasion have only recently been studied. Also, forest managers are engaging in fuel reduction projects to lessen fire severity, often without considering potential negative ecological consequences such as nonnative plant species introductions. Increased availability of light, nutrients, and bare ground have all been associated with high-severity fires and fuel treatments and are known to aid in the establishment of nonnative plant species. We use vegetation and environmental data collected after wildfires at seven sites in coniferous forests in the western United States to study responses of nonnative plants to wildfire. We compared burned vs. unburned plots and plots treated with mechanical thinning and/or prescribed burning vs. untreated plots for nonnative plant species richness and cover and used correlation analyses to infer the effect of abiotic site conditions on invasibility. Wildfire was responsible for significant increases in nonnative species richness and cover, and a significant decrease in native cover. Mechanical thinning and prescribed fire fuel treatments were associated with significant changes in plant species composition at some sites. Treatment effects across sites were minimal and inconclusive due to significant site and site x treatment interaction effects caused by variation between sites including differences in treatment and fire severities and initial conditions (e.g., nonnative species sources). We used canonical correspondence analysis (CCA) to determine what combinations of environmental variables best explained patterns of nonnative plant species richness and cover. Variables related to fire severity, soil nutrients, and elevation explained most of the variation in species composition. Nonnative species were generally associated with sites with higher fire severity, elevation, percentage of bare ground, and lower soil nutrient levels and lower canopy cover. Early assessments of postfire stand conditions can guide rapid responses to nonnative plant invasions.     

Simulating dynamic and mixed-severity fire regimes: A process-based fire extension for LANDIS-II

Fire regimes result from reciprocal interactions between vegetation and fire that may be further affected by other disturbances, including climate, landform, and terrain. In this paper, we describe fire and fuel extensions for the forest landscape simulation model, LANDIS-II, that allow dynamic interactions among fire, vegetation, climate, and landscape structure, and incorporate realistic fire characteristics (shapes, distributions, and effects) that can vary within and between fire events. We demonstrate the capabilities of the new extensions using two case study examples with very different ecosystem characteristics: a boreal forest system from central Labrador, Canada, and a mixed conifer system from the Sierra Nevada Mountains (California, USA). In Labrador, comparison between the more complex dynamic fire extension and a classic fire simulator based on a simple fire size distribution showed little difference in terms of mean fire rotation and potential severity, but cumulative burn patterns created by the dynamic fire extension were more heterogeneous due to feedback between fuel types and fire behavior. Simulations in the Sierra Nevada indicated that burn patterns were responsive to topographic features, fuel types, and an extreme weather scenario, although the magnitude of responses depended on elevation. In both study areas, simulated fire size and resulting fire rotation intervals were moderately sensitive to parameters controlling the curvilinear response between fire spread and weather, as well as to the assumptions underlying the correlation between weather conditions and fire duration. Potential fire severity was more variable within the Sierra Nevada landscape and also was more sensitive to the correlation between weather conditions and fire duration. The fire modeling approach described here should be applicable to questions related to climate change and disturbance interactions, particularly within locations characterized by steep topography, where temporally or spatially dynamic vegetation significantly influences spread rates, where fire severity is variable, and where multiple disturbance types of varying severities are common.     

Fire severity and ecosytem responses following crown fires in California shrublands

