Human-caused wildfire risk rating for prevention planning in Spain

This paper identifies human factors associated with high forest fire risk in Spain and analyses the spatial distribution of fire occurrence in the country. The spatial units were 6,066 municipalities of the Spanish peninsular territory and Balearic Islands. The study covered a 13-year series of fire occurrence data. One hundred and eight variables were generated and input to a dedicated Geographic Information System (GIS) to model different factors related to fire ignition. After exploratory analysis, 29 were selected to build a predictive model of human fire ignition using logistic regression analysis. The binary model estimated the probability of high or low occurrence of forest fires, as defined by an ignition danger index that is currently used by the Spanish forest service (number of fires divided by forest area in each municipality). Thirteen explanatory variables were identified by the model. They were related to agricultural landscape fragmentation, agricultural abandonment and development processes. The prediction agreement found between the model binary outputs and the historical fire data was 85.3% for the model building dataset (60% of municipalities). A slightly lower predictive power (76.2%) was found for the validation data (the remaining 40%). The probabilistic output of the logistic was significantly related to the raw ignition index (Spearman correlation of 0.710) used by the Spanish Forest Service. Therefore, the model can be considered a good predictor of human-caused fire risk, aiding spatial decisions related to prevention planning in Spanish municipalities.     

Mapping probability of fire occurrence in San Jacinto Mountains,California, USA

An ecological data base for the San Jacinto Mountains, California, USA, was used to construct a probability model of wildland fire occurrence. The model incorporates both environmental and human factors, including vegetation, temperature, precipitation, human structures, and transportation. Spatial autocorrelation was examined for both fire activity and vegetation to determine the specification of neighborhood effects in the model. Parameters were estimated using stepwise logistic regressions. Among the explanatory variables, the variable that represents the neighborhood effects of spatial processes is shown to be of great importance in the distribution of wildland fires. An important implication of this result is that the management of wildland fires must take into consideration neighborhood effects in addition to environmental and human factors. The distribution of fire occurrence probability is more accurately mapped when the model incorporates the spatial term of neighborhood effects. The map of fire occurrence probability is useful for designing large-scale management strategies of wildfire prevention.     

Effects of anthropogenic fire history on savanna vegetation in northeastern Namibia

Anthropogenic fires in Africa are an ancient form of environmental disturbance, which probably have shaped the savanna vegetation more than any other human induced disturbance. Despite anthropogenic fires having played a significant role in savanna management by herders, previous ecological research did not incorporate the traditional knowledge of anthropogenic fire history. This paper integrates ecological data and anthropogenic fire history, as reconstructed by herders, to assess landscape and regional level vegetation change in northeastern Namibia. We investigated effects of fire frequency (i.e. <5, 5-10 and >10 years) to understand changes in vegetation cover, life form species richness and savanna conditions (defined as a ratio of shrub cover to herbaceous cover). Additionally, we analysed trends in the vegetation variables between different fire histories at the landscape and regional scales. Shrub cover was negatively correlated to herbaceous cover and herbaceous species richness. The findings showed that bush cover homogenisation at landscape and regional scales may suggest that the problem of bush encroachment was widespread. Frequent fires reduced shrub cover temporarily and promoted herbaceous cover. The effects on tree cover were less dramatic. The response to fire history was scale-independent for shrub, herbaceous and tree cover, but scale-dependent for the richness of grass and tree life forms. Fire history, and not grazing pressure, improved savanna conditions. The findings emphasise the need to assess effects of anthropogenic fires on vegetation change before introducing new fire management policies in savanna ecosystems of northeastern Namibia.     

Spatio-temporal analysis of fire events in India: implications for environmental conservation

Information on fires in different geographic regions of India is relatively scarce. This study quantifies spatial and temporal patterns in fire occurrences covering different states and districts in India. Two important scientific questions are answered in this study: (1) how are the fire events distributed across different geographical regions? (2) are there any specific districts where fire events clustered across space and time? To address these questions, Along Track Scanning Radiometer (ATSR) derived satellite fire counts from 1997–2006 were used and the datasets were analysed using spatial scan statistic. Spatial scan statistic provides a test statistic for most likely ‘hotspot’ spatial clusters, based on the likelihood ratio test and Monte Carlo simulation. Results from geographical analysis based on state boundaries suggested Maharastra state had the highest number of fires followed by Madhya Pradesh, Chattisgarh, Orissa, etc., during the 10-year period. Among the several districts, the spatial scan statistic identified the most likely cluster of fire events in Dausa, Karauli, Sawai Madhopur, Bharatpur and Alwar in addition to several other secondary clusters, with high statistical significance. These results are based on a large sample of cases, and they provide convincing evidence of spatial clustering of fire events in the Indian region. Results relating to hotspot areas of fire risk can guide policy makers towards the best management strategies for avoiding damages to forests, human life and personal property in the ‘hotspot’ districts.     

