A deep learning approach to downscale geostationary satellite imagery for decision support in high impact wildfires

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models

An implementation of the rothermel fire spread model in the R programming language

This note describes an implementation of the Rothermel fire spread model in the R programming language. The main function, ros(), computes the forward rate of spread at the head of a surface fire according to Rothermel fire behavior model. Additional functions are described to illustrate the potential use and expansions of the package. The function rosunc() carries out uncertainty analysis of fire behavior, that has the ability of generating information-rich, probabilistic predictions, and can be coupled to spatially-explicit fire growth models using an ensemble forecasting technique. The function bestFM() estimates the fit of Standard Fuel Models to observed fire rate of spread, based on absolute bias and root mean square error. Advantages of the R implementation of Rothermel model include: open-source coding, cross-platform availability, high computational efficiency, and linking to other R packages to perform complex analyses on Rothermel fire predictions

Probabilistic fire spread forecast as a management tool in an operational setting

Background: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events. Results: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial–temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected. Conclusion: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these firesinfo:eu-repo/semantics/publishedVersio

Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators

Refining fuel loads in LPJ-GUESS-SPITFIRE for wet-dry areas : with an emphasis on Kruger National Park in South Africa

Eld är en av de viktigaste störningsprocesserna som påverkar den terrestra biosfären genom att forma vegetationens spridning, komposition, struktur, växtdiversitet och biokemiska cykler. Några av de mest påverkade ekosystemen är våt-torra områden, här klassificerade som savann eller Medelhavsområden. Denna påverkan kan studeras på olika sätt, antingen genom ett långvarigt och fortlöpande experiment, som i Kruger National Park i Sydafrika, eller genom att använda sig av modeller, som till exempel LPJ-GUESS-SPITFIRE. SPITFIRE är en processbaserad eldmodell som har kopplats samman med en dynamisk vegetationsmodell för att förutspå eldens spridning, intensitet och hur länge det kan brinna. I LPJ-GUESS-SPITFIRE används en faktor som kallas omsättningshastigheten, vilket är ett halvtidsvärde för nedbrytning av biologiskt avfall vid en viss temperatur och fuktighet. Den används för att räkna ut hur mycket tillgängligt växtmaterial som finns i det modellerade området för att kunna bestämma eldintensiteten. Denna konstant är satt till 2.85 och är densamma i alla världens ekosystem. I verkligheten beror nedbrytningen på vilka de aktiva förhållandena är och det är därför orealistiskt att använda samma konstant. Data från experimentet i Kruger National Park användes för att förbättra parametrarna i DGVM LPJ_GUESS_SPITFIRE för att hitta mer representativa nedbrytningsvärden i våt-torra områden. Avfallet delades in i två pooler som är det huvudsakliga bränslet för markbränder; avfall i form av löv och trä och deras omsättningshastigheter studerades individuellt. En litteraturstudie visade att de nuvarande värdet för löv är för högt och för lågt satt för trä. Nya värden testades, avfallsmängder för olika eldintervall räknades ut och jämfördes med mätta mängder i Kruger National Park. En omsättningshastighet på 0.6 år visade sig vara bästa representanten för våt-torra områden för lövavfall utan träd. Ett värde för träavfall kunde inte hittas, då det inte fanns tillgång till data att jämföra värdena med. Däremot kunde effekten av omsättningshastigheten studeras för att få en bättre förståelse för vilken effekt det hade på dessa typer av ekosystem. Denna uppsats visar vikten av att förstå hur olika ekosystem fungerar och att det fortfarande kan göras förbättringar i den använda modellen.Fire is one of the most important disturbance processes affecting the terrestrial biosphere, altering the vegetation composition and distribution, structure, plant diversity and biogeochemical cycles. Some of the most influenced ecosystems are wet-dry areas, here classified as savannah or Mediterranean regions. The influence can be studied in different ways, either by long-term experiments like the burn plot trial in Kruger National Park in South Africa, or by the use of models, for example LPJ-GUESS-SPITFIRE. SPITFIRE is a process-based fire model which have been coupled to a dynamic global vegetation model in order to predict fire spread, intensity and residence time of fires. In LPJ-GUESS-SPITFIRE a variable called the turnover time, which is the logarithmic decomposition rate for litter at a defined temperature and moisture content is used to calculate how much available litter (or in other words fuel) is available in the modelled patch in order to determine the fire intensity. This constant is set to 2.85 and is used for all ecosystems around the world. In reality however, the turnover time varies depending on the existing circumstances, which makes it unrealistic to use the same constant. Data obtained from the burn plot trial in Kruger National Park was used for parameter refinements within the DGVM LPJ-GUESS-SPITFIRE in order to find more representative values. The litter pool was divided into two pools which are the main input for surface fires; leaf litter and wood litter and their turnover times were studied individually. A literature study showed that presently used values for the leaf turnover time are overestimated by the model and underestimated for wood. In order to find more suitable parameters, new values were tested, litter amounts for different fire return intervals were calculated and compared with measured litter amounts in the experiment in Kruger National Park. A turnover time of 0.6 years for leaf litter without trees was found to be the best representative for wet-dry areas. The turnover time for woody litter could not be adjusted, since there was no available data to compare them with. However, the effect of turnover times within the range found in the literature study was examined in order to better understand the effect wood litter had on these kinds of ecosystems. This thesis shows the importance to understand how different ecosystems work and that improvements still can be made in the used model

