Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale

Forest fire is regarded as one of the most significant factors leading to land degradation. While evaluating fire hazard or producing fire risk zone maps, quantitative analyses using historic fire data is often required, and during all these modeling and multi-criteria analysis processes, the fire event itself is taken as the dependent variable. However, there are two main problematic issues in analyzing historic fire data. The first difficulty arises from the fact that it is in point format, whereas a continuous surface is frequently needed for statistically analyzing the relationship of fire events with other factors, such as anthropogenic, topographic and climatic conditions. Another, and probably the most bothersome challenge is to overcome inaccuracy inherent in historic fire data in point format, since the exact coordinates of ignition points are mostly unknown. In this study, kernel density mapping, a widely used method for converting discrete point data into a continuous raster surface, was used to map the historic fire data in Mumcular Forest Sub-district in Mu?la, Turkey. The historic fire data was transferred onto the digital forest stand map of the study area, where the exact locations of ignition points are unknown; however, the exact number of ignition points in each compartment of the forest stand map is known. Different random distributions of ignition points were produced, and for each random distribution, kernel density maps were produced by applying two distinct kernel functions with several smoothing parameter options. The obtained maps were compared through correlation analysis in order to illustrate the effect of randomness, choice of kernel function and smoothing parameter. The proposed method gives a range of values rather than a single bandwidth value; however, it provides a more reliable way than comparing the maps with different bandwidths subjectively by eye.     

Assessing the risk of ignition in the Russian far east within a modeling framework of fire threat.

The forests of high biological importance in the Russian Far East (RFE) have been experiencing increasing pressure from growing demands for natural resources under the changing economy of post-Soviet Russia. This pressure is further amplified by the rising threat of large and catastrophic fire occurrence, which threatens both the resources and the economic potential of the region. In this paper we introduce a conceptual Fire Threat Model (FTM) and use it to provide quantitative assessment of the risk of ignition in the Russian Far East. The remotely sensed data driven FTM is aimed at evaluating potential wildland fire occurrence and its impact and recovery potential for a given resource. This model is intended for use by resource managers to assist in assessing current levels of fire threat to a given resource, projecting the changes in fire threat under changing climate and land use, and evaluating the efficiency of various management approaches aimed at minimizing the fire impact. Risk of ignition (one of the major uncertainties within fire threat modeling) was analyzed using the MODIS active fire product. The risk of ignition in the RFE is shown to be highly variable in spatial and temporal domains. However, the number of ignition points is not directly proportional to the amount of fire occurrence in the area. Fire ignitions in the RFE are strongly linked to anthropogenic activity (transportation routes, settlements, and land use). An increase in the number of fire ignitions during summer months could be attributed to (1) disruption of the summer monsoons and subsequent changes in fire weather and (2) an increase in natural sources of fire ignitions.     

Local spatial modeling of white-tailed deer distribution

Complex spatial heterogeneity of ecological systems is difficult to capture and interpret using global models alone. For this reason, recent attention has been paid to local spatial modeling techniques. We used one local modeling approach, geographically weighted regression (GWR), to investigate the effects of local spatial heterogeneity on multivariate relationships of white-tailed deer distribution using land cover patch metrics and climate factors. The results of these analyses quantify differences in the contributions of model parameters to estimates of deer density over space. A GWR model with local kernel bandwidth was compared to a GWR model with global kernel bandwidth and an ordinary least-squares regression (OLS) model with the same parameters to evaluate their relative abilities in modeling deer distributions. The results indicated that the GWR models predicted deer density better than the traditional ordinary least-squares model and also provided useful information regarding local environmental processes affecting deer distribution. GWR model comparisons showed that the local kernel bandwidth GWR model was more realistic than the global kernel bandwidth GWR model, as the latter exaggerated local spatial variation. The parameter estimates and model statistics (e.g., model R²) of the GWR models were mapped using geographic information systems (GIS) to illustrate local spatial variation in the regression relationship and to identify causes of large-scale model misspecifications and low estimation efficiencies.     

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.

Hierarchical space-time models for fire ignition and percentage of land burned by wildfires

Policy responses for local and global fire management as well as international green-gas inventories depend heavily on the proper understanding of the annual fire extend as well as its spatial variation across any given study area. Proper statistical models are important tools in quantifying these fire risks. We propose Bayesian methods to model jointly the probability of ignition and fire sizes in Australia and New Zeland. The data set on which we base our model and results consists of annual observations of several meteorological and topographical explanatory variables, together with the percentage of land burned over a grid with resolution of 1° across Austalia and New Zealand. Our model and conclusions bring improvements on the results reported by Russell-Smith et al. in Int J Wildland Fire, 16:361–377 (2007) based on a similar data set.     

