Forest Fire Risk Assessment Using Point Process Modeling & Monte Carlo Fire Simulation: A Case Study in Gyeongju, South Korea

Forest fire risk assessment becomes critical for developing forest and fire management strategies in Korea since the magnitude of damage from fires significantly increased over the past decades. Fire behavior probability is one of the major components in quantifying fire risk, and is often presented as burn probability. Burn probability estimation requires a proper estimation of fire occurrence probability because fire spread is largely influenced by ignition locations in addition to other environmental factors, such as weather, topography, and land covers. The objective of this study is to assess forest fire risk over a large forested landscape in and around the City of Gyeongju, Republic of Korea, while incorporating fire occurrence probability into estimation of burn probability. A fire occurrence probability model with spatial covariates and autocorrelation was developed using historical record of fire occurrence between 1991 and 2012 and a spatial point processing (SPP) method. A total of 502 fire ignition points were generated using the fire occurrence probability model. Monte Carlo fire spread simulations were performed from the ignition points under the 95% extreme weather scenario, resulting in burn probability estimation for each land parcel across the landscape. Finally, the burn probability was combined with government-appraised land property value to assess potential loss value per land parcel due to forest fires. The density of forest fires of the study landscape was associated with lower elevation, moderate slope, coniferous land cover, distance to road, and higher tomb density. A positive spatial autocorrelations between the locations of fire ignition was also found. An area-interaction point process model including the spatial covariate effects and interpoint interaction term appeared to be suitable as a fire occurrence probability model. A correlation analysis among the fire occurrence probability, burn probability, land property value, and potential value loss indicates that fire risk is largely associated with spatial pattern of burn probability (Pearson’s correlation =0.7084). These results can provide forest and fire management authorities in the study region with useful information for decision making. It is also hoped that the methodology presented here can provide an improved framework for assessing fire risk in other regions

A SPATIAL-TEMPORAL POINT PROCESS MODEL FOR ESTIMATING PROBABILITY OF WILDFIRES IN LOS ANGELES COUNTY

In Los Angeles County, wildfires are among the most catastrophic environmental events caused by regional characteristics and climate change. In this study, we develop a point process model to estimate the probability of wildfires based on historical weather data and past wildfires data from Los Angeles County from 2004 to 2018. First, we partition Los Angeles County into small rectangular regions, called voxels, with daily temporal resolution. Then, we use random forests and generalized additive models to obtain estimated probabilities on a training data set. In addition to daily weather and fuel-condition measurements, our models incorporate seasonal and geographical effects. Because measurements on weather and fuel conditions are available only from a fixed set of remote automated weather stations, their data must be averaged to relate them to the voxel level, and the way this is done is a factor in modeling. Through the developed model, it is possible to obtain localized, estimated probabilities of wildfires. Ultimately, this tool can aid Los Angeles County Fire Department in improving its capability and effectiveness.So-ryeong, Republic of Korea Air ForceApproved for public release. Distribution is unlimited

A review of machine learning applications in wildfire science and management

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 progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Evaluation of geographically weighted logistic model and mixed effect model in forest fire prediction in northeast China

IntroductionForest fires seriously threaten the safety of forest resources and human beings. Establishing an accurate forest fire forecasting model is crucial for forest fire management.MethodsWe used different meteorological and vegetation factors as predictors to construct forest fire prediction models for different fire prevention periods in Heilongjiang Province in northeast China. The logistic regression (LR) model, mixed-effect logistic (mixed LR) model, and geographically weighted logistic regression (GWLR) model were developed and evaluated respectively.ResultsThe results showed that (1) the validation accuracies of the LR model were 77.25 and 81.76% in spring and autumn fire prevention periods, respectively. Compared with the LR model, both the mixed LR and GWLR models had significantly improved the fit and validated results, and the GWLR model performed best with an increase of 6.27 and 10.98%, respectively. (2) The three models were ranked as LR model < mixed LR model < GWLR model in predicting forest fire occurrence of Heilongjiang Province. The medium-and high-risk areas of forest fire predicted by the GWLR model were distributed in western and eastern parts of Heilongjiang Province in spring, and western part in autumn, which was consistent with the observed data. (3) Driving factors had strong temporal and spatial heterogeneities; different factors had different effects on forest fire occurrence in different time periods. The relationship between driving factors and forest fire occurrence varied from positive to negative correlations, whether it’s spring or autumn fire prevention period.DiscussionThe GWLR model has advantages in explaining the spatial variation of different factors and can provide more reliable forest fire predictions