Efficient knowledge retrieval to calibrate input variables in forest fire prediction

Forest fires are a serious threat to humans and nature from an ecological, social and economic point of view. Predicting their behaviour by simulation still delivers unreliable results and remains a challenging task. Latest approaches try to calibrate input variables, often tainted with imprecision, using optimisation techniques like Genetic Algorithms. To converge faster towards fitter solutions, the GA is guided with knowledge obtained from historical or synthetical fires. We developed a robust and efficient knowledge storage and retrieval method. Nearest neighbour search is applied to find the fire configuration from knowledge base most similar to the current configuration. Therefore, a distance measure was elaborated and implemented in several ways. Experiments show the performance of the different implementations regarding occupied storage and retrieval time with overly satisfactory results.Los incendios forestales son una grave amenaza para seres humanos y para la naturalza desde el punto de vista ecológico, social y económico. Predecir su comportamiento usando simulaciones todavía da resultados poco fiables y sigue siendo una tarea desafiante. Trabajos más recientes, intentan calibrar variables de entrada, muchas veces imprecisas, aplicando técnicas de optimización como algoritmos genéticos. Para converger más rápido hacia soluciones más adecuadas, el algoritmo genético es guiado con conocimiento obtenido de fuegos históricos o sintéticos. Hemos desarrollado un método robusto y eficiente para almacenar y recuperar ese conocimiento. Aplicamos la búsqueda del vecino más cercano para encontrar la configuración del fuego más similar a la configuración actual dentro de la base de conocimiento. Para esto, hemos elaborado una función de distancia y la hemos implementado de diferentes maneras. Experimentos muestran el rendimiento de las distintas implementaciones considerando el almacenamiento ocupado y el tiempo de recuperación con resultados muy satisfactorios.Els incendis forestals són una amenaça important tant pels homes com per a la natura des d'un punt de vista ecològic, social i econòmic. La predicció del comportament dels incendis forestals utilitzant simulació encara genera resultats poc fiables i, per tant, segueix essent un desafiament important. Aproximacions recents a aquest problema, intenten calibrar les variables d'entrada dels simuladors, les quals sovint presenten un grau important d'incertesa, utilitzant tècniques d'optimització com poden ser els Algoritmes Genètics (AG). Per tal de que la convergència dels AG a una solució bona sigui ràpida, l'AG es guia mitjançant el coneixement obtingut d'històrics d'incendis o focs sintètics. Per aquest treball s'ha desenvolupat un mètode eficient i robust d'emmagatzemament i recuperació del coneixement. El mètode anomenat Nearest Neighbour Search s'aplica per trobar la configuracióo guardada en la base de coneixements que més s'assembli a la configuracióo real de l'incendi. Per a tal efecte, s'ha desenvolupat una mètrica de distància la qual ha estat implementada de diferents formes alternatives. L'experimentació realitzada mostra resultats encoratjadors en el rendiment de les diferents implementacions tenint en compte l'emmagatzemament ocupat i el temps de recuperació de la informació