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

Built by Fire: Wildfire Management and Policy in Canada

abstract: Wildfire is an inescapable feature of Canadian landscapes, burning an average of over two million hectares annually and causing significant repercussions for communities, infrastructure, and resources. Because fire is managed provincially, each jurisdiction has developed a distinctive approach to preparing for, responding to, and recovering from fire on its landscapes. Using a comparative study between seven provinces and four national agencies, this dissertation examines differences in institutional design and policy with respect to the knowledge management systems required to respond to wildfire: How do policies and procedures vary between jurisdictions, how do they affect the practices of each fire management agency, and how can they be improved through a critical analysis of the knowledge management systems in use? And, what is the role of and limits on expertise within these fire management institutions that manage high-risk, highly uncertain socio- environmental challenges? I begin by introducing the 2016 Fort McMurray/Horse River fire as a lens for exploring these questions. I then use the past one hundred years of fire history in Canada to illustrate the continual presence of fire, its human and social dimensions, and the evolution of differing fire management regimes. Drawing on extended ethnographic observation and interviewing of fire managers across Canada, I examine the varied provincial systems of response through following an active fire day in Alberta. I analyze the decision support and geospatial information systems used to guide fire agency decision-making, as well as the factors that limit their effectiveness in both response and hazard reduction modes. I begin Part Two with a discussion of mutual aid arrangements between the provinces, and critically examine the core strategy – interagency fungibility – used to allow this exchange. I analyze forecasting and predictive models used in firefighting, with an emphasis on comparing advantages and disadvantages of attempts at predicting future firefighter capacity requirements. I review organizational learning approaches, considering both fire research strategies and after action reviews. Finally, I consider the implication of changes in climates, politics, and public behaviours and their impacts on fire management.Dissertation/ThesisDoctoral Dissertation Human and Social Dimensions of Science and Technology 201

Interactions Among Climate, Fire, And Vegetation In The Alaskan Boreal Forest

Thesis (Ph.D.) University of Alaska Fairbanks, 2006The boreal forest covers 12 million kM2 of the northern hemisphere and contains roughly 40% of the world's reactive soil carbon. The Northern high latitudes have experienced significant warming over the past century and there is a pressing need to characterize the response of the disturbance regime in the boreal forest to climatic change. The interior Alaskan boreal forest contains approximately 60 million burnable hectares and, relative to the other disturbance mechanisms that exist in Alaska, fire dominates at the landscape-scale. In order to assess the impact of forecast climate change on the structure and function of the Alaskan boreal forest, the interactions among climate, fire and vegetation need to be quantified. The results of this work demonstrate that monthly weather and teleconnection indices explain the majority of observed variability in annual area burned in Alaska from 1950-2003. Human impacts and fire-vegetation interactions likely account for a significant portion of the remaining variability. Analysis of stand age distributions indicate that anthropogenic disturbance in the early 1900's has left a distinct, yet localized impact. Additionally, we analyzed remotely sensed burn severity data to better understand interactions among fire, vegetation and topography. These results show a significant relationship between burn severity and vegetation type in flat landscapes but not in topographically complex landscapes, and collectively strengthen the argument that differential flammability of vegetation plays a significant role in fire-vegetation interactions. These results were used to calibrate a cellular automata model based on the current conceptual model of interactions among weather, fire and vegetation. The model generates spatially explicit maps of simulated stand ages at 1 km resolution across interior Alaska, and output was validated using observed stand age distributions. Analysis of simulation output suggests that significant temporal variability of both the mean and variance of the stand age distribution is an intrinsic property of the stand age distributions of the Alaskan boreal forest. As a consequence of this non-stationarity, we recommend that simulation based methods be used to analyze the impact of forecast climatic change on the structure and function of the Alaskan boreal forest. To assess the impact climate change has on the Alaskan boreal forest, interactions among climate, fire and vegetation were quantified. This work shows that climatic signals exert the dominant influence on area burned. These results inform a simulation model to assess the historical and future states of the Alaskan boreal forest

Review and new methodological approaches in human-caused wildfire modeling and ecological vulnerability: Risk modeling at mainland Spain

