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Fire
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Data and Models from Integrated Socio-Environmental Fire Science Projects
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Data and Models from Integrated Socio-Environmental Fire Science Projects

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Science Models, Remote Sensing, and Data".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 22093

Special Issue Editor

Prof. Dr. Alistair M. S. Smith

E-Mail Website
Guest Editor
Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID 83843, USA
Interests: wildfire; remote sensing; vegetation mortality and productivity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks to highlight data collected and models developed as part of large integrated socio-environmental fire science projects. Many such projects have been funded by national science agencies and often seek to integrate social, biophysical, and natural sciences. Projects can include those that focus on fire hydrological processes, land-atmosphere interactions, scientific visualizations, individual and community social science problems, climatology and weather, among many other topics. Many of these projects also include interactions with the public and a wide array of stakeholders. These projects usually involve multiple institutions and multiple scientific disciplines.

Datasets can include, but are not limited to, field data, laboratory data, model simulations, scientific visualization descriptions and virtual asset development, social science datasets, remote sensing datasets, educational datasets, etc.

The scope of this Special Issue includes articles, data descriptors (data papers), technical notes data syntheses, model development, and model applications associated with any integrated socio-environmental fire science projects. Data descriptors should include links to supplemental tables and figures containing the data being described. Perspectives providing a brief description of project goals, outcomes, and further work are encouraged. Synthesis and review papers that provide an overview of field campaigns and integrated projects are also welcome.

Prof. Dr. Alistair M. S. Smith
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • integration
  • interdisciplinary
  • convergence
  • visualizations
  • modeling
  • remote sensing
  • field
  • laboratories

