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An assessment of fire-damaged forest using spatial analysis techniques
Abstract
Although forest fires are commonly accepted as a natural part of the ecosystem, frequent forest fires present great challenges to fire managers. In this research, ‘Fire Area Simulator’ has been used to simulate and study forest fire behaviour. Once the predicted perimeters were generated and compared to those shown in postmortem aerial infrared images, partial agreement was observed for the direction and extent of the forest fire. Using spatial analysis functions, the characteristics of the damaged areas were also observed. It is shown that constructing fuel models and collecting weather data with regard to local and regional forest fires can improve the simulation of forest fires. The spatial modelling of landscapes in aerial infrared images can be used for the evaluation of the extent of damage due to forest fires.
... Annual 30 m (Mhawej et al., 2016;Pourghasemi, 2016) 5 Population density Because of human activities, residential growth can increase the risk of wildfires. Every 5 years 1 km (Canu et al., 2017;Lee and Lim, 2010) (Fovell and Gallagher, 2018;Jahdi et al., 2014;Sakellariou et al., 2017) 8 Precipitation It is known that precipitation has an inverse relationship with wildfires and influences their speed. Daily 0.5 • (Razali et al., 2010;Tanskanen et al., 2005b;Vasilakos et al., 2009) occurrence, and resampling using the nearest neighbor approach was conducted to modify the wind data to a fixed patch size of 400 × 350. ...
... Human activity in residential communities elevates the risk of wildfire occurrences (Lee and Lim, 2010). The Gridded Population of World version 4 (GPWv4) dataset, which provides population density information, was made available by the Center for International Earth Science Information Network (CIESIN). ...
... Table 6 reveals that an increase in batch size leads to reduced accuracy. However, research conducted by Lee and Lim [2010] demonstrated a direct relationship between batch size and accuracy regarding predicting burned areas. ...
Wildfires represent a significant natural disaster with the potential to inflict widespread damage on both ecosystems and property. In recent years, there has been a growing interest in leveraging deep learning (DL) techniques for predicting the spread of wildfires (WS). However, existing studies have predominantly employed combined features with uniform weighting, overlooking the varying temporal resolutions they can offer (hourly, daily, and constant).As such, this study proposes a hybrid multi-temporal convolutional neural network (CNN) model called FirePred to fill this knowledge gap. In particular, 177 wildfire events were utilized along with related environmental variables between the years 2002 and 2018 in British Columbia, Canada. In pursuit of optimizing the model's performance, an exhaustive exploration of parameter configurations and settings was conducted. This involved assessing diverse combinations of loss functions, padding sizes, batch sizes, and thresholds. Notably, this rigorous analysis yielded an exceptional F1-score of 94% utilizing the most effective parameter set. In addition, to examine the versatility of our proposed model, we conducted an assessment using a dataset encompassing 10 instances of wildfires that transpired in Alaska between 2016 and 2019, as well as a wildfire occurrence in Nova Scotia during 2023. The findings revealed that the performance of the model can be influenced by regional parameters. Finally, the implementation of an uncertainty protocol discovered that the edges of the wildfire contribute the most to the uncertainty.
... However, the use of FARSITE simulator on areas different from those ones where the model was originally developed requires a local calibration and validation (Arca et al., 2007) using observed real wildfire data, and corresponds to the primary step to then apply the simulator at larger scales (Ager et al., 2007(Ager et al., , 2010Stratton, 2006;Salis et al., 2013Salis et al., , 2014b. The reliability of FARSITE 15 as a tool for improving wildfire analysis and landscape management options has been reported by several papers in southern Europe (Molina and Castellnou, 2002;Arca et al., 2007;Duguy et al., 2007;Mallinis et al., 2008;Glasa and Halada, 2011), as well as in New Zealand, Australia (Opperman et al., 2006) and southeast Asia (Lee et al., 2010). Nevertheless, no studies have been carried out with FARSITE in Iran and the 20 surrounding countries of southwest Asia. ...
... Nevertheless, no studies have been carried out with FARSITE in Iran and the 20 surrounding countries of southwest Asia. FARSITE requires a set of geospatial data concerning topography, surface fuel models and canopy characteristics derived from GIS or remote sensing, as well as the physical parameters of the fuel bed, fuel moisture content, and weather data: the outputs of fire spread models strongly depend on the quality of the above mentioned 25 input data, especially as far as weather data and fuel models are concerned (Arca et al., 2007). Although data availability increased during the recent years, fuel maps still result difficult to be generated and updated in many regions of the world, due to the absence of specific geospatial fuel model cartography or the lack of employable information on mapped vegetation attributes (Pettinari et al., 2013). ...