Ecological legacies of drought, fire, and insect disturbance in western North American forests, The

Includes bibliographical references.2015 Fall.Temperate forest ecosystems are subject to various disturbances including insect agents, drought and fire, which can have profound effects on the structure of the ecosystem for many years after the event. Impacts of disturbance can vary widely, therefore an understanding of the legacies of an event are critical in the interpretation of contemporary forest patterns and those of the near future. The primary objective of this dissertation was to investigate the ecological legacies of drought, beetle outbreak and ensuing wildfire in two different ecosystems. A secondary objective of my research, data development, was motivated by a lack of available data which precluded ecological investigation of each disturbance. I studied the effects of drought on deciduous and coniferous forest along a forest-shrubland ecotone in the southern portion of the Wyoming Basin Ecoregion. The results show that forests in the region have experienced high levels of cumulative drought related mortality over the last decade. Negative trends were not consistent across forest type or distributed randomly across the study area. The patterns of long-term trends highlight areas of forest that are resistant, persistent or vulnerable to severe drought. In the second thread of my dissertation, I used multiple lines of evidence to retrospectively characterize a landscape scale mountain pine beetle disturbance from the 1970s in Glacier National Park. The lack of spatially explicit data on this disturbance was a major data gap since wildfire had removed some of the evidence from the landscape. I used this information to assess the influence of beetle severity on the burn severity of subsequent wildfires in the decades after the outbreak. Although many factors contribute to burn severity, my results indicate that beetle severity can positively influence burn severity of wildfire. This is likely due to the change in forest structure in the decades after the outbreak and not as a direct result of tree mortality from the outbreak. The long-term perspective of this study suggests that ecological legacies of high severity disturbance may continue to influence subsequent disturbance for many years after the initial event. This work also provides insight on future disturbance interactions associated with the recent mountain pine beetle outbreak that has impacted tens of millions of hectares in western North America over the last two decades

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard

Sampling strategies for forest aerial detection survey in Colorado

2015 Summer.Aerial detection survey (ADS) has been commonly employed in forest surveys in the United States for detecting forest damage and monitoring forest health. In Colorado, ADS by USDA Forest Service has conducted annual 100% census of government forested land for more than 20 years with the goal of achieving information about forest damage due to different causal agents and disorders. Sketchmapping has been commonly employed in ADS with the goal of detecting and documenting on maps mortality, defoliation and other visible forest change from aircraft. At medium and large scale, sketchmapping is a suitable technique for forest monitoring that provides valuable information in forest health. This dissertation deals with data of forest area damaged by five causal agents mountain pine beetle, spruce beetle, western spruce budworm, pin engraver, and Douglas fir beetle and two disorders subalpine fir mortality and sudden aspen decline. The combined areas damaged by all causes were also considered. Data were downloaded from ADS in Colorado from 1994 to 2013 as polygon shapefiles with associated information such as causal agents or disorders, area damaged, and type of forest. The goal of my dissertation was to identify an appropriate sampling strategies to archive good estimates of total area damaged, to decrease survey cost, and to increase safety by reducing the amount of flights. To approach this goal, four sample designs for estimating total area damaged caused by various causal agent were evaluated: simple random sampling, stratified random sampling, probability proportional to size, and non-alignment systematic sampling. A GIS layer of 150 transects covering Colorado’s forestlands was developed and represented the sample unit for my study. Each transect was 3.2 km wide and 625 km long and was numbered from 1 to 150 from south to north. Each sample design was evaluated using eight sample sizes (10, 15, 20, 25,30, 35, 50, and 70) and applied to the seven damages and the combined damaged area. The statistical properties were evaluated to determine the optimal sample design for estimating area damaged caused by different causal agents. The spatio-temporal characteristics of area damaged that influence precision and accuracy of estimate were considered. Most of the damaged forest areas by single causal agents and disorders showed aggregated spatial patterns; whereas the combined damaged areas were uniformly distributed across the landscape. A loss plus cost function was employed to determine the optimal sample size for each sample design and analyzed for the cost advantage of alternative sample designs. We found that stratified random sampling was the most optimal sample design by producing the highest percentage of unbiased estimates of total area damaged and the smallest variances. The next best sampling designs were simple random sampling and probability proportional to size. The non-alignment systematic sampling was the worst for estimating total area damaged both for individual causal agents and disorders and all causal agents combined. The optimal sample size varied by sample design and causal agents and disorders as well as the level of confidence. Optimal sample size increased with increasing variability in the population and as the desired level of confidence increased. Larger samples were required to simultaneously provide estimates for multiple causal agents and disorder with reasonable levels of precision when compared to a single causal agent. Stratified random sampling was the most cost effective when compared with other sample designs. For example, the cost advantage of stratified sampling over random sampling for estimating the damage from subalpine-fir mortality was $85,000 per year. In contrast, PPS sampling had a cost disadvantage of -$13,000 per year when compared with simple random sampling and -$95,000 per year when compared with stratified sampling for estimating the total damage from all causal agents combined at the 0.95 level of confidence