Remotely-Sensed Active Fire Data for Protected Area Management: Eight-Year Patterns in the Manas National Park, India

The Terai-Duar savanna and grasslands, which once extended along most of the Himalayan foothills, now only remain in a number of protected areas. Within these localities, grassland burning is a major issue, but data on frequency and distribution of fires are limited. Here, we analysed the incidence of active fires, which only occur during the dry season (Nov.–Mar.), within a significant area of Terai grasslands: the Manas National Park (MNP), India. We obtained locations of 781 fires during the 2000–2008 dry seasons, from the Fire Information for Resource Management System (FIRMS) that delivers global MODIS hotspot/fire locations using remote sensing and GIS technologies. Annual number of fires rose significantly from around 20 at the start of the study period to over 90 after 2002, with most (85%) detected between December and January. Over half of the fires occurred in tall grasslands, but fire density was highest in wetland and riverine vegetation, dry at the time. Most burning took place near rivers, roads and the park boundary, suggesting anthropogenic origins. A kernel density map of all recorded fires indicated three heavily burnt areas in the MNP, all within the tall grasslands. Our study demonstrates, despite some technical caveats linked to fire detection technology, which is improving, that remote fire data can be a practical tool in understanding fire concentration and burning temporal patterns in highly vulnerable habitats, useful in guiding management.     

Predicting Sustained Fire Spread in Tasmanian Native Grasslands

Fire is widely used in conservation management of native grasslands. Burning is often carried out under conditions that are marginal for sustained fire spread, and therefore it would be useful to be able to predict fire sustainability. There is currently no model allowing such prediction in temperate grasslands. This study aims to identify the environmental variables that determine whether fires will sustain in native grasslands in Tasmania, Australia, and develop a model for predicting fire sustainability in this vegetation. Fuel characteristics and weather conditions were recorded for 111 test fires. Logistic regression modeling identified dead fuel moisture content, fuel load, and percentage dead fuel as predictors of fire sustainability. Classification tree modeling identified dead fuel moisture and fuel load threshold values for sustaining fires. There was also evidence indicating a percentage dead fuel threshold. The logistic regression model and a model combining the results of the classification tree and the percentage dead fuel threshold accurately predicted the outcomes of a small set of experimental fires. These models are likely to have utility in predicting fire sustainability in Tasmanian grasslands and are also likely to be applicable to similar grasslands elsewhere.     

A heuristic expert system for forest fire guidance in Greece

Forests and forestlands are common inheritance for all Greeks and a piece of the national wealth that must be handed over to the next generations in the best possible condition. After 1974, Greece faces a severe forest fire problem and forest fire forecasting is the process that will enable the Greek ministry of Agriculture to reduce the destruction. This paper describes the basic design principles of an Expert System that performs forest fire forecasting (for the following fire season) and classification of the prefectures of Greece into forest fire risk zones. The Expert system handles uncertainty and uses heuristics in order to produce scenarios based on the presence or absence of various qualitative factors. The initial research focused on the construction of a mathematical model which attempted to describe the annual number of forest fires and burnt area in Greece based on historical data. However this has proven to be impossible using regression analysis and time series. A closer analysis of the fire data revealed that two qualitative factors dramatically affect the number of forest fires and the hectares of burnt areas annually. The first is political stability and national elections and the other is drought cycles. Heuristics were constructed that use political stability and drought cycles, to provide forest fire guidance. Fuzzy logic was applied to produce a fuzzy expected interval for each prefecture of Greece. A fuzzy expected interval is a narrow interval of values that best describes the situation in the country or a part of the country for a certain time period. A successful classification of the prefectures of Greece in forest fire risk zones was done by the system, by comparing the fuzzy expected intervals to each other. The system was tested for the years 1994 and 1995. The testing has clearly shown that the system can predict accurately, the number of forest fires for each prefecture for the following year. The average accuracy was as high as 85.25% for 1995 and 80.89% for 1994. This makes the Expert System a very important tool for forest fire prevention planning.     