... In this area, the largest fires (> 100 ha) accounted for about 15 % of the total number, and were responsible of almost 75 % of the total area burned (Fig. 4). 25 FARSITE simulations were run to simulate spread and behavior of four wildfires that affected the study areas during the 2010 and 2011 fire seasons: Toshi and Malekroud 5; Table 1). The fire started at 04:00 p.m. and lasted approximately 25 h. ...
Wildfire simulators based on empirical or physical models need to be
locally calibrated and validated when used under conditions that
differ from those where the simulators were originally
developed. This study aims to calibrate FARSITE fire spread model
considering a set of recent wildfires occurred in Northern Iran
forests. Site specific fuel models in the study areas were selected
by sampling the main natural vegetation type complexes and assigning
standard fuel models. Overall, simulated fires presented reliable
outputs that accurately replicated the observed fire perimeters and
behavior. Standard fuel models of Scott and Burgan (2005) afforded
better accuracy in the simulated fire perimeters than the standard
fuel models of Anderson (1982). The best match between observed and
modeled burned areas was observed on herbaceous type fuel
models. Fire modeling showed a high potential for estimating spatial
variability in fire spread and behavior in the study areas. This
work represents a first step in the application of fire spread
modeling on Northern Iran for wildfire risk monitoring and
management.
... The wildfires affect vegetation, not only at the level of the individual bush or tree but also at the levels of the forest ecosystem and the landscape [8]. Though wildfires are commonly recognized as a natural part of a forest ecosystem, the increasing frequency of events, the increasing areas damaged by the fire, and the severity of wildfires present considerable challenges in forestry areas [9]. Various factors like wind, topography, and droughts have great impacts on fire occurrence and spread, but, in many cases, fires are caused by humans [10]. ...
... Both natural and anthropogenic wildfire conditioning factors make it difficult for environmental organizations to predict wildfires, a difficulty which results in complications during combustion controlling responses. These responses are essential for effectively combating wildfires and reducing their harmful consequences [9]. In addition to environmental damages, wildfires have widespread economic and social impacts on the local people in our study area in northern Iran. ...
Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.
... The use of FAR-SITE simulator on areas different from those ones where the model was originally developed requires a local calibration and validation (Arca et al., 2007) using observed wildfire data and is the primary step to applying the simulator at larger scales (Ager et al., 2007;Stratton, 2006;Salis et al., 2013). The reliability of FARSITE as a tool for improving wildfire analysis and landscape management options has been reported by several papers in southern Europe (Molina and Castellnou, 2002;Arca et al., 2007;Duguy et al., 2007;Mallinis et al., 2008;Glasa and Halada, 2011), New Zealand, Australia (Opperman et al., 2006) and southeast Asia (Lee et al., 2010). Nevertheless, no studies have been carried out with FARSITE in Iran and the surrounding countries of southwest Asia. ...
... The wildfire spread depends on complex interactions among terrain, fuel types, weather conditions, fire suppression and the heat released by the fire environment (Viegas et al., 1998;Forthofer and Butler, 2007;Fernandes, 2009;Lee et al., 2010;Sharples et al., 2012;Cardil et al., 2013). The use of fire spread models can help in the understanding of potential fire behavior, improve logistics and decision-making and thereby improve awareness and safety of firefighters. ...
Wildfire simulators based on empirical or physical models need to be locally
calibrated and validated when used under conditions that differ from those
where the simulators were originally developed. This study aims to calibrate
the FARSITE fire spread model considering a set of recent wildfires that occurred in
northern Iranian forests. Site-specific fuel models in the study areas were
selected by sampling the main natural vegetation type complexes and
assigning standard fuel models. Overall, simulated fires presented reliable
outputs that accurately replicated the observed fire perimeters and
behavior. Standard fuel models of Scott and Burgan (2005) afforded better
accuracy in the simulated fire perimeters than the standard fuel models of
Anderson (1982). The best match between observed and modeled burned areas
was observed on herbaceous fuel models. Fire modeling showed a high
potential for estimating spatial variability in fire spread and behavior in
the study areas. This work represents a first step in the application of
fire spread modeling in northern Iran for wildfire risk monitoring and
management.
... Forest health conditions are a determinant of ecological indicators in the surrounding environment [2]. Forest fires have become the main factor observed in recent times as a primary variable to forest disturbance because their frequency is increasing [3].Data from local disaster management agency BPDP shows the occurrences of forest fire events from 2014 to 2018 have increased (Table 1)[4]. This available data was then used to investigate the use of random forest performance in classifying forest fire severity classes. ...
