MODELING OF PHYSICALLY-BASED PREDICTIVE FIRE EVENTS IN A VIRTUAL ENVIRONMENT FROM GEOSPECIFIC DATA

Climate change and corresponding extreme weather events, such as wildfires, present significant threats to personnel and critical infrastructure on military installations and the communities in which they live. The inherent dynamic and unpredictable nature of wildfires makes it imperative to develop robust frameworks for understanding and managing wildfire risk. This thesis addresses this urgency by developing a computational technique to simulate wildfire impacts through virtual modeling using geospatial data. Using a fast-running fire modeling software, HFIRE, surface fire spreads are simulated on the fictional continent of Dystopia. A Monte Carlo simulation is conducted to analyze wildfire events, assess fire spread, and evaluate direct and indirect impacts to a designated military installation. Model excursions consider how potential climate change consequences affect these impacts. The findings underscore that drier conditions invariably result in increased fire severity and more frequent impacts to the military installation. Furthermore, it enables the identification of high-risk areas subject to wildfires. These results can facilitate enhanced preventive measures and more effective emergency responses, thereby minimizing the vulnerability of military installations to wildfires in an era of climate change.Approved for public release. Distribution is unlimited.Major, United States Marine CorpsStrategic Environmental Research and Development Program (SERDP

Three Dimensional Visualization of Fire Spreading Over Forest Landscapes

Previous studies in fire visualization have required high end computer hardware and specialized technical skills. This study demonstrated fire visualization is possible using Visual Nature Studio and standard computer hardware. Elevation and vegetation data were used to create a representation of the New Jersey pine barren environment and a forest compartment within Hobcaw Barony. Photographic images were edited to use as image object models for forest vegetation. The FARSITE fire behavioral model was used to model a fire typical of that area. Output from FARSITE was used to visualize the fire with tree models edited to simulate burning and flame models. Both static and animated views of the fire spread and effects were visualized. The two visualization methods were compared for advantages and disadvantages. VNS visualizations were more realistic, including many effects such as ground textures, lighting, user made models, and atmospheric effects. However the program had higher hardware requirements and sometimes rendered images slowly. ArcScene had lower hardware requirements and produced visualizations with real time movement. The resulting images lacked many of the effects found in VNS and were more simplistic looking

DATA ASSIMILATION AND VISUALIZATION FOR ENSEMBLE WILDLAND FIRE MODELS

This thesis describes an observation function for a dynamic data driven application system designed to produce short range forecasts of the behavior of a wildland fire. The thesis presents an overview of the atmosphere-fire model, which models the complex interactions between the fire and the surrounding weather and the data assimilation module which is responsible for assimilating sensor information into the model. Observation plays an important role in data assimilation as it is used to estimate the model variables at the sensor locations. Also described is the implementation of a portable and user friendly visualization tool which displays the locations of wildfires in the Google Earth virtual globe