Predicted effects of prescribed burning and harvesting on forest recovery and sustainability in southwest Georgia, USA

A model-based analysis of the effect of prescribed burning and forest thinning or clear-cutting on stand recovery and sustainability was conducted at Fort Benning, GA, in the southeastern USA. Two experiments were performed with the model. In the first experiment, forest recovery from degraded soils was predicted for 100 years with or without prescribed burning. In the second experiment simulations began with 100 years of predicted stand growth, then forest sustainability was predicted for an additional 100 years under different combinations of prescribed burning and forest harvesting. Three levels of fire intensity (low, medium, and high), that corresponded to 17%, 33%, and 50% consumption of the forest floor C stock by fire, were evaluated at 1-, 2-, and 3-year fire return intervals. Relative to the control (no fire), prescribed burning with a 2- or 3-year return interval caused only a small reduction in predicted steady state soil C stocks (< or =25%) and had no effect on steady state tree wood biomass, regardless of fire intensity. Annual high intensity burns did adversely impact forest recovery and sustainability (after harvesting) on less sandy soils, but not on more sandy soils that had greater N availability. Higher intensity and frequency of ground fires increased the chance that tree biomass would not return to pre-harvest levels. Soil N limitation was indicated as the cause of unsustainable forests when prescribed burns were too frequent or too intense to permit stand recovery.     

A Review of the Main Driving Factors of Forest Fire Ignition Over Europe

Knowledge of the causes of forest fires, and of the main driving factors of ignition, is an indispensable step towards effective fire prevention policies. This study analyses the factors driving forest fire ignition in the Mediterranean region including the most common human and environmental factors used for modelling in the European context. Fire ignition factors are compared to spatial and temporal variations of fire occurrence in the region, then are compared to results obtained in other areas of the world, with a special focus on North America (US and Canada) where a significant number of studies has been carried out on this topic. The causes of forest fires are varied and their distribution differs among countries, but may also differ spatially and temporally within the same country. In Europe, and especially in the Mediterranean basin, fires are mostly human-caused mainly due arson. The distance to transport networks and the distance to urban or recreation areas are among the most frequently used human factors in modelling exercises and the Wildland-Urban Interface is increasingly taken into account in the modelling of fire occurrence. Depending on the socio-economic context of the region concerned, factors such as the unemployment rate or variables linked to agricultural activity can explain the ignition of intentional and unintentional fires. Regarding environmental factors, those related to weather, fuel and topography are the most significant drivers of ignition of forest fires, especially in Mediterranean-type regions. For both human and lightning-caused fires, there is a geographical gradient of fire ignition, mainly due to variations in climate and fuel composition but also to population density for instance. The timing of fires depends on their causes. In populated areas, the timing of human-caused fires is closely linked to human activities and peaks in the afternoon whereas, in remote areas, the timing of lightning-caused fires is more linked to weather conditions and the season, with most such fires occurring in summer.     

Forest-user attitudes toward alternative fire management policies

The formulation and implementation of new fire policies in the national forests depend upon public acceptance. A national survey of organized groups of forest users indicates that, contrary to the concern of many forest managers, considerable support exists for flexible fire suppression policies. Forest users are also willing to accept the risk associated with the manager's use of prescribed fire. However, important intergroup differences do exist. Such variation is discussed in relation to a number of socioeconomic variables, general fire knowledge, specific knowledge about the effects of low-intensity fires, and risk preference levels.     

Wildland–Urban Interface Fires and Socioeconomic Conditions: A Case Study of a Northwestern Patagonia City

In many regions of the world, fires are primarily of anthropogenic origin. In northwestern Patagonia, the number of fires is not correlated with meteorological variables, but is concentrated in urban areas. This study was conducted in the wildland–urban interface (WUI) area of San Carlos de Bariloche (Patagonia, Argentina), within the Nahuel Huapi National Park. WUI fires are particularly problematic because, besides people and goods, they represent a danger to protected areas. We studied the relationship between fire records and socioeconomic indicators within the WUI of San Carlos de Bariloche. We conducted a Multiple Correspondence Factorial Analysis and an Ascendant Hierarchical Classification of the city neighborhoods. The results show that the neighborhoods in Bariloche can be divided into three classes: High Socioeconomic Fire Risk neighborhoods, including neighborhoods with the highest fire rates, where people have low instruction level, high levels of unsatisfied basic needs and high unemployment levels; Low Socioeconomic Fire Risk neighborhoods, that groups neighborhoods which present the opposite characterization, and Moderate Socioeconomic Fire Risk neighborhoods, which are more heterogeneous. Once neighborhoods were classified, a Socioeconomic Fire Risk map was generated, supplementing the existing WUI Fire Danger map. Our results emphasize the relevance of socioeconomic variables to fire policies.     