Estimating PM2.5 Concentrations Using 3 KM MODIS AOD Products: A Case Study in British Columbia, Canada

PM2.5 refers to fine particles with diameters smaller than 2.5 μm. The rising level of PM2.5 reveals adverse effects on climate change, economic losses, international conflicts, and public health. Exposure to the high level of PM2.5 would increase the risk of premature death, especially for people with weak immune systems, such as children and elder people. The main sources of PM2.5 include combustion of biomass, vehicle and industrial emissions, and wildfire smoke. British Columbia (BC), Canada, with a land area of 944,735 km2 and 27 regional districts, experienced its record-breaking wildfire season in 2017. However, due to the uneven distribution of PM2.5 ground monitoring stations in BC, PM2.5 concentrations in the rural area are difficult to retrieve. Remote sensing techniques and geographical information systems (GIS) could be used as supplementary tools to estimate PM2.5 concentrations. Aerosol Optical Depth (AOD) has been proven to have a strong correlation with PM2.5. Moderate Resolution Imaging Spectroradiometer (MODIS) provides AOD products in both 3 km and 10 km resolutions. The 3 km MODIS AOD products were released in 2013, and have been widely used to estimate PM2.5 concentrations in several studies. This study adopted the 3 km Aqua MODIS AOD products to estimate PM2.5 concentrations in BC in the year of 2017 by combining ground station measurements, meteorological and supplementary data. MODIS AOD products were validated with ground-level AErosol RObotic NETwork (AERONET) AOD data. The Multiple Linear Regression (MLR) model, Geographically Weighted Regression (GWR) model, and a novel theoretical model were then conducted to estimate PM2.5 concentrations by integrating MODIS AOD products, ground-level PM2.5 concentrations, meteorological and supplementary data. After comparing the performance of the three models, the GWR model was used to generate annual, seasonal, and monthly spatial distribution maps of PM2.5. The application feasibility of MODIS AOD products in predicting PM2.5 was also examined. The validation results showed that there was a strong correlation between the MODIS AOD and the AERONET AOD. The GWR model had the best prediction performance, while the MLR generated the worst prediction results. After analyzing the spatial distribution maps of PM2.5 with ground-level PM2.5 distribution maps, it could be concluded that the PM2.5 concentrations estimated by the GWR model almost follow the same trend as ground station measured PM2.5. In addition, PM2.5 concentrations were the highest in summer and August based on the estimation results of seasonal and monthly GWR models. It indicated that the application feasibility of MODIS AOD products in predicting PM2.5 concentrations during BC’s wildfire season was high