Chaparral shrublands burn in large high-intensity crown fires. Managers interested in how these wildfires affect ecosystem processes generally rely on surrogate measures of fire intensity known as fire severity metrics. In shrublands burned in the autumn of 2003, a study of 250 sites investigated factors determining fire severity and ecosystem responses. Using structural equation modeling we show that stand age, prefire shrub density, and the shortest interval of the prior fire history had significant direct effects on fire severity, explaining > 50% of the variation in severity. Fire severity per se is of interest to resource managers primarily because it is presumed to be an indicator of important ecosystem processes such as vegetative regeneration, community recovery, and erosion. Fire severity contributed relatively little to explaining patterns of regeneration after fire. Two generalizations can be drawn: fire severity effects are mostly shortlived, i.e., by the second year they are greatly diminished, and fire severity may have opposite effects on different functional types. Species richness exhibited a negative relationship to fire severity in the first year, but fire severity impacts were substantially less in the second postfire year and varied by functional type. Much of this relationship was due to alien plants that are sensitive to high fire severity; at all scales from 1 to 1000 m2, the percentage of alien species in the postfire flora declined with increased fire severity. Other aspects of disturbance history are also important determinants of alien cover and richness as both increased with the number of times the site had burned and decreased with time since last fire. A substantial number of studies have shown that remote-sensing indices are correlated with field measurements of fire severity. Across our sites, absolute differenced normalized burn ratio (dNBR) was strongly correlated with field measures of fire severity and with fire history at a site but relative dNBR was not. Despite being correlated with fire severity, absolute dNBR showed little or no relationship with important ecosystem responses to wildfire such as shrub resprouting or total vegetative regeneration. These findings point to a critical need for further research on interpreting remote sensing indices as applied to postfire management of these shrublands.     

Modeling mammal response to fire based on species’ traits

Fire has shaped ecological communities worldwide for millennia, but impacts of fire on individual species are often poorly understood. We performed a meta-analysis to predict which traits, habitat, or study variables and fire characteristics affect how mammal species respond to fire. We modeled effect sizes of measures of population abundance or occupancy as a function of various combinations of these traits and variables with phylogenetic least squares regression. Nine of 115 modeled species (7.83%) returned statistically significant effect sizes, suggesting most mammals are resilient to fire. The top-ranked model predicted a negative impact of fire on species with lower reproductive rates, regardless of fire type (estimate = –0.68), a positive impact of burrowing in prescribed fires (estimate = 1.46) but not wildfires, and a positive impact of average fire return interval for wildfires (estimate = 0.93) but not prescribed fires. If a species’ International Union for Conservation of Nature Red List assessment includes fire as a known or possible threat, the species was predicted to respond negatively to wildfire relative to prescribed fire (estimate = –2.84). These findings provide evidence of experts’ abilities to predict whether fire is a threat to a mammal species and the ability of managers to meet the needs of fire-threatened species through prescribed fire. Where empirical data are lacking, our methods provide a basis for predicting mammal responses to fire and thus can guide conservation actions or interventions in species or communities.     

Wildfire smoke and health impacts: A closer look at fire attributes and their marginal effects

Existing studies on the economic impact of wildfire smoke have focused on single fire events or entire seasons without considering the marginal effect of daily fire progression on downwind communities. Neither approach allows for an examination of the impact of even the most basic fire attributes, such as distance and fuel type, on air quality and health outcomes. Improved knowledge of these effects can provide important guidance for efficient wildfire management strategies. This study aims to bridge this gap using detailed information on 24 large-scale wildfires that sent smoke plumes to the Reno/Sparks area of Northern Nevada over a 4-year period. We relate the daily acreage burned by these fires to daily data on air pollutants and local hospital admissions. Using information on medical expenses, we compute the per-acre health cost of wildfires of different attributes. We find that patient counts can be causally linked to fires as far as 200–300 miles from the impact area. As expected, the marginal impact per acre burned generally diminishes with distance and for fires with lighter fuel loads. Our results also highlight the importance of allowing for temporal lags between fire occurrence and pollutant levels.     

Human influence on California fire regimes.

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.     

Evidence, exaggeration, and error in historical accounts of chaparral wildfires in California.