The role of stand history in assessing forest impacts

Air pollution, harvesting practices, and natural disturbances can affect the growth of trees and forest development. To make predictions about anthropogenic impacts on forests, we need to understand how these factors affect tree growth. In this study the effect of disturbance history on tree growth and stand structure was examined by using a computer model of forest development. The model was run under the climatic conditions of east Tennessee, USA, and the results compared to stand structure and tree growth data from a yellow poplar-white oak forest. Basal area growth and forest biomass were more accurately projected when rough approximations of the thinning and fire history typical of the measured plots were included in the simulation model. Stand history can influence tree growth rates and forest structure and should be included in any attempt to assess forest impacts.     

Impact of informal regulation of pollution on water quality in rivers in India

In this paper an attempt is made to assess the impact of informal regulation of water pollution on water quality in Indian rivers. For this purpose, an econometric analysis of determinants of water quality in Indian rivers is carried out using water quality (water class) data for 106 monitoring points on 10 important rivers for five years, 1995-1999. To explain variations in water quality, an Ordered Probit model is estimated, in which poll percentage in parliamentary elections, a proxy for the intensity of informal regulation, is taken as one of the main explanatory variables. Rainfall, industrialization, irrigation intensity and fertilizer use are some of the other explanatory variables used in the model to control for the influence of these factors. As expected, river water quality is found to be positively related with rainfall, and negatively related with industrialization, irrigation intensity and fertilizer use. A significant positive relationship is found between poll percentage and water quality, and also between the rate of increase in literacy level in a district and the water quality in rivers flowing through the district. These results point to a significant favorable effect of informal regulation of pollution on water quality in rivers in India.     

A Statistical Sediment Yield Prediction Model Incorporating the Effect of Fires and Subsequent Storm Events1

Abstract: Alluvial fans are continuously being developed for residential, industrial, commercial, and agricultural uses in southern California. Development and alteration of alluvial fans need to consider the possibility of mud and debris flows from upstream mountain watersheds affected by fires. Accurate prediction of sediment yield (or hyper‐concentrated sediment yield) is essential for the design, operation, and maintenance of debris basins to safeguard properly the general populace. This paper presents a model for the prediction of sediment yields that result from a combination of fire and subsequent storm events. The watersheds used in this analysis are located in the foothills of the San Gabriel Mountains in southern California. A multiple regression analysis is first utilized to establish a fundamental statistical relationship for sediment yield as a function of relief ratio, drainage area, maximum 1‐h rainfall intensity and fire factor using 45 years of data (1938‐1983). In addition, a method for multi‐sequence sediment yield prediction under fire conditions was developed and calibrated using 17 years of sediment yield, fire, and precipitation data for the period 1984‐2000. After calibration, this model was verified by applying it to provide a prediction of the sediment yields for the 2001‐2002 fire events in southern California. The findings indicate a strong correlation between the estimated and measured sediment yields. The proposed method for sequence sediment yield prediction following fire events can be a useful tool to schedule cleanout operations for debris basins and to develop an emergency response strategy for the southern California region where plentiful sediment supplies exist and frequent fires occur.     

Analysis of the prescribed burning practice in the pine forest of northwestern Portugal

The ignition of low-intensity fires in the dormant season in the pine stands of north-western Portugal seeks to reduce the existing fuel hazard without compromising site quality. The purpose of this study is to characterise this practice and assess its effectiveness, based on information resulting from the normal monitoring process at the management level, and using operational guidelines, fire behaviour models and a newly developed method to classify prescribed fire severity. Although the region's humid climate strongly constrains the activity of prescribed fire, 87% of the fires analysed were undertaken under acceptable meteorological and fuel moisture conditions. In fact, most operations achieved satisfactory results. On average, prescribed fire reduces by 96% the potential intensity of a wildfire occurring under extreme weather conditions, but 36% of the treated sites would still require heavy fire fighting resources to suppress such fire, and 17% would still carry it in the tree canopy. Only 10% of the prescribed burns have an excessive impact on trees or the forest floor, while 89% (normal fire weather) or 59% (extreme fire weather) comply with both ecological integrity maintenance and wildfire protection needs. Improved planning and monitoring procedures are recommended in order to overcome the current deficiencies.     