En las últimas décadas, las autoridades en materia de incendios han fomentado la investigación acerca de los factores desencadenantes del fuego, parámetro decisivo para lograr un entendimiento mayor de los patrones de la ocurrencia de incendios y mejorar las medidas preventivas. Existe por tanto una necesidad de mejorar y actualizar los enfoques metodológicos para el modelado de incendios forestales, teniendo en cuenta no sólo algoritmos innovadores, sino también la mejora y/o superación de los métodos clásicos de regresión. Por otra parte, es también imprescindible fomentar la evaluación de los posibles daños potenciales en los ecosistemas naturales, promoviendo así la conservación de los servicios de valor económico, ambiental, cultural y estético que éstos proporcionan a la sociedad. El objetivo principal de esta tesis doctoral es explorar nuevos métodos para el modelado de la causalidad humana en incendios forestales así como de los efectos adversos sobre las comunidades vegetales potencialmente afectadas. El modelado de la causalidad humana se ha realizado a partir de métodos de aprendizaje artificial y de técnicas de regresión geográficamente ponderada. Estas técnicas permiten por una parte el ajuste de modelos de probabilidad de ocurrencia espacialmente explícitos y, por otra, el estudio de la variabilidad espacial de los factores explicativos. La estimación de la vulnerabilidad de la vegetación frente al fuego, se ha llevado a cabo utilizando un enfoque cuantitativo, que permita superar los métodos existentes, que, si bien pueden ser útiles en algunas áreas de la gestión del territorio, son inadecuados para otros tipos de análisis, tales como la estimación de las pérdidas económicas inducidas por el fuego como consecuencia de la interrupción de los servicios ambientales (por ejemplo, la madera, la caza, y la recolección de setas). Para abordar el análisis de la vulnerabilidad se propone un método basado en la estimación del tiempo de recuperación de las comunidades vegetales tras el fuego, desarrollado mediante álgebra de mapas en entorno SIG. Los resultados indican que la utilización de métodos de aprendizaje artificial (concretamente el algoritmo Random Forest) supone una mejora sustancial respecto a los métodos clásicos de regresión, si bien parece que existe cierta incertidumbre en los modelos desarrollados, relacionada principalmente con la calidad de los datos de ocurrencia. Además, la aplicación de modelos GWR ha revelado la existencia de una elevada heterogeneidad espacial en la relación y capacidad explicativa de los factores relacionados con la ocurrencia de incendios con origen antrópico. Por otra parte, la aplicación del modelo propuesto para la estimación cuantitativa de la vulnerabilidad ecológica sugiere que la capacidad de respuesta de la vegetación se encuentra estrechamente relacionada con la estrategia reproductiva de las especies afectadas.Over the last decades, authorities responsible on forest fire have encouraged research on fire triggering factors, recognizing this as a critical point to achieve a greater understanding of fire occurrence patterns and improve preventive measures. There is therefore a need to improve and update the methodological approaches for modeling forest fires, taking into account not only innovative algorithms, but also improving and/or overcoming classical regression methods. On the other hand it is also essential to encourage the assessment of potential damage on natural ecosystems, promoting the conservation of its economic, environmental, cultural and aesthetic assets they provide to society. The main objective of this PhD thesis is to explore new methods for modeling human causality in forest fires and adverse effects on the plant communities potentially affected. Human causality modeling was carried out from machine learning methods and geographically weighted regression techniques. These procedures allow the adjustment spatially explicit probability models of occurrence and, secondly, the study of the spatial variability of wildfire explanatory factors. The estimation of the vulnerability of vegetation to fire was carried out using a quantitative approach to overcome current methods, which, while they may be useful in some areas of land management, are inadequate for other types of analysis, such as estimating economic losses induced by interrupting ecosystem services (e.g., wood, hunting, and gathering mushrooms). To address the vulnerability a method based on evaluating the recovery time of plant communities after the fire using a GIS map algebra approach is proposed. The results suggest that the use of machine learning methods (specifically the Random Forest algorithm) represents a substantial improvement over traditional methods of regression, although it appears that there is some uncertainty in the models, primarily related to the quality of ignition. Furthermore, the application of GWR models has revealed the existence of a high spatial heterogeneity in the relationship and explanatory power of the factors related to the occurrence of anthropogenic fires. Moreover, the application of the proposed model for the quantitative estimation of ecological vulnerability suggests that the responsiveness of vegetation is closely related to the reproductive strategy of the fire-affected species

Earth resources: A continuing bibliography with indexes (issue 47)

This bibliography lists 524 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Terrestrial Environment (Climatic) Criteria Guidelines for use in Aerospace Vehicle Development

This document provides guidelines for the terrestrial environment that are specifically applicable in the development of design requirements/specifications for NASA aerospace vehicles, payloads, and associated ground support equipment. The primary geographic areas encompassed are the John F. Kennedy Space Center, FL; Vandenberg AFB, CA; Edwards AFB, CA; Michoud Assembly Facility, New Orleans, LA; John C. Stennis Space Center, MS; Lyndon B. Johnson Space Center, Houston, TX; George C. Marshall Space Flight Center, Huntsville, AL; and the White Sands Missile Range, NM. This document presents the latest available information on the terrestrial environment applicable to the design and operations of aerospace vehicles and supersedes information presented in NASA-HDBK-1001 and TM X-64589, TM X-64757, TM-78118, TM-82473, and TM-4511. Information is included on winds, atmospheric thermodynamic models, radiation, humidity, precipitation, severe weather, sea state, lightning, atmospheric chemistry, seismic criteria, and a model to predict atmospheric dispersion of aerospace engine exhaust cloud rise and growth. In addition, a section has been included to provide information on the general distribution of natural environmental extremes in the conterminous United States, and world-wide, that may be needed to specify design criteria in the transportation of space vehicle subsystems and components. A section on atmospheric attenuation has been added since measurements by sensors on certain Earth orbital experiment missions are influenced by the Earth s atmosphere. There is also a section on mission analysis, prelaunch monitoring, and flight evaluation as related to the terrestrial environment inputs. The information in these guidelines is recommended for use in the development of aerospace vehicle and related equipment design and associated operational criteria, unless otherwise stated in contract work specifications. The terrestrial environmental data in these guidelines are primarily limited to information below 90 km altitude

Watershed Management on Range and Forest Lands Proceedings of the Fifth Workshop of the United States/Australia Rangelands Panel

Preface: The U.S.-Australia Cooperative Rangeland Science Program In October 1968 the governments of the United States and Australia entered into an agreement for the purpose of facilitating close cooperative activities between the scientific communities of the two countries. The joint communique issued at that time designated the U.S. National Science Foundation and the Australian Commonwealth Department of Education and Science as the coordinating agencies. Both countries were to encourage binational teamwork in research, interchanges of scientists, joint seminars, and exchanges of information. A United States-Australia Rangeland Panel was established in December 1969 to further cooperation between the two countries in the rangeland sciences. The present panel includes the following