IoT-inspired Framework for Real-time Prediction of Forest Fire

Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS

Forest fire simulator system for emergency resources management support

Europe suffers approximately 65,000 fires every year, which burn, on average, half a million hectares of forest areas [1]. The main direct effect of forest fires is the destruction of the natural landscape and the consequent loss of ecosystem service that have drastic economic impact, but mainly and much more important, fires also result in the loss of human lives every year. Although being forest fires a problem present in all EU members, the most affected areas to this hazards are the southern countries due to their climatological conditions. All affected countries invest lots of resources to minimize fire damages, but many times when dealing with large fires, regional and national disaster management units are lack of efficient and reliable tools to help wildfire analysts. In this work, we describe a process to generate on-line wildfire simulations coupled with the regional weather forecast service (Servei Meteorològic de Catalunya, SMC) and the helicopter company (Helipistas S.L) who provides isochronous perimeters of the fire behaviour in a certain moment of the emergency and how both of this data sources feed the inputs for the simulation process.Europa sufre aproximadamente 65,000 incendios cada año, de media, medio millón de hectáreas forestales[1]. El principal efecto de los fuegos forestales es la destrucción de la superfície natural y como consecuencia la pérdida del ecosistema y el gran impacto económico, pero principamente y de manera mucho más importante el fuego tambien repercute en la pérdida de vidas humanas año tras año. Los fuegos forestales además de ser un problema para los miembros de la UE, se ven repercutidos, especialmente los paises del sur debido a sus condiciones climatológicas. Todos estos paises afectados invierten gran cantidad de recursos para minimizar estos efectos. Generalmente cuando se trata de grandes incendios forestales, las unidades de mando de estos medios de exinción a nivel regional y nacional se ven necesitados de herramientas eficientes y útiles para el análisis de la predicción del comportamiento de estos grandes incendios forestales. En este trabajo, describimos un sistema de predicción de incendios forestales acoplado con el servicio meteorológicos de catalunya (SMC) y la empresa de helicópteros (Helipistas S.L) los cuales proveen de los perímetros del incendio en un instante de tiempo de la emergencia y cómo estas dos fuentes de datos se anexan al proceso de simulación.Europa pateix aproximadament 65,000 incendis cada any, de mitja, cada mig-milió d'hectàrees forestals[1]. El principal efecte dels focs forestals es la destrucció de la superfície natural i com a conseqüència la pèrdua de l'ecosistema i el gran impacte econòmic, però principalment i de manera molt més important el foc, també, repercuteix en la pèrdua de vides humanes any rere any. Els focs forestals a més a més de representar un problema pels països membres de la UE, es veuen afectats els països del Sud degut a les seves condicions climatològiques. Tots aquests països afectats inverteixen grans quantitat de recursos per a minimitzar aquests efectes. Generalment quan es tracta de grans incendis forestals, les unitats de comandament d'aquests medis d'extinció a nivell regional i nacional es veuen necessitats d'eines útils i eficients per a l'anàlisis de la predicció en el comportament dels grans incendis forestals. En aquest treball, descrivim un sistema de predicció d'incendis forestals acoblat amb el servei meteorològic de Catalunya (SMC) i l'empresa d'helicòpters (Helipistas S.L) els quals proveïxen dels perímetres de l'incendi en un instant de temps de l'emergència i com aquestes dos fonts de dades annexen al procés de simulació

Modelling pyro-convection phenomenon during a mega-fire event in Portugal

The present study contributes to an increased understanding of pyro-convection phenomena by using a fire-atmosphere coupled simulation, and investigates in detail the large-scale meteorological conditions affecting Portugal during the occurrence of multiple mega-fires events on 15 October 2017. Two numerical simulations were performed using the MesoNH atmospheric model. The first simulation, was run for a large single domain (300 x 250 grid points) with a 15 km resolution. In the second one, the MesoNH was coupled to a fire propagation model (ForeFire) to study in detail the Quiaios's fire. To optimize both high resolution in the proximity of the fire region and computational efficiency, the simulation is set up using 3 nested domains (300 x 300 grid points) with horizontal resolution of 2000 m, 400 m, and 80 m respectively. The emission into the atmosphere of the heat and the water vapour fluxes caused by the evolving fire is managed by the ForeFire code. The fire spatio-temporal evolution is based on an assigned map, which follows what reported by public authorities. At the large scale, the simulation shows the evolution of the hurricane Ophelia, pointing out the influence of south/southwest winds on the rapid spread of active fires, as well as the subtropical moisture transport toward mainland Portugal in the early evening, when violent pyro-convective activity was observed in Central Portugal. The coupled simulation allowed to reproduce the formation of a PyroCu cloud inside the smoke plume. The convective updraughts caused by the fire led to the vertical transport of water vapour to higher levels and enhanced the development of a high-based cloud over a dry atmospheric layer within the smoke plume

Wildfire Hazard and Risk Assessment

Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes

Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% of the Earth's biodiversity. However, we are witnessing an average loss of 480 ha of forest every hour because of destructive wildfires spreading across the globe. To effectively mitigate the threat of wildfires, it is crucial to devise precise and dependable approaches for forecasting fire dynamics and formulating efficient fire management strategies, such as the utilisation of fuel models The objective of this study was to enhance forest fuel classification that considers only structural information, such as the Prometheus model, by integrating data on the fire responses of various tree species and other vegetation elements, such as ground litter and shrubs. This distinction can be achieved using multispectral (MS) Light Detection and Ranging (LiDAR) data in mixed forests. The methodology involves a novel approach in semantic classifications of forests by generating synthetic data with semantic labels regarding fire responses and reflectance information at different spectral bands, as a real MS scanner device would detect. Forests, which are highly intricate environments, present challenges in accurately classifying point clouds. To address this complexity, a deep learning (DL) model for semantic classification was trained on synthetic point clouds in different formats to achieve the best performance when leveraging MS data Forest plots in the study region were scanned using different Terrestrial Laser Scanning sensors at wavelengths of 905 and 1550 nm. Subsequently, an interpolation process was applied to generate the MS point clouds of each plot, and the trained DL model was applied to classify them. These classifications surpassed the average thresholds of 90% and 75% for accuracy and intersection over union, respectively, resulting in a more precise categorisation of fuel models based on the distinct responses of forest elements to fire. The results of this study reveal the potential of MS LiDAR data and DL classification models for improving fuel model retrieval in forest ecosystems and enhancing wildfire management effortsMinisterio de Universidades | Ref. FPU16/00855Agencia Estatal de Investigación | Ref. PCI2020-120705-2Universidade de Vigo/CISU