CLIMATOLOGICAL WATERSHED CALIBRATION1

ABSTRACT: This study tests the hypothesis that climatic data can be used to develop a watershed model so that stream flow changes following forest harvest can be determined. Measured independent variables were precipitation, daily maximum and minimum temperature, and concurrent relative humidity. Computed variables were humidity deficit, saturated vapor pressure, and ambient vapor pressure. These climatic variables were combined to compute a monthly evaporation index. Finally, the evaporation index and monthly precipitation were regressed with measured monthly stream flow and the monthly estimates of stream flow were combined for the hydrologic year. A regression of predicted versus measured annual stream flow had a standard error of 1.5 inches (within 6.1 percent of the measured value). When 10, 15, and 20 years of data were used to develop the regression equations, predicted minus measured stream flow for the last 7 years of record (1972–1978) were within 16.8, 11.5, and 9.7 percent of the measured mean, respectively. Although single watershed calibration can be used in special conditions, the paired watershed approach is expected to remain the preferred method for determining the effects of forest management on the water resource.     

Multi-criteria forest road network planning in fire-prone environment: a case study in Serbia

This paper presents a multi-criteria selection of the best forest road network with the aim of achieving effective preventive and timely repressive protection against forest fires. The research was carried out in national park (NP) “Tara” at a site which is under constant threat of fire. Four variants of the forest road network were designed and analyzed against seven criteria, and the best variant was selected using entropy weight coefficients (EWC) method. The importance of these seven criteria was studied by the management of the NP “Tara.” The greatest importance was given to the criteria relating to the cost of construction and maintenance of permanent and temporary roads (47% of the total value of all criteria). The EWC method did not select the variant based on the lowest costs of construction and maintenance as the best option, but the variant whose location was the most suitable for effective defense against forest fires.     

Effects of watershed configuration and composition on downstream lake water quality

We evaluated the relationships between landscape characteristics and lake water quality in receiving waters by regressing four water quality responses on landscape variables that were measured for whole watersheds and three different buffer distances (30, 60, and 120 m). Classical percolation theory was used to conceptualize nutrient pathways and to explain nonlinear responses. The response variables were total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chl-a), and Secchi transparency (SD). Landscape data were obtained from satellite image-derived maps of 130 watersheds in Iowa using geographic information systems software. We developed regression models with a stepwise protocol selecting the optimal number of significant explanatory variables. Configuration variables such as contagion, the cohesion of cropland and urban land, and the aggregation index of forest were very important and more important than variables assessing landscape composition (e.g., percentage farmland). Whole watershed models predicted between 15 and 67% of the variability in TN, TP, Chl-a, and SD. Proximity-explicit data offered only slightly improved statistical power over land cover data derived from the entire watershed for variables TN, Chl-a. and SD, but not for TP.     

Determining Relative Contributions of Vegetation and Topography to Burn Severity from LANDSAT Imagery

Fire is a dominant process in boreal forest landscapes and creates a spatial patch mosaic with different burn severities and age classes. Quantifying effects of vegetation and topography on burn severity provides a scientific basis on which forest fire management plans are developed to reduce catastrophic fires. However, the relative contribution of vegetation and topography to burn severity is highly debated especially under extreme weather conditions. In this study, we hypothesized that relationships of vegetation and topography to burn severity vary with fire size. We examined this hypothesis in a boreal forest landscape of northeastern China by computing the burn severity of 24 fire patches as the difference between the pre- and post-fire Normalized Difference Vegetation Index obtained from two Landsat TM images. The vegetation and topography to burn severity relationships were evaluated at three fire-size levels of small (<100 ha, n = 12), moderate (100–1,000 ha, n = 9), and large (>1,000 ha, n = 3). Our results showed that vegetation and topography to burn severity relationships were fire-size-dependent. The burn severity of small fires was primary controlled by vegetation conditions (e.g., understory cover), and the burn severity of large fires was strongly influenced by topographic conditions (e.g., elevation). For moderate fires, the relationships were complex and indistinguishable. Our results also indicated that the pattern trends of relative importance for both vegetation and topography factors were not dependent on fire size. Our study can help managers to design fire management plans according to vegetation characteristics that are found important in controlling burn severity and prioritize management locations based on the relative importance of vegetation and topography.