For more than half a century, ecologists and historians have been integrating the contemporary study of ecosystems with data gathered from historical sources to evaluate change over broad temporal and spatial scales. This approach is especially useful where ecosystems were altered before formal study as a result of natural resources management, land development, environmental pollution, and climate change. Yet, in many places, historical documents do not provide precise information, and pre-historical evidence is unavailable or has ambiguous interpretation. There are similar challenges in evaluating how the fire regime of chaparral in California has changed as a result of fire suppression management initiated at the beginning of the 20th century. Although the firestorm of October 2003 was the largest officially recorded in California (approximately 300,000 ha), historical accounts of pre-suppression wildfires have been cited as evidence that such a scale of burning was not unprecedented, suggesting the fire regime and patch mosaic in chaparral have not substantially changed. We find that the data do not support pre-suppression megafires, and that the impression of large historical wildfires is a result of imprecision and inaccuracy in the original reports, as well as a parlance that is beset with hyperbole. We underscore themes of importance for critically analyzing historical documents to evaluate ecological change. A putative 100 mile long by 10 mile wide (160 x 16 km) wildfire reported in 1889 was reconstructed to an area of chaparral approximately 40 times smaller by linking local accounts to property tax records, voter registration rolls, claimed insurance, and place names mapped with a geographical information system (GIS) which includes data from historical vegetation surveys. We also show that historical sources cited as evidence of other large chaparral wildfires are either demonstrably inaccurate or provide anecdotal information that is immaterial in the appraisal of pre-suppression fire size. Since historical evidence is inadequate for reconstructing a statistical distribution of pre-suppression fire sizes to compare with post-suppression data, other more propitious methods of evaluating change are discussed.     

Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty

Fire managers need to study fire history in terms of occurrence in order to understand and model the spatial distribution of the causes of ignition. Fire atlases are useful open sources of information, recording each single fire event by means of its geographical position. In such cases the fire event is considered as point-based, rather than area-based data, completely losing its surface nature. Thus, an accurate method is needed to estimate continuous density surfaces from ignition points where location is affected by a certain degree of uncertainty. Recently, the fire scientific community has focused its attention on the kernel density interpolation technique in order to convert point-based data into continuous surface or surface-data. The kernel density technique needs a priori setting of smoothing parameters, such as the bandwidth size. Up to now, the bandwidth size was often based on subjective choices still needing expert knowledge, eventually supported by empirical decisions, thus leading to serious uncertainties. Nonetheless, a geostatistical model able to describe the point concentration and consequently the clustering degree is required. This paper tries to solve such issues by implementing the kernel density adaptive mode. Lightning/human-caused fires occurrence was investigated in the region of Aragón's autonomy over 19 years (1983–2001) using 3428 and 4195 ignition points respectively for the two causes of fire origin. An analytical calibration procedure was implemented to select the most reliable density surfaces to reduce under/over-density estimation, overcoming the current drawbacks to define it by visual inspection or personal interpretation. Besides, ignition point location uncertainty was investigated to check the sensitivity of the proposed model. The different concentration degree and the dissimilar spatial pattern of the two datasets, allow testing the proposed calibration methodology under several conditions. After having discovered the slight sensitivity of the model to the exact point position, the obtained density surfaces for the two causes were combined to discover hotspot areas and spatial patterns of the two causes. Evident differences in spatial location of the origin causes were noted and described. The general trend follows the geographical features and the human activity of the study areas. The proposed technique should be promising to support decision-making in wildfire prevention actions, because of the occurrence map can be used as a response variable in fire risk predicting models.     

Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban-forest interface are important factors for wildfire ignition.     

Rapid Increases and Time‐Lagged Declines in Amphibian Occupancy after Wildfire

Climate change is expected to increase the frequency and severity of drought and wildfire. Aquatic and moisture‐sensitive species, such as amphibians, may be particularly vulnerable to these modified disturbance regimes because large wildfires often occur during extended droughts and thus may compound environmental threats. However, understanding of the effects of wildfires on amphibians in forests with long fire‐return intervals is limited. Numerous stand‐replacing wildfires have occurred since 1988 in Glacier National Park (Montana, U.S.A.), where we have conducted long‐term monitoring of amphibians. We measured responses of 3 amphibian species to fires of different sizes, severity, and age in a small geographic area with uniform management. We used data from wetlands associated with 6 wildfires that burned between 1988 and 2003 to evaluate whether burn extent and severity and interactions between wildfire and wetland isolation affected the distribution of breeding populations. We measured responses with models that accounted for imperfect detection to estimate occupancy during prefire (0–4 years) and different postfire recovery periods. For the long‐toed salamander (Ambystoma macrodactylum) and Columbia spotted frog (Rana luteiventris), occupancy was not affected for 6 years after wildfire. But 7–21 years after wildfire, occupancy for both species decreased ≥25% in areas where >50% of the forest within 500 m of wetlands burned. In contrast, occupancy of the boreal toad (Anaxyrus boreas) tripled in the 3 years after low‐elevation forests burned. This increase in occupancy was followed by a gradual decline. Our results show that accounting for magnitude of change and time lags is critical to understanding population dynamics of amphibians after large disturbances. Our results also inform understanding of the potential threat of increases in wildfire frequency or severity to amphibians in the region. Incrementos Rápidos y Declinaciones Desfasadas en la Ocupación de Anfibios Después de un Incendio     

Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications

Understanding and being able to predict forest fire occurrence, fire growth and fire intensity are important aspects of forest fire management. In Canada fire management agencies use the Canadian Forest Fire Danger Rating System (CFFDRS) to help predict these elements of forest fire activity. In this paper a review of the CFFDRS is presented with the main focus on understanding and interpreting Canadian Fire Weather Index (FWI) System outputs. The need to interpret the outputs of the FWI System with consideration to regional differences is emphasized and examples are shown of how the relationship between actual fuel moisture and the FWI System’s moisture codes vary from region to region. Examples are then shown of the relationship between fuel moisture and fire occurrence for both human- and lightning-caused fire for regions with different forest composition. The relationship between rate of spread, fuel consumption and the relative fire behaviour indices of the FWI System for different forest types is also discussed. The outputs of the CFFDRS are used every day across Canada by fire managers in every district, regional and provincial fire management office. The purpose of this review is to provide modellers with an understanding of this system and how its outputs can be interpreted. It is hoped that this review will expose statistical modellers and other researchers to some of the models used currently in forest fire management and encourage further research and development of models useful for understanding and managing forest fire activity.

A method for mapping fire hazard and risk across multiple scales and its application in fire management

This paper presents modeling methods for mapping fire hazard and fire risk using a research model called FIREHARM (FIRE Hazard and Risk Model) that computes common measures of fire behavior, fire danger, and fire effects to spatially portray fire hazard over space. FIREHARM can compute a measure of risk associated with the distribution of these measures over time using 18 years of gridded DAYMET daily weather data used to simulate fuel moistures to compute fire variables. We detail the background, structure, and application of FIREHARM and then present validation results of six of the FIREHARM output variables that revealed accuracy rates ranging from 20 to 80% correct depending on the quality of input data and the behavior of the fire behavior simulation framework. Overall accuracies appeared acceptable for prioritization analysis and large scale assessments because precision was high. We discuss advantages and disadvantages of the fire hazard and risk approaches and a possible agenda for future development of comprehensive fire hazard and risk mapping is presented.     

Spatio-temporal log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 1994–2008

Wildfires have become one of the principal environmental problems in the Mediterranean basin. While fire plays an important role in most terrestrial plant ecosystems, the potential hazard that it represents for human lives and property has led to the application of fire exclusion policies that, in the long term, have caused severe damage, mainly due to the increase of fuel loadings in forested areas, in some forest systems. The lack of an easy solution to forest fire management highlights the importance of preventive tasks. The observed spatio-temporal pattern of wildfire occurrences may be idealized as a realization of some stochastic process. In particular, we may use a space–time point pattern approach for the analysis and inference process. We studied wildfires in Catalonia, a region in the north-east of the Iberian Peninsula, and we analyzed the spatio-temporal patterns produced by those wildfire incidences by considering the influence of covariates on trends in the intensity of wildfire locations. A total of 3,166 wildfires from 1994–2008 have been recorded. We specified spatio-temporal log-Gaussian Cox process models. Models were estimated using Bayesian inference for Gaussian Markov Random Field through the integrated nested Laplace approximation algorithm. The results of our analysis have provided statistical evidence that areas closer to humans have more human induced wildfires, areas farther have more naturally occurring wildfires. We believe the methods presented in this paper may contribute to the prevention and management of those wildfires which are not random in space or time.     

