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The Future of Wildfires: Latest Geo-Technologies Applied to Fires and a New Perspective of Forest Resilience

The aim of this article collection is to contribute to the study of the future of wildfires, using update geo-technologies (including, but not limited to, Remote Sensing, LiDAR, UAVs, IA) and rethinking resilience to fire. This topic encourages interdisciplinary studies to provide ideas for developing ways in which we can improve the way we live with wildfires, from a multidisciplinary approach.

Wildfires have severe consequences for human health and wellbeing, biodiversity, and economies around the world. Understanding of forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, severity and resilience (capacity of an ecosystem to recuperate to its pre-disturbance situation). Geo-technologies may be seen as a tool to study forest cycle management (risk, suppression, damage and regeneration analysis). While more importantly, the use of methodologies based on geo-technologies can be of great help in allowing researchers to redefine wildfire resilience.

This topic entitled “The Future of Wildfires: Latest Geo-Technologies Applied to Fires and a New Perspective of Forest Resilience” is focused on advances in geo-technologies such as new remote sensing technologies, new sensors, big data collections, and processing methodologies, applied to fire cycle analysis, fire resilience redefinition, and to guide management of fire-prone ecosystems affected by large fires. We welcome submissions from Forests, Remote Sensing, Fire and Geomatics journals that cover, but are not limited to:

  • Innovative geo-technology for wildfire suppression, operational planning, or research methods;
  • Rethinking wildfire resilience and adaptation to global change vulnerability (impact on forest fire severity and consequences for ecosystem recovery);
  • Incorporation of wildfire processes within the earth-system, socio-economic and landscape fields;
  • Monitoring fire risk/danger using new geospatial data and technologies;
  • Fire effects on vegetation, soils, and hydrology using geo-technologies;
  • Improved methods of modelling image time-series of fire disturbance recovery;
  • Impact of fires and environmental consequences of large fires from remote sensing, big data, unmanned aerial vehicles (UAV), LiDAR, radar, and machine learning algorithms;
  • Time series analysis for forest resilience study.

Deadline for abstract submissions: 31 October 2021.
Deadline for manuscript submissions: 31 March 2022.

Topic Board

Dr. Alfonso Fernández-Manso
E-Mail Website
Topic Editor-in-Chief
Applied Ecology and Remote Sensing Group, Agrarian Science and Engineering Department, University of León, Av. Astorga s/n. 24400 Ponferrada, Spain
Interests: forestry; wildfire; forest management; remote sensing; LiDAR
Special Issues and Collections in MDPI journals
Dr. Carmen Quintano
E-Mail Website
Topic Editor-in-Chief
1. Electronic Technology Department, School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain
2. Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology (INIA), 34004 Palencia, Spain
Interests: fire damage (burned area, burn severity); multi and hyper-spectral remote sensing; unmixing; classification
Special Issues and Collections in MDPI journals

Keywords

fire; LiDAR; machine learning algorithms; remote sensing; geomatics; forest resilience; fire risk; geo-technologies

Relevant Journals List

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Forests
forests
2.633 3.3 2010 17.79 Days 1800 CHF Submit
Geomatics
geomatics
- - 2021 17.72 Days 1000 CHF Submit
Remote Sensing
remotesensing
4.848 6.6 2009 16.06 Days 2400 CHF Submit
Fire
fire
- 3.7 2018 13.54 Days 1400 CHF Submit

Published Papers (3 papers)

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Article
Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China
Forests 2021, 12(10), 1299; https://0-doi-org.brum.beds.ac.uk/10.3390/f12101299 - 23 Sep 2021
Abstract
Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as [...] Read more.
Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%. Full article
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Article
A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation
Forests 2021, 12(7), 933; https://0-doi-org.brum.beds.ac.uk/10.3390/f12070933 - 16 Jul 2021
Abstract
Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to [...] Read more.
Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to build relationships between the input drivers (weather, fuel and site characteristics) and observed DFMC. The latter attempts to simulate the processes that occur in the fuel with energy and water balance conservation equations. However, empirical models lack explanations for physical processes, and process-based models may provide an incomplete representation of DFMC. To combine the benefits of empirical and process-based models, here we introduced the Long Short-Term Memory (LSTM) network and its combination with an effective physics process-based model fuel stick moisture model (FSMM) to estimate DFMC. The LSTM network showed its powerful ability in describing the temporal dynamic changes of DFMC with high R2 (0.91), low RMSE (3.24%) and MAE (1.97%). When combined with a FSMM model, the physics-guided model FSMM-LSTM showed betterperformance (R2 = 0.96, RMSE = 2.21% and MAE = 1.41%) compared with the other models. Therefore, the combination of the physics process and deep learning estimated 10-h DFMC more accurately, allowing the improvement of wildfire risk assessments and fire simulating. Full article
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Article
Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data
Remote Sens. 2021, 13(6), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061189 - 20 Mar 2021
Abstract
Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually [...] Read more.
Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation. Full article
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