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Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 6181

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Special Issue Editors

Dr. Xikun Hu

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Guest Editor

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: forest fires; wildfire assessment; target detection; remote sensing; deep learning

Dr. Ioannis Gitas

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Guest Editor

Lab of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: forest fires; land-use/land-cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
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Dr. Puzhao Zhang

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Guest Editor

DHI, Agern Alle 5, 2970 Hørsholm, Denmark
Interests: environmental change monitoring; machine learning/deep learning; geospatial artificial intelligence
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Special Issue Information

Dear Colleagues,

Remote sensing, utilizing sensors on satellites, aircraft or UAVs, is crucial for wildfire risk assessment, monitoring and recovery. It enables rapid active fire detection, timely mapping and tracking of burned areas and environmental impact assessment at various scales. Recent advancements in remote sensing technology have further enhanced its capabilities in the field of fire risk assessment, monitoring and recovery. Multi-source sensors, such as optical, thermal, radar and LiDAR, have made it possible to map forest structure, fuel type and arrangement, fire characteristics, etc. Additionally, artificial intelligence algorithms can learn high-level features from multi-source data and significantly improve the accuracy and efficiency of wildfire detection, mapping and recovery modelling.

We welcome research contributions that explore novel methods in remote sensing for risk assessment, monitoring and recovery of fires. We look forward to receiving journal manuscripts that focus on leveraging remote sensing data and artificial intelligence technologies to help analyze the impact of wildfires and advance the state-of-the-art research in this critical field. Topics of interest include, but are not limited to, the following:

Utilizing remote sensing data for early fire detection and risk assessment;
Monitoring fire spread and behavior using satellites, aircraft or UAVs;
Assessing post-fire damage and recovery efforts through remote sensing techniques;
Integrating remote sensing with other technologies for comprehensive fire management;
Developing new algorithms and methods for processing and analyzing remote sensing data related to fires.

Dr. Xikun Hu
Dr. Ioannis Gitas
Dr. Puzhao Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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

remote sensing
geoscience and earth observation
artificial intelligence
machine learning
forest fires
post-fire assessment
visualization and mapping

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Published Papers (5 papers)

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Research

26 pages, 8977 KB

Open AccessArticle

Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California

by Andrew Alamillo, Jingjing Li, Alireza Farahmand, Madeleine Pascolini-Campbell and Christine Lee

Remote Sens. 2025, 17(24), 4023; https://doi.org/10.3390/rs17244023 - 13 Dec 2025

Viewed by 179

Abstract

Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)

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22 pages, 159873 KB

Open AccessArticle

Advancing Wildfire Damage Assessment with Aerial Thermal Remote Sensing and AI: Applications to the 2025 Eaton and Palisades Fires

by Siddharth Trivedi, Rawaf al Rawaf, Francesca Hart, Jessica Block, Mai H. Nguyen, Daniel Roten, Daniel Crawl, Robert Scott, Michael Martin, Chris Pahalek, Erik Rodriguez and Ilkay Altintas

Remote Sens. 2025, 17(24), 3962; https://doi.org/10.3390/rs17243962 - 8 Dec 2025

Viewed by 568

Abstract

Driven by dangerous Santa Ana winds and fueled by dry vegetation, the 2025 Eaton and Palisades wildfires in California caused historic levels of devastation, ultimately becoming the second and third most destructive fires in California history. Burning at the same time and drawing from the same resources, these fires burned a combined total of 16,251 structures. The first several hours of an emerging wildfire are a crucial period for fire officials to assess potential damage and develop a timely and appropriate response. A method to quickly generate accurate estimates of structural damage is essential to providing this crucial rapid response to wildfires. In this paper, we present a machine learning approach for automated assessment of structural damage caused by wildfires. By leveraging multiple data sources in model development (satellite-based building footprints, expert-labeled post-fire damage points, fire perimeters, and aerial thermal imagery) and innovative data processing techniques, the approach can be used to identify various levels of structural damage from just aerial thermal imagery during operational use. The resulting system offers an effective approach for rapid and reliable assessment of burned structures, suitable for operational wildfire damage assessment. Results on the Eaton and Palisades Fires demonstrate the effectiveness of this method and its applicability to real-world scenarios. Full article

(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)

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39 pages, 20818 KB

Open AccessArticle

Effects of Prescribed Fire on Spatial Patterns of Plant Functional Traits and Spectral Diversity Using Hyperspectral Imagery from Savannah Landscapes on the Edwards Plateau of Texas, USA

by Xavier A. Jaime, Jay P. Angerer, Chenghai Yang, Douglas R. Tolleson, Samuel D. Fuhlendorf and X. Ben Wu

Remote Sens. 2025, 17(23), 3873; https://doi.org/10.3390/rs17233873 - 29 Nov 2025