A Hierarchical Bayesian model of wildfire in a Mediterranean biodiversity hotspot: Implications of weather variability and global circulation

In this study we combined an extensive database of observed wildfires with high-resolution meteorological data to build a novel spatially and temporally varying survival model to analyze fire regimes in the Mediterranean ecosystem in the Cape Floristic Region (CFR) of South Africa during the period 1980-2000. The model revealed an important influence of seasonally anomalous weather on fire probability, with increased probability of fire in seasons that are warmer and drier than average. In addition to these local-scale influences, the Antarctic Ocean Oscillation (AAO) was identified as an important large-scale influence or teleconnection to global circulation patterns. Fire probability increased in seasons during positive AAO phases, when the subtropical jet moves northward and low level moisture transport decreases. These results confirm that fire occurrence in the CFR is strongly affected by climatic variability at both local and global scales, and thus likely to respond sensitively to future climate change. Comparison of the modelled fire probability between two periods (1951-1975 and 1976-2000) revealed a 4-year decrease in an average fire return time. If, as currently forecasted, climate change in the region continues to produce higher temperatures, more frequent heat waves, and/or lower rainfall, our model thus indicates that fire frequency is likely to increase substantially. The regional implications of shorter fire return times include shifting community structure and composition, favoring species that tolerate more frequent fires.     

Modeling the influence of precipitation and nitrogen deposition on forest understory fuel connectivity in Sierra Nevada mixed-conifer forest

Climate change models for California's Sierra Nevada predict greater inter-annual variability in precipitation over the next 50 years. These increases in precipitation variability coupled with increases in nitrogen deposition from fossil fuel consumption are likely to result in increased productivity levels and significant increases in forest understory fuel loads. Higher understory plant biomass contributes to fuel connectivity and may increase future fire size and severity in the Sierra Nevada. The objective of this research was to develop and test a model to determine how changing precipitation and nitrogen deposition levels affect shrub and herb biomass production, and to determine how often prescribed fire would be needed to counter increasing fuel loads. Model outputs indicate that under an increasing precipitation scenario significant increases in shrub and herb biomass occur that can be counteracted by decreasing the fire return interval to 10 years. Under a scenario with greater inter-annual variability in precipitation and increased nitrogen deposition, implementing fire treatments at an interval equivalent to the historical range of 15–30 years maintains understory vegetation fuel loads at levels comparable to the control.     

Cicada emergence in Southwestern riparian forest: influences of wildfire and vegetation composition.

Annually emerging cicadas are a numerically and ecologically dominant species in Southwestern riparian forests. Humans have altered disturbance regimes that structure these forests such that floods are less common and wildfires occur more frequently than was historically the case. Impacts of these changes on primary consumers such as riparian cicadas are unknown. Because cicadas are consumed by a variety of animal species, disturbances that alter timing of their emergence or abundance could have consequences for species at higher trophic levels. We trapped emerging cicadas (Tibicen dealbatus) in burned and unburned riparian forest plots along the Middle Rio Grande in central New Mexico (USA) to determine effects of wildfire and vegetation structure on their density and phenology. We measured vegetation variables and soil temperature at cicada traps and related these variables to variation in emergence density and phenology. We also experimentally heated soil under emergence traps to examine the relationship between soil temperature and emergence phenology. Emergence density was similar in wildfire and unburned plots, though emergence date averaged earlier in wildfire plots and experimentally heated traps. We identified models containing cottonwood proximity (distance from the nearest cottonwood tree) and cottonwood canopy coverage as the most parsimonious explanations of emergence density at each trap. Model selection results were consistent with the literature and field observations that showed that cottonwood trees are an essential resource for T. dealbatus. Cottonwood canopy was also correlated with low soil temperatures, which are associated with later emergence dates. Failure of cottonwoods to reestablish following wildfire could result in cicadas emerging at lower densities and at earlier dates. For cicadas to emerge at densities and times that provide the greatest benefits to birds and other riparian-obligate secondary consumers, riparian forests should be protected from fire, and native vegetation in wildfire sites should be restored.     

Development of a framework for fire risk assessment using remote sensing and geographic information system technologies

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents proposals for their integration into synthetic risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal assessment of risk conditions. All variables were mapped at 1 km² spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and fire occurrence in the different study regions.