Evaluating the ecological benefits of wildfire by integrating fire and ecosystem simulation models

Fire managers are now realizing that wildfires can be beneficial because they can reduce hazardous fuels and restore fire-dominated ecosystems. A software tool that assesses potential beneficial and detrimental ecological effects from wildfire would be helpful to fire management. This paper presents a simulation platform called FLEAT (Fire and Landscape Ecology Assessment Tool) that integrates several existing landscape- and stand-level simulation models to compute an ecologically based measure that describes if a wildfire is moving the burning landscape towards or away from the historical range and variation of vegetation composition. FLEAT uses a fire effects model to simulate fire severity, which is then used to predict vegetation development for 1, 10, and 100 years into the future using a landscape simulation model. The landscape is then simulated for 5000 years using parameters derived from historical data to create an historical time series that is compared to the predicted landscape composition at year 1, 10, and 100 to compute a metric that describes their similarity to the simulated historical conditions. This tool is designed to be used in operational wildfire management using the LANDFIRE spatial database so that fire managers can decide how aggressively to suppress wildfires. Validation of fire severity predictions using field data from six wildfires revealed that while accuracy is moderate (30-60%), it is mostly dictated by the quality of GIS layers input to FLEAT. Predicted 1-year landscape compositions were only 8% accurate but this was because the LANDFIRE mapped pre-fire composition accuracy was low (21%). This platform can be integrated into current readily available software products to produce an operational tool for balancing benefits of wildfire with potential dangers.     

The fire frequency analysis branch of the pyrostatistics tree: sampling decisions and censoring in fire interval data

Statistical characterization of past fire regimes is important for both the ecology and management of fire-prone ecosystems. Survival analysis—or fire frequency analysis as it is often called in the fire literature—has increasingly been used over the last few decades to examine fire interval distributions. These distributions can be generated from a variety of sources (e.g., tree rings and stand age patterns), and analysis typically involves fitting the Weibull model. Given the widespread use of fire frequency analysis and the increasing availability of mapped fire history data, our goal has been to review and to examine some of the issues faced in applying these methods in a spatially explicit context. In particular, through a case study on the massive Cedar Fire in 2003 in southern California, we examine sensitivities of parameter estimates to the spatial resolution of sampling, point- and area-based methods for assigning sample values, current age surfaces versus historical intervals in generating distributions, and the inclusion of censored (i.e., incomplete) observations. Weibull parameter estimates were found to be roughly consistent with previous fire frequency analyses for shrublands (i.e., median age at burning of ~30–50 years and relatively low age dependency). Results indicate, however, that the inclusion or omission of censored observations can have a substantial effect on parameter estimates, far more than other decisions about specifics of sampling.

Simulating a natural fire regime on an Atlantic coast barrier island complex in Florida, USA

The HFire fire regime model was used to simulate the natural fire regime (prior to European settlement) on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. Model simulations were run for 500 years and the model was parameterized using information generated from previously published empirical studies on these properties (e.g., lightning fire ignition frequencies and ignition seasonality). A mosaic pattern of frequent small fires dominated this fire regime with rare but extremely large fires occurring during dry La Niña periods. This simulated fire size distribution very closely matched the previously published fire size distribution for lightning ignitions on these properties. A sensitivity analysis was performed to establish which parameters were most influential and the range of variation surrounding empirically parameterized model output. Dead fuel moisture and wind speed had the largest influence on model outcome. A wide range of variance was observed surrounding the composite simulation with the least being 6% in total burn frequency and the greatest being 49% in total area burned. Because simulation modeling is the best option for fire regime reconstruction in many rapidly growing shrub dominated systems, these results will be of interest to scientists and fire managers for delineating the natural fire regime on these properties, the southeastern United States and other fire adapted shrub systems worldwide.     

Non-linear ecological processes, fires, environmental heterogeneity and shrub invasion in northwestern Patagonia

The emergent behaviors of nature are not only the sum of interactions among ecosystem parts but also depend on the organization of these interactions. Fire, climate and vegetation patterns produce non-linear fire propagation across the landscape. Environmental heterogeneity, like outcrop presence and hare density, increases landscape patchiness and makes possible the occupation of fire refuges by plants, like Fabiana imbricata shrubs. We monitored shrub recruitment and cover during nine postfire years in northwestern Patagonia grasslands and we studied the long-term population dynamics under different environmental conditions through a matrix model, exploring different fire frequencies and spring precipitation regimes. Both, the field monitoring and the model seem to confirm the relationships between shrub invasion and fire. The climate change forecast predicts an increase in the frequency of El Niño Southern Oscillation phenomena that could causes more coupled fires—rainy springs in northwestern Patagonia, and consequently, more recruitment windows for shrubs, like F. imbricata. The matrix model also indicates that this scenario would be the most favourable for shrub invasion. Our results contribute to the knowledge of the ecosystem properties and processes, providing useful information to improve the grasslands sustainable use.     

