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.

/pdf/a-review-of-the-main-driving-factors-of-forest-fire-ignition-34pb8c86c3.pdf

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

Human-caused wildfire risk rating for prevention planning in Spain.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly ...

/pdf/a-review-of-machine-learning-applications-in-wildfire-3yit0f75yl.pdf

A review of machine learning applications in wildfire science and management

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Portugal has the highest density of wildfire ignitions among southern European countries. The ability to predict the spatial patterns of ignitions constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. In this study, we analyzed 127 490 ignitions that occurred in Portugal during a 5-year period. We used logistic regression models to predict the likelihood of ignition occurrence, using a set of potentially explanatory variables, and produced an ignition risk map for the Portuguese mainland. Results show that population density, human accessibility, land cover and elevation are important determinants of spatial distribution of fire ignitions. In this paper, we demonstrate that it is possible to predict the spatial patterns of ignitions at the national level with good accuracy and using a small number of easily obtainable variables, which can be useful in decision-making for wildfire management.

/pdf/modeling-and-mapping-wildfire-ignition-risk-in-portugal-2z5yzzja26.pdf

Modeling and mapping wildfire ignition risk in Portugal

Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network’s weights after the training procedure (i.e., the percentage of influence—PI, the weight product—WP, and the partial derivatives—PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables’ importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence.

Identifying wildland fire ignition factors through sensitivity analysis of a neural network

Prevention is one of the most important stages in wildfire and other natural hazard management regimes. Fire danger rating systems have been adopted by many developed countries dealing with wildfire prevention and pre-suppression planning, so that civil protection agencies are able to define areas with high probabilities of fire ignition and resort to necessary actions. This present paper presents a fire ignition risk scheme, developed in the study area of Lesvos Island, Greece, that can be an integral component of a quantitative Fire Danger Rating System. The proposed methodology estimates the geo-spatial fire risk regardless of fire causes or expected burned area, and it has the ability of forecasting based on meteorological data. The main output of the proposed scheme is the Fire Ignition Index, which is based on three other indices: Fire Weather Index, Fire Hazard Index, and Fire Risk Index. These indices are not just a relative probability for fire occurrence, but a rather quantitative assessment of fire danger in a systematic way. Remote sensing data from the high-resolution QuickBird and the Landsat ETM satellite sensors were utilised in order to provide part of the input parameters to the scheme, while Remote Automatic Weather Stations and the SKIRON/Eta weather forecasting system provided real-time and forecasted meteorological data, respectively. Geographic Information Systems were used for management and spatial analyses of the input parameters. The relationship between wildfire occurrence and the input parameters was investigated by neural networks whose training was based on historical data.

Integrating new methods and tools in fire danger rating

Topographic Mapping: Covering the Wider Field of Geospatial Information Science & Technology (GIS&T)

An analytical photograinlnetric system using non-metric cameras and data reduction software has been developed. Important factors influencing the development, such as camera calibration and project design, are described. The system has been experimentally tested and found to yield accurate results. These results suggest that this system is suitable for application in construction surveys, bioengineering, quality control, surface model-

An analytical system for close-range photogrammetry

A photogrammetric monitoring method of structural deformation has been developed during the course of this project. The Gabion Wall which is part of the I-90 project has been used as the site for practical tests of the theoretical development. The monitoring consists of photographing the structure from three camera stations with KA-2 f=24" camera. The camera has been modified to a plate camera to provide the maximum accuracy. The methodology consists of the geodetic determination of the camera location and the orientation and photogrammetric determination of targets (natural and artificial) on the structure. During the course of this project more than 100 target ocations were determined by three dimensional coordinates. The maximum error was found to be plus 3/4 inch; the average, 1/2 inch. This represents a relative accuracy of from 1/58,000 to 1/120,000 of the photographic distance. Using the actual construction site for research has permitted immediate implementation. The instrumentation as well as the methodology along with the computer program has been transmitted to the Washington State Higwhay Department & their Photogrammetric Branch has been assisted in the implementation. /FHWA/

Photogrammetric monitoring of a gabion wall

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