The increasing incidence of forest fires in Raden Soerjo’s Grand Park over the last 5 years has become a concern for conducting research on fire predictions. The availability of free remote sensing data makes it easier to analyze forest fires using Geographic Information Systems (GIS). and remote sensing. Varying publicly available spatial datasets were used to classify forest fires affected areas with an approach of Machine Learning (ML) using Random Forest (RF). Nine (9) variables suspected as potential cause of forest fires (NDVI. ET. LST. Aspect. Slope. Altitude. Distance to Road (ED_Road). Distance to Built-up area (ED_BUA). and Distance to river (ED_River) were used as determinants. About 80% of the data were used as Trained data and 20% as a validation. The results of the model produced an accuracy of 0.96. The model result was checked for its sensitivity with the Area Under the Curve (AUC) AUC results with a value of 0.89. Findings show that Random Forest could be applied to map forest fire severity classes with a good result.
... DSSs are valuable tools for prevention and management of forest fires and are globally adopted at growing rates. The basic models-subsystems which comprise the structural elements for managing forest fires, and most DSSs use, are the following (Bonazountas et al. 2007;Dimopoulou and Giannikos 2004;European Commission 2012a;Giovando et al. 2013;Glasa 2009;Gumusay and Sahin 2009;Kalabokidis et al. 2012;Keramitsoglou et al. 2004;Lee et al. 2002Lee et al. , 2010Noonan-Wright et al. 2011;Wybo 1998): ...
Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems (DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.
... Perminov (2010) developed a mathematical model for describing heat and mass transfer processes at crown forest fire initiation, taking into account their mutual influence. Lee et al. (2010) simulated and visualized forest fire to estimate forest damage. They reported that fuel models and climate data in regional fires were useful for simulation. ...
The purpose of this study was to assess the forest fire behaviour and investigate the impact of different parameters on the spread of surface fire in the Hyrcanian forest of Iran. Surface fire was simulated using mathematical models in Microsoft Visual Basic 6.0 environment during a 30-minute time period. Several parameters that contributed to the speed of surface fire such as slope, wind velocity and litter thickness in the forest floor and various types of forest litter associated with hornbeam (Carpinus betulus L.), Persian ironwood (Parrotia persica C.A.M), beech (Fagus orientalis L.) and maple (Acer velutinum L.) were investigated. The results indicated that the maximum burned area was associated with beech litter. Forest surface fire demonstrated similar behaviour for the litter types of beech and Ironwood, whereas in the case of maple and hornbeam litters, the fire spread parallelly and perpendicularly to contour lines, respectively. The burned area increased in an irregular pattern as the forest floor slope gradient was increased. Moreover, the skewed pattern of the burned area for the forest floor composed of maple, beech, ironwood and hornbeam litter was described as high, low, moderate and low, respectively. The fire spread angle in forest floor associated with maple and beech litters changed with litter thickness. Finally, litter thickness had a significant effect on the direction of fire spread and this was more prominent with hornbeam litter.
A new software tool has been developed to simulate surface wind speed and direction at the 100m to 300 m scale. This tool is useful when trying to estimate fi re behavior in mountainous terrain. It is based on widely used computational fl uid dynamics technology and has been tested against measured wind fl ows. In recent years it has been used to support fi re management decisions to improve fi refi ghter and public safety, understand the environmental conditions associated with entrapment fi res, improve prescribed fi re prescriptions, and estimate fi re potential. Outputs from this tool include tiff images, GIS shape fi les, and FARSITE wind input fi les.
There is currently no spatial wildfi re spread and growth simulation model used commonly across New Zealand or Australia. Fire management decision-making would be enhanced through the use of spatial fi re simulators. Various groups from around the world met in January 2006 to evaluate the applicability of different spatial fi re spread applications for common use in both New Zealand and Australia. Devel- opers and researchers from Canada, the United States, and Australia were invited to apply Prometheus, FARSITE, and other similar models to New Zealand and Australian wildfi res in grass, scrub, and forested fuel types. Although the lack of site-specifi c fuel models and weather data were a concern, coarse spatial and temporal data inputs proved adequate for modeling fi res within a reasonable margin of error. The choice of grass models proved less important than expected since spread rates were easily manipulated through moisture content values during calibration. The fi nal modeled perimeters are affected by several user inputs that are impossible to separate from model error. These various inputs exist to allow experienced users to approximate local environmental variability as closely as possible to obtain successful outputs. Rather than attempt to quantify direct comparisons, local users concluded it was more important to choose an application that provides an appropriate level of functionality, that is compatible with current data and fi re management systems, and that can be easily modifi ed to use unique and varied fi re spread equations. Prometheus and FARSITE performed very well and will be further investigated to understand how each might be customized for use with local fi re spread models. This paper describes the process and results of testing some existing fi re growth simulation models for use on fi res in New Zealand and Australia.