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.     

Understanding Interaction Effects of Climate Change and Fire Management on Bird Distributions through Combined Process and Habitat Models

Abstract: Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process‐based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail (Callipepla squamata), Loggerhead Shrike (Lanius ludovicianus), and Rock Wren (Salpinctes obsoletus), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo (Fouquieria splendens) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf‐area index values were lower in shrubland. This high probability of occurrence likely is related to the species’ use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.     

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 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.     

Effects of time since fire on birds: how informative are generalized fire response curves for conservation management?

Fire is both a widespread natural disturbance that affects the distribution of species and a tool that can be used to manage habitats for species. Knowledge of temporal changes in the occurrence of species after fire is essential for conservation management in fire-prone environments. Two key issues are: whether postfire responses of species are idiosyncratic or if multiple species show a limited number of similar responses; and whether such responses to time since fire can predict the occurrence of species across broad spatial scales. We examined the response of bird species to time since fire in semiarid shrubland in southeastern Australia using data from surveys at 499 sites representing a 100-year chronosequence. We used nonlinear regression to model the probability of occurrence of 30 species with time since fire in two vegetation types, and compared species' responses with generalized response shapes from the literature. The occurrence of 16 species was significantly influenced by time since fire: they displayed six main responses consistent with generalized response shapes. Of these 16 species, 15 occurred more frequently in mid- or later-successional vegetation (> 20 years since fire), and only one species occurred more often in early succession (< 5 years since fire). The models had reasonable predictive ability for eight species, some predictive ability for seven species, and were little better than random for one species. Bird species displayed a limited range of responses to time since fire; thus a small set of fire ages should allow the provision of habitat for most species. Postfire successional changes extend for decades and management of the age class distribution of vegetation will need to reflect this timescale. Response curves revealed important seral stages for species and highlighted the importance of mid- to late-successional vegetation (> 20 years). Although time since fire clearly influences the distribution of numerous bird species, predictive models of the spatial distribution of species in fire-prone landscapes need to incorporate other factors in addition to time since fire.     

Modeling resource selection using polytomous logistic regression and kernel density estimates

Wildlife resource selection studies typically compare used to available resources; selection or avoidance occurs when use is disproportionately greater or less than availability. Comparing used to available resources is problematic because results are often greatly influenced by what is considered available to the animal. Moreover, placing relocation points within resource units is often difficult due to radiotelemetry and mapping errors. Given these problems, we suggest that an animal’s resource use be summarized at the scale of the home range (i.e., the spatial distribution of all point locations of an animal) rather than by individual points that are considered used or available. To account for differences in use-intensity throughout an animal’s home range, we model resource selection using kernel density estimates and polytomous logistic regression. We present a case study of elk (Cervus elaphus) resource selection in South Dakota to illustrate the procedure. There are several advantages of our proposed approach. First, resource availability goes undefined by the investigator, which is a difficult and often arbitrary decision. Instead, the technique compares the intensity of animal use throughout the home range. This technique also avoids problems with classifying locations rigidly as used or unused. Second, location coordinates do not need to be placed within mapped resource units, which is problematic given mapping and telemetry error. Finally, resource use is considered at an appropriate scale for management because most wildlife resource decisions are made at the level of the patch. Despite the advantages of this use-intensity procedure, future research should address spatial autocorrelation and develop spatial models for ordered categorical variables.     

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.     

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.     

Integrating models to predict regional haze from wildland fire

Visibility impairment from regional haze is a significant problem throughout the continental United States. A substantial portion of regional haze is produced by smoke from prescribed and wildland fires. Here we describe the integration of four simulation models, an array of GIS raster layers, and a set of algorithms for fire-danger calculations into a modeling framework for simulating regional-scale smoke dispersion. We focus on a representative fire season (2003) in the northwestern USA, on a 12 km domain, and track the simulated dispersion and concentration of PM_2.5 over the course of the season. Simulated visibility reductions over national parks and wilderness areas are within the ranges of measured values at selected monitoring sites, although the magnitudes of peak events are underestimated because these include inputs other than fire. By linking the spatial and temporal patterns of haze-producing emissions to climatic variability, particularly synoptic weather patterns, and the stochastic nature of fire occurrence across the region, we can provide a robust method for estimating the quantity and distribution of fire-caused regional haze under climate-warming scenarios.     

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.