Simulation Software as a Service and Service-Oriented Simulation Experiment

Simulation software is being increasingly used in various domains for system analysis and/or behavior prediction. Traditionally, researchers and field experts need to have access to the computers that host the simulation software to do simulation experiments. With recent advances in cloud computing and Software as a Service (SaaS), a new paradigm is emerging where simulation software is used as services that are composed with others and dynamically influence each other for service-oriented simulation experiment on the Internet. The new service-oriented paradigm brings new research challenges in composing multiple simulation services in a meaningful and correct way for simulation experiments. To systematically support simulation software as a service (SimSaaS) and service-oriented simulation experiment, we propose a layered framework that includes five layers: an infrastructure layer, a simulation execution engine layer, a simulation service layer, a simulation experiment layer and finally a graphical user interface layer. Within this layered framework, we provide a specification for both simulation experiment and the involved individual simulation services. Such a formal specification is useful in order to support systematic compositions of simulation services as well as automatic deployment of composed services for carrying out simulation experiments. Built on this specification, we identify the issue of mismatch of time granularity and event granularity in composing simulation services at the pragmatic level, and develop four types of granularity handling agents to be associated with the couplings between services. The ultimate goal is to achieve standard and automated approaches for simulation service composition in the emerging service-oriented computing environment. Finally, to achieve more efficient service-oriented simulation, we develop a profile-based partitioning method that exploits a system’s dynamic behavior and uses it as a profile to guide the spatial partitioning for more efficient parallel simulation. We develop the work in this dissertation within the application context of wildfire spread simulation, and demonstrate the effectiveness of our work based on this application

Development of a participatory virtual studio for ecological planning: a case study of wildfire simulation in ecological planning.

Zhao Yibin.Thesis submitted in: November 2001.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 103-111).Abstracts in English and Chinese.Abstract --- p.IACKNOWLEDGEMENT --- p.VTable of Contents --- p.VIIIList of Tables --- p.IXList of Figures --- p.XChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Research background and problem statement --- p.1Chapter 1.2 --- Research objectives --- p.6Chapter 1.3 --- Methodology --- p.7Chapter 1.4 --- Significance of this study --- p.9Chapter 1.5 --- Organization of the thesis --- p.10Chapter Chapter 2 --- "Literature review: wildfire behavior simulation, Web GIS and public participation GIS" --- p.11Chapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Investigating wildfire behavior --- p.12Chapter 2.3 --- Modeling wildfire with GIS --- p.20Chapter 2.4 --- Emergence of the Web GIS --- p.27Chapter 2.5 --- New agenda from public participation --- p.28Chapter 2.6 --- Summary --- p.31Chapter Chapter 3 --- System design: requirements analysis and feasibility analysis --- p.34Chapter 3.1 --- Introduction --- p.34Chapter 3.2 --- Analysis of functions requirement --- p.35Chapter 3.3 --- A host of solutions --- p.41Chapter 3.4 --- Summary --- p.52Chapter Chapter 4 --- Simulating the wildfire --- p.53Chapter 4.1 --- Physical Setting of experiment area and data preparation --- p.53Chapter 4.2 --- Adaptation and formularization of the Rothermel's fire behavior model --- p.60Chapter 4.3 --- Fire spreading algorithm --- p.66Chapter 4.4 --- Defining wildfire with Object Oriented Design (OOD) method --- p.71Chapter 4.5 --- Summary --- p.74Chapter Chapter 5 --- Participation process with interactive tools empowered by IT technologies --- p.76Chapter 5.1 --- Comprehending the problem in an interactive way --- p.76Chapter 5.2 --- Performing wildfire simulation --- p.81Chapter 5.3 --- Submitting of end users comments --- p.84Chapter 5.4 --- Discussion bulletin board --- p.94Chapter 5.5 --- Summary --- p.96Chapter Chapter 6 --- Discussions and conclusions --- p.98Chapter 6.1 --- Research limitations and discussions --- p.98Chapter 6.2 --- Conclusions --- p.99BIBLIOGRAPHY --- p.103Appendix 1 .Defining MapService with ArcXML --- p.112Appendix 2.Defining MapNotes with ArcXML --- p.11