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Progress in Estimating, Monitoring, and Modelling Wildfire Fuel Loads Using...
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Progress in Estimating, Monitoring, and Modelling Wildfire Fuel Loads Using Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2905

Special Issue Editors

Dr. Nicolas Younes

E-Mail Website
Guest Editor
1. Fenner School of Environment & Society, The Australian National University, Canberra, ACT 2601, Australia
2. Center for Tropical Environmental and Sustainability Science, James Cook University, Smithfield, QLD 4878, Australia
Interests: remote sensing; bushfire hazard analysis; forest fuel load monitoring; imaging spectroscopy; geocomputation; calibration and validation of satellite imagery; vegetation phenology
Dr. Paulo Jose Murillo-Sandoval

E-Mail Website
Guest Editor
Faculdad de Tecnologías, Universidad del Tolima, Ibagué 73000 6299, Colombia
Interests: remote sensing; land cover monitoring; impacts armed conflict on landscape change
Dr. Li Zhao

E-Mail Website
Guest Editor
Fenner School of Environment & Society, The Australian National University, Canberra, ACT, 2600, Australia
Interests: fuel moisture monitoring; Soil–vegetation–atmosphere transfer modeling
Dr. Colleen Bryant

E-Mail Website
Guest Editor
Fenner School of Environment & Society, The Australian National University, ACT, Australia Bushfire and Natural Hazards Cooperative Research Centre, Melbourne, VIC, Australia
Interests: natural hazards; lightning ignitions; lightning tracking; fires

Special Issue Information

Dear Colleagues,

From the Arctic Circle to tropical rainforests, wildfires destroy millions of hectares of forests, grasslands, and bushlands in the northern and southern hemispheres. In Australia alone, more than 30 million hectares were burnt in what is now known as the ‘Black Summer’. Fuel loads are key drivers of fire intensity, spread, and behaviour; therefore, management activities benefit from accurate and timely fuel load estimations for hazard reduction burns, asset management, and for resource allocation during wildfires. In Africa and some countries in South America, fire is commonly used as a method for clearing land, mostly in non-protected areas, for agricultural expansion.

This Special Issue aims at compiling the latest advances in the estimation, monitoring, and modelling of fuel loads in diverse environments. Topics of interest for this Special Issue may include the following:

  • The characterization and modelling of fuel biophysical traits and attributes;
  • Fuel moisture content (FMC): detection, estimation, and modelling;
  • Live/dead fuel estimation and modelling;
  • Usage of active and passive remote sensing (e.g., optical, radar, LiDAR) for estimating and modelling fuel loads (live or dead);
  • Radiative transfer modelling applied to fuel load estimation and modelling.

We invite contributions from field, laboratory, computational, and remote sensing perspectives that enhance our understanding of fuel loads in diverse ecosystems.

Dr. Nicolas Younes
Dr. Paulo Jose Murillo-Sandoval
Dr. Li Zhao
Dr. Colleen Bryant
Guest Editors

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • fuel loads
  • bushfires
  • wildfires
  • fuel flammability
  • fuel moisture content (FMC)
  • live/dead fuel biochemistry
  • hyperspectral imagery
  • fire monitoring

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

Published Papers (2 papers)

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Research

17 pages, 6224 KB  
Article
Assessing Umbellularia californica Basal Resprouting Response Post-Wildfire Using Field Measurements and Ground-Based LiDAR Scanning
by Dawson Bell, Michelle Halbur, Francisco Elias, Nancy Pearson, Daniel E. Crocker and Lisa Patrick Bentley
Remote Sens. 2025, 17(17), 3101; https://doi.org/10.3390/rs17173101 - 5 Sep 2025
Viewed by 695
Abstract
In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are [...] Read more.
In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article
(This article belongs to the Special Issue Progress in Estimating, Monitoring, and Modelling Wildfire Fuel Loads Using Remote Sensing)
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13 pages, 7776 KB  
Communication
Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach
by Filippe L. M. Santos, Gonçalo Rodrigues, Miguel Potes, Flavio T. Couto, Maria João Costa, Susana Dias, Maria José Monteiro, Nuno de Almeida Ribeiro and Rui Salgado
Remote Sens. 2024, 16(23), 4434; https://doi.org/10.3390/rs16234434 - 27 Nov 2024
Cited by 2 | Viewed by 1236
Abstract
Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can [...] Read more.
Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R2 = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R2 = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. Full article
(This article belongs to the Special Issue Progress in Estimating, Monitoring, and Modelling Wildfire Fuel Loads Using Remote Sensing)
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