Viewed by 298

Abstract

Vegetation heterogeneity supports biodiversity, while homogeneity limits it. In the Great Plains, fire and herbivory enhance ecosystem function by increasing spatial heterogeneity. However, quantifying their effects on plant functional traits and spectral diversity remains challenging due to landscape complexity and scaling limitations. Hyperspectral remote sensing offers a high-resolution approach to assessing these dynamics, improving the evaluations of post-fire recovery and vegetation function. This study examines the impact of fire on plant functional traits and spectral diversity within a savanna landscape in the Edwards Plateau, Texas, using airborne hyperspectral and multispectral imagery. Specifically, it aims to (1) quantify the spatial patterns of plant functional traits and spectral diversity, (2) assess fire’s effects on these patterns, and (3) evaluate how soil type, woody structure, and burn patterns mediate fire responses. High-resolution airborne images from 2018 (pre-fire) and 2020 (post-fire) were analyzed to classify burned and unburned areas, pre-fire woody cover, and derive spectral indices representing plant functional traits, β-diversity components, and spectral evenness. The results indicate that temporal patterns in spectral diversity were driven primarily by soil properties and weather, with limited evidence that fire altered spectral evenness or β-diversity across soils. In contrast, spectral indices showed clearer—but still soil-dependent—fire effects: declines in canopy structure, greenness, and chlorophyll content were less pronounced in burned areas, indicating that fire partially moderated late-season senescence. Fire had a substantial influence on spatial patterns of spectral evenness (but not β-diversity) and vegetation spectral functional traits, and fire and dry-down increased spatial heterogeneity in spectral evenness and in spectral indices indicative of biophysical and biochemical traits across scales. These findings demonstrate that environmental drivers, particularly soil–moisture interactions and interannual moisture variability, exert a stronger control over post-fire spectral diversity than fire alone. Hyperspectral imaging effectively captured these dynamics, supporting its role in monitoring post-fire vegetation responses. In addition to the use of hyperspectral imaging, fire management strategies should consider broader ecological drivers, including soil and weather interactions, to improve the assessments of ecosystem resilience and recovery. Full article

(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)

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23 pages, 10835 KB

Open AccessArticle

Evaluation of Post-Fire Treatments (Erosion Barriers) on Vegetation Recovery Using RPAS and Sentinel-2 Time-Series Imagery

by Fernando Pérez-Cabello, Carlos Baroja-Saenz, Raquel Montorio and Jorge Angás-Pajas

Remote Sens. 2025, 17(20), 3422; https://doi.org/10.3390/rs17203422 - 13 Oct 2025

Viewed by 754

Abstract

Post-fire soil and vegetation changes can intensify erosion and sediment yield by altering the factors controlling the runoff–infiltration balance. Erosion barriers (EBs) are widely used in hydrological and forest restoration to mitigate erosion, reduce sediment transport, and promote vegetation recovery. However, precise spatial assessments of their effectiveness remain scarce, requiring validation through operational methodologies. This study evaluates the impact of EB on post-fire vegetation recovery at two temporal and spatial scales: (1) Remotely Piloted Aircraft System (RPAS) imagery, acquired at high spatial resolution but limited to a single acquisition date coinciding with the field flight. These data were captured using a MicaSense RedEdge-MX multispectral camera and an RGB optical sensor (SODA), from which NDVI and vegetation height were derived through aerial photogrammetry and digital surface models (DSMs). (2) Sentinel-2 satellite imagery, offering coarser spatial resolution but enabling multi-temporal analysis, through NDVI time series spanning four consecutive years. The study was conducted in the area of the Luna Fire (northern Spain), which burned in July 2015. A paired sampling design compared upstream and downstream areas of burned wood stacks and control sites using NDVI values and vegetation height. Results showed slightly higher NDVI values (0.45) upstream of the EB (p < 0.05), while vegetation height was, on average, ~8 cm lower than in control sites (p > 0.05). Sentinel-2 analysis revealed significant differences in NDVI distributions between treatments (p < 0.05), although mean values were similar (~0.32), both showing positive trends over four years. This study offers indirect insight into the functioning and effectiveness of EB in post-fire recovery. The findings highlight the need for continued monitoring of treated areas to better understand environmental responses over time and to inform more effective land management strategies. Full article

(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)

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23 pages, 6963 KB

Open AccessArticle

Remote-Sensing-Based Prioritization of Post-Fire Restoration Actions in Mediterranean Ecosystems: A Case Study in Cyprus

by Maria Prodromou, Ioannis Gitas, Christodoulos Mettas, Marios Tzouvaras, Kyriacos Themistocleous, Andreas Konstantinidis, Andreas Pamboris and Diofantos Hadjimitsis

Remote Sens. 2025, 17(7), 1269; https://doi.org/10.3390/rs17071269 - 2 Apr 2025

Cited by 8 | Viewed by 3369

Abstract

Global forest degradation and deforestation present urgent environmental challenges demanding efficient strategies for ecological restoration to maximize the impacts and minimize the costs. This study aims to develop a spatial decision support tool to prioritize post-fire restoration actions in Mediterranean ecosystems, with a focus on Cyprus. At the core of this study is the GRESTO Index (GreenHIT-RESTORATION Index), a novel geospatial tool designed to guide reforestation efforts in fire-affected areas. GRESTO integrates geospatial data and ecological criteria through a multi-criteria decision-making approach based on the Analytic Hierarchy Process (AHP). The model incorporates nine key indicators, including fire severity, tree density, land cover, fire history, slope, elevation, aspect, precipitation, and temperature, and classifies restoration priority zones into low, medium, and high categories. When applied to the Solea fire event in Cyprus, the model identified 24% of the area as high priority, 66% as medium and 10% as low. The validation against previous restoration actions implemented in the study area demonstrated reliable agreement, with an overall accuracy of 80.9%, a recall of 0.70 for high priority areas, and an AUC of 0.79, indicating very good separability. Moreover, sensitivity analysis further confirmed the robustness of the model under varying parameter weights. These findings highlight the GRESTO model’s potential to support data-driven, cost-effective restoration planning aligned with national and international environmental goals. Full article

(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)

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