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

29 pages, 6165 KiB  
Article
Exploring and Testing Wildfire Risk Decision-Making in the Face of Deep Uncertainty
by Bart R. Johnson, Alan A. Ager, Cody R. Evers, David W. Hulse, Max Nielsen-Pincus, Timothy J. Sheehan and John P. Bolte
Fire 2023, 6(7), 276; https://doi.org/10.3390/fire6070276 - 18 Jul 2023
Cited by 3 | Viewed by 2310
Abstract
We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire. Our goal was to develop an adaptable simulation platform for anticipating risk-mitigation tradeoffs in [...] Read more.
We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire. Our goal was to develop an adaptable simulation platform for anticipating risk-mitigation tradeoffs in a fire-prone wildland–urban interface (WUI) facing conditions outside the bounds of experience. We describe how five social and ecological system (SES) submodels interact over time and space to generate highly variable alternative futures even within the same scenario as stochastic elements in simulated wildfire, succession, and landowner decisions create large sets of unique, path-dependent futures for analysis. We applied the modeling system to an 815 km2 study area in western Oregon at a sub-taxlot parcel grain and annual timestep, generating hundreds of alternative futures for 2007–2056 (50 years) to explore how WUI communities facing compound risks from increasing wildfire and expanding periurban development can situate and assess alternative risk management approaches in their localized SES context. The ability to link trends and uncertainties across many futures to processes and events that unfold in individual futures is central to the modeling system. By contrasting selected alternative futures, we illustrate how assessing simulated feedbacks between wildfire and other SES processes can identify tradeoffs and leverage points in fire-prone WUI landscapes. Assessments include a detailed “post-mortem” of a rare, extreme wildfire event, and uncovered, unexpected stabilizing feedbacks from treatment costs that reduced the effectiveness of agent responses to signs of increasing risk. Full article
(This article belongs to the Special Issue Data and Models from Integrated Socio-Environmental Fire Science Projects)
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19 pages, 3808 KiB  
Article
Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence
by Tichaona Mukunga, Matthias Forkel, Matthew Forrest, Ruxandra-Maria Zotta, Nirlipta Pande, Stefan Schlaffer and Wouter Dorigo
Fire 2023, 6(5), 197; https://doi.org/10.3390/fire6050197 - 10 May 2023
Cited by 4 | Viewed by 2294
Abstract
Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics [...] Read more.
Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics and the Earth’s system. However, there is no consensus on how human-caused fire ignitions should be represented in such models. This study aimed to identify which globally available predictors of human activity explain global fire ignitions as observed by satellites. We applied a random forest machine learning framework to state-of-the-art global climate, vegetation, and land cover datasets to establish a baseline against which influences of socioeconomic data (cropland fraction, gross domestic product (GDP), road density, livestock density, grazed lands) on fire ignition occurrence were evaluated. Our results showed that a baseline random forest without human predictors captured the spatial patterns of fire ignitions globally, with hotspots over Sub-Saharan Africa and South East Asia. Adding single human predictors to the baseline model revealed that human variables vary in their effects on fire ignitions and that of the variables considered GDP is the most vital driver of fire ignitions. A combined model with all human predictors showed that the human variables improve the ignition predictions in most regions of the world, with some regions exhibiting worse predictions than the baseline model. We concluded that an ensemble of human predictors can add value to physical and empirical models. There are complex relationships between the variables, as evidenced by the improvement in bias in the combined model compared to the individual models. Furthermore, the variables tested have complex relationships that random forests may struggle to disentangle. Further work is required to detangle the complex regional relationships between these variables. These variables, e.g., population density, are well documented to have substantial effects on fire at local and regional scales; we determined that these variables may provide more insight at more continental scales. Full article
(This article belongs to the Special Issue Data and Models from Integrated Socio-Environmental Fire Science Projects)
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23 pages, 9368 KiB  
Article
Using Structure Location Data to Map the Wildland–Urban Interface in Montana, USA
by Alexander R. Ketchpaw, Dapeng Li, Shahid Nawaz Khan, Yuhan Jiang, Yingru Li and Ling Zhang
Fire 2022, 5(5), 129; https://doi.org/10.3390/fire5050129 - 29 Aug 2022
Cited by 2 | Viewed by 2700
Abstract
The increasing wildfire activity and rapid population growth in the wildland–urban interface (WUI) have made more Americans exposed to wildfire risk. WUI mapping plays a significant role in wildfire management. This study used the Microsoft building footprint (MBF) and the Montana address/structure framework [...] Read more.
The increasing wildfire activity and rapid population growth in the wildland–urban interface (WUI) have made more Americans exposed to wildfire risk. WUI mapping plays a significant role in wildfire management. This study used the Microsoft building footprint (MBF) and the Montana address/structure framework datasets to map the WUI in Montana. A systematic comparison of the following three types of WUI was performed: the WUI maps derived from the Montana address/structure framework dataset (WUI-P), the WUI maps derived from the MBF dataset (WUI-S), and the Radeloff WUI map derived from census data (WUI-Z). The results show that WUI-S and WUI-P are greater than WUI-Z in the WUI area. Moreover, WUI-S has more WUI area than WUI-P due to the inclusion of all structures rather than just address points. Spatial analysis revealed clusters of high percentage WUI area in western Montana and low percentage WUI area in eastern Montana, which is likely related to a combination of factors including topography and population density. A web GIS application was also developed to facilitate the dissemination of the resulting WUI maps and allow visual comparison between the three WUI types. This study demonstrated that the MBF can be a useful resource for mapping the WUI and could be used in place of a national address point dataset. Full article