ABSTRACT This paper addresses community-scale fires, which have also been called urban/wildland interface or inter- mix fires. These fires arise when,wildland fires invade the built environment,and attack structures as well as wildland fuels. The prediction of the spread of wildland fires, such as those occurring out West during the summer of 2000, has been accomplished through ”operational” mathematical models. These models are based on empirical correlations for wildland fuels and have generally performed well. They fail, however, when,the fire spreads to the built environment,where the empirical correlations no longer apply and where there is greatly increased potential for property damage, injury and death. The Oakland and Berkeley Hills fire of October 21, 1991, and the Los Alamos fires of May 2000 are examples of community-scale fires.
Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.
A software system aimed at the simulation of fire spread over complex topography is presented. The software implements a semi-empirical model for fire rate of spread, which takes as input local terrain slope, parameters describing fuel properties as well as the wind speed and direction. Fire shape is described with recourse to an ellipse-type model. Two different models are implemented for the simulation of the wind field. Both these models predict wind velocity and direction based on local observation taken at meteorological stations. The whole system was developed under a graphical interface, aiming at a better ease of use and output readability so as to facilitate its application under operational conditions. This work describes the mathematical models employed, provides an overview of the graphical interface and presents the results of some simulations tested against experimental data.
We propose a new model to predict the spatial and temporal behaviour of wildfires. Fire spread and intensity were simulated using a cellular automaton model. Monte Carlo techniques were used to provide fire risk probabilities for areas where fuel loadings and topography are known. Implementation of the model allows the linking of fire monitoring using remotely sensed data, potentially in real time, to rapid simulations of predicted fire behaviour. Calibration of the model is based on thermal infrared remotely sensed imagery of a test burn during 1986 in the San Dimas experimental forest. The model and its various implementations show distinct promise for real-time fire management and fire risk planning. -from Authors
A wildland fire is a serious threat for forest ecosystems in Southern Europe affecting severely and irreversibly regions of significant ecological value as well as human communities. To support decision makers during large-scale forest fire incidents, a multidisciplinary system has been developed that provides rational and quantitative information based on the site-specific circumstances and the possible consequences. The system's architecture consists of several distinct supplementary modules of near real-time satellite monitoring and fire forecast using an integrated framework of satellite Remote Sensing, GIS, and RDBMS technologies equipped with interactive communication capabilities. The system may handle multiple fire ignitions and support decisions regarding dispatching of utilities, equipment, and personnel that would appropriately attack the fire front. The operational system was developed for the region of Penteli Mountain in Attika, Greece, one of the mountain areas in the country most hit by fires. Starting from a real fire incident in August 2000, a scenario is presented to illustrate the effectiveness of the proposed approach.
The range and variation of historical landscape dynamics could provide a useful reference for designing fuel treatments on today's landscapes. Simulation modeling is a vehicle that can be used to estimate the range of conditions experienced on historical landscapes. A landscape fire succession model called LANDSUMv4 (LANDscape SUccession Model version 4.0) is presented here as a tool for estimating historical range and variation (HRV) of landscape characteristics. The model simulates fire and succession on fine scale landscapes for land management applications. It simulates vegetation development as a deterministic process by changing the species composition and stand structure assigned to a polygon. Disturbance initiation is modeled stochastically and disturbance effects are based on the current vegetation conditions of the polygon. Details of all model algorithms are discussed and the model is demonstrated for two applications. Results of an extensive sensitivity and model behavior analysis are also presented.
A simple elliptic model is developed for the spread of a fire front through grassland. This is used to predict theoretical fire fronts, which agree closely with those obtained in practice.
KRF-2006-611-D00035) and the authors gratefully appreciate Hanjin Information Systems and Telecommuni-cation Co., Ltd. for providing the aerial images Classifica-tion of fire simulation systems
- D Meisner
Government (MOEHRD) (KRF-2006-611-D00035) and the authors gratefully appreciate Hanjin Information Systems and Telecommuni-cation Co., Ltd. for providing the aerial images. References Albright, D., & Meisner, B.N. (1999) Classifica-tion of fire simulation systems, Fire Manage-ment Notes, vol. 59, no. 2, pp. 5–12.
Classification of fire simulation systems
- D Albright
- B N Meisner
Albright, D., & Meisner, B.N. (1999) Classification of fire simulation systems, Fire Management Notes, vol. 59, no. 2, pp. 5–12.