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Remote Sensing
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Remote Sensing Applications for Enhancing Wildfire Management and Ecosystem Multifunctionality
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Remote Sensing Applications for Enhancing Wildfire Management and Ecosystem Multifunctionality

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 4455

Special Issue Editors

Dr. Paula García-Llamas

E-Mail Website
Guest Editor
Department of Didactics, Specifics and Theory of Education, Faculty of Education, University of León, 24007 León, Spain
Interests: fire ecology; landscape ecology; ecosystem services; land use/land cover; remote sensing; land dynamics
Dr. Angela Taboada

E-Mail Website
Guest Editor
1. Department of Biodiversity and Environmental Management, Area of Ecology, Faculty of Biology and Environmental Sciences, University or León, 24007 León, Spain
2. Institute of Environmental Research (IMA), University of León, 24007 León, Spain
Interests: global change; wildfires; ecosystem services; applied ecology; ecosystem functioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfires represent a critical challenge for ecosystem multifunctionality worldwide, exerting profound impacts on biodiversity, ecosystem services, and human livelihoods. In recent years, the frequency and intensity of wildfires have escalated, exacerbated by factors such as climate change, land-use practices, and human activities. Amidst these challenges, the integration of remote sensing technologies and wildfire management has emerged as a promising approach to enhance preparedness, response, and recovery efforts.

By leveraging data from satellites, aircraft, and ground-based sensors, remote sensing offers an unparalleled ability to provide critical insights into fires’ behavior, extent, and severity, empowering stakeholders with accurate information for effective pre- and post-wildfire management; mitigating risks; and protecting vulnerable ecosystems, their functions, and services.

This Special Issue calls for manuscripts addressing new applications and developments in remote sensing for a deeper understanding of the interactions between wildfires, ecosystems, and society and to identify actionable strategies for enhancing wildfire management and ecosystem resilience and multifunctionality. The specific topics of interests include, but are not limited to, the following:

  • Remote sensing-based decision support systems for wildfire management.
  • Fuel structure and composition.
  • Fire and biotic and abiotic interactions at the landscape scale.
  • Fire-induced changes in ecosystem functioning and services.
  • Machine and deep learning approaches for wildfire remote sensing.
  • Wildfire classification.
  • Fire risk assessment.
  • Remote sensing analyses for post-fire assessment and recovery.
  • Fire emissions and their effects on health and climate.

Dr. Paula García-Llamas
Dr. Angela Taboada
Guest Editors

Esther Peña Molina
Guest Editor Assistant

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

  • wildfire
  • fire management
  • remote sensing
  • ecosystem functioning
  • fire products
  • fire risk
  • ecosystem services

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

Published Papers (3 papers)

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Research

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24 pages, 10570 KiB  
Article
Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine
by Yue Chen, Weili Kou, Wenna Miao, Xiong Yin, Jiayue Gao and Weiyu Zhuang
Remote Sens. 2025, 17(5), 741; https://doi.org/10.3390/rs17050741 - 20 Feb 2025
Viewed by 586
Abstract
This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A [...] Read more.
This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A machine learning workflow was developed on Google Earth Engine by combining Dynamic World land cover data with official fire records, utilizing a logistic regression-based feature selection strategy and an enhanced SNIC segmentation GEOBIA framework. The performance of four classifiers (RF, SVM, KNN, CART) in burn detection was evaluated through a comparative analysis of their spectral–spatial discrimination capabilities. The results indicated that the RF classifier achieved the highest performance, with an overall accuracy of 96.32% and a Kappa coefficient of 0.951. Spatial analysis further revealed that regions at medium altitudes (800–1600 m) and moderate slopes (15–25°) are more prone to forest fires. This study demonstrates a robust approach for generating accurate large-scale forest fire maps and provides valuable insights for effective fire management in complex terrain areas. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Enhancing Wildfire Management and Ecosystem Multifunctionality)
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21 pages, 5043 KiB  
Article
Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories
by Annalise Taylor, Iryna Dronova, Alexii Sigona and Maggi Kelly
Remote Sens. 2024, 16(12), 2232; https://doi.org/10.3390/rs16122232 - 19 Jun 2024
Viewed by 1307
Abstract
As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity [...] Read more.
As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Enhancing Wildfire Management and Ecosystem Multifunctionality)
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Review

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26 pages, 888 KiB  
Review
Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review
by Arnick Abdollahi and Marta Yebra
Remote Sens. 2025, 17(3), 415; https://doi.org/10.3390/rs17030415 - 25 Jan 2025
Cited by 1 | Viewed by 1905
Abstract
Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform [...] Read more.
Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Enhancing Wildfire Management and Ecosystem Multifunctionality)
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