(This article belongs to the Special Issue Data and Models from Integrated Socio-Environmental Fire Science Projects)
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44 pages, 8983 KiB  
Article
The Construction of Probabilistic Wildfire Risk Estimates for Individual Real Estate Parcels for the Contiguous United States
by Edward J. Kearns, David Saah, Carrie R. Levine, Chris Lautenberger, Owen M. Doherty, Jeremy R. Porter, Michael Amodeo, Carl Rudeen, Kyle D. Woodward, Gary W. Johnson, Kel Markert, Evelyn Shu, Neil Freeman, Mark Bauer, Kelvin Lai, Ho Hsieh, Bradley Wilson, Beth McClenny, Andrea McMahon and Farrukh Chishtie
Fire 2022, 5(4), 117; https://doi.org/10.3390/fire5040117 - 15 Aug 2022
Cited by 8 | Viewed by 8875
Abstract
The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented. The FSF-WFM integrates several existing methods from the wildfire science [...] Read more.
The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented. The FSF-WFM integrates several existing methods from the wildfire science community and implements computationally efficient and scalable modeling techniques to allow for new high-resolution, CONUS-wide hazard generation. Burn probability, flame length, and ember spread for the years 2022 and 2052 are computed from two ten-year representative Monte Carlo simulations of wildfire behavior, utilizing augmented LANDFIRE fuel estimates updated with all the available disturbance information. FSF-WFM utilizes ELMFIRE, an open-source, Rothermel-based wildfire behavior model, and multiple US Federal Government open data sources to drive the simulations. LANDFIRE non-burnable fuel classes within the wildland–urban interface (WUI) are replaced with fuel estimates from machine-learning models, trained on data from historical fires, to allow the propagation of wildfire through the WUI in the model. Historical wildfire ignition locations and NOAA’s hourly time series of surface weather at 2.5 km resolution are used to drive ELMFIRE to produce wildfire hazards representative of the 2022 and 2052 conditions at 30 m resolution, with the future weather conditions scaled to the IPCC CMIP5 RCP4.5 model ensemble predictions. Winds and vegetation were held constant between the 2022 and 2052 simulations, and climate change’s impacts on the future fuel conditions are the main contributors to the changes observed in the 2052 results. Non-zero wildfire exposure is estimated for 71.8 million out of 140 million properties across CONUS. Climate change impacts add another 11% properties to this non-zero exposure class over the next 30 years, with much of this change observed in the forested areas east of the Mississippi River. “Major” aggregate wildfire exposure of greater than 6% over the 30-year analysis period from 2022 to 2052 is estimated for 10.2 million properties. The FSF-WFM represents a notable contribution to the ability to produce property-specific, climate-adjusted wildfire risk assessments in the US. Full article
(This article belongs to the Special Issue Data and Models from Integrated Socio-Environmental Fire Science Projects)
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22 pages, 3900 KiB  
Article
Human Fire Use and Management: A Global Database of Anthropogenic Fire Impacts for Modelling
by James D. A. Millington, Oliver Perkins and Cathy Smith
Fire 2022, 5(4), 87; https://doi.org/10.3390/fire5040087 - 23 Jun 2022
Cited by 6 | Viewed by 4605
Abstract
Human use and management of fire in landscapes have a long history and vary globally in purpose and impact. Existing local research on how people use and manage fire is fragmented across multiple disciplines and is diverse in methods of data collection and [...] Read more.
Human use and management of fire in landscapes have a long history and vary globally in purpose and impact. Existing local research on how people use and manage fire is fragmented across multiple disciplines and is diverse in methods of data collection and analysis. If progress is to be made on systematic understanding of human fire use and management globally, so that it might be better represented in dynamic global vegetation models, for example, we need improved synthesis of existing local research and literature. The database of anthropogenic fire impacts (DAFI) presented here is a response to this challenge. We use a conceptual framework that accounts for categorical differences in the land system and socio-economic context of human fire to structure a meta-study for developing the database. From the data collated, we find that our defined anthropogenic fire regimes have distinct quantitative signatures and identify seven main modes of fire use that account for 93% of fire instance records. We describe the underlying rationales of these seven modes of fire use, map their spatial distribution and summarise their quantitative characteristics, providing a new understanding that could become the basis of improved representation of anthropogenic fire in global process-based models. Our analysis highlights the generally small size of human fires (60% of DAFI records for mean size of deliberately started fires are <21 ha) and the need for continuing improvements in methods for observing small fires via remote sensing. Future efforts to model anthropogenic fire should avoid assuming that drivers are uniform globally and will be assisted by aligning remotely sensed data with field-based data and process understanding of human fire use and management. Full article
(This article belongs to the Special Issue Data and Models from Integrated Socio-Environmental Fire Science Projects)
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