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Advances in Remote Sensing for Burned Area Mapping
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Advances in Remote Sensing for Burned Area Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 6563

Editors

Dr. Giandomenico De Luca

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Guest Editor
Institute of BioEconomy (IBE), National Research Council of Italy (CNR), 50145 Sesto Fiorentino, Italy
Interests: remote sensing; forest engineering; wildfires; geo-statistics; image classification; environmental monitoring and modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giuseppe Modica

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Guest Editor
Department of Veterinary Sciences, University of Messina, I-98168 Messina, Italy
Interests: landscape ecology; geodesy; geo-statistics; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Arcidiaco

E-Mail Website
Guest Editor
Italian National Research Council | CNR, Institute for Biometeorology IBIMET, Florence, Italy
Interests: lidar; remote sensing; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although fires are a primary natural component influencing environments and landscapes dynamics, they pose critical ecological and socio-economic challenges when their intensity, frequency and severity are exacerbated. However, they are also affected by the rapidly changing climate or interactions with human activities and infrastructures (urban and wildland–urban interface–WUI–fires). Mapping their spatial distribution is thus essential for assessing lands impacts and informing long-term planning to mitigate further degradation. Burned area assessments extend beyond perimeter delineation, and remote sensing provides suitable tools for the advanced analysis of immediate post-fire effects.

This Special Issue integrates traditional detection approaches with geospatial analyses and thematic mapping, focusing on post-fire consequences such as fire severity, fire regime, biomass loss, hydrogeological alterations, microclimatic variations, landscape changes, and human-related consequences—including health risks, infrastructure damage, and degraded ecosystem services—which are essential for a comprehensive understanding of fire-induced ecological and socio-economic impacts. Studies employing multi-scale, multi-sensor—including passive (multispectral, hyperspectral, thermal) and active (SAR, LiDAR) data—multi-platform (satellite, airborne, UAV, proximal sensors) remote sensing are encouraged to enhance spectral and structural details. This also encompasses approaches entailing geo-statistical, geo-informatic, geomatic, and geographical information systems (GISs), spectral mixture analysis, 3D modeling and up-to-date modeling/processing algorithms.

We invite original research articles, reviews, and case studies addressing the following topics:

  • Advances in remote sensing for burned area mapping, active fire detection and fire severity assessment approaches;
  • Spectral and thermal characterization of post-fire combustion residues;
  • Biomass loss and carbon emission quantification;
  • Immediate biodiversity and habitat impact, including species resilience and ecosystem transformations;
  • Fire-induced microclimatic changes and fire–weather interactions;
  • Urban, peri-urban, and wildland–urban interface (WUI) fires, with socio-economic implications for air quality, ecosystem services, public health, and infrastructures;
  • Immediate impact on soil dynamics, hydrological cycles, and water resources;
  • Novel fire-related landscape metrics;
  • Innovative fire simulation for burned areas’ spread;
  • Cutting-edge processing and modeling algorithms.

You may choose our Joint Special Issue in Fire.

Dr. Giandomenico De Luca
Prof. Dr. Giuseppe Modica
Dr. Lorenzo Arcidiaco
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-anonymized 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

  • burn areas’ detection and mapping
  • geo-informatics
  • fire severity
  • fire regime
  • biomass loss
  • wildland–urban interface (WUI) fires
  • three-dimensional modeling
  • image processing

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Published Papers (4 papers)

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Research

27 pages, 8734 KB  
Article
Digital Landscapes: Assessing Fire Severity and Its Drivers Using Remote Sensing and Google Earth Engine Based on dNBR and NPP Indicators
by Dana El Khatib, Georgio Kallas, Joseph Bechara, Micheline Wehbe and Jean Stephan
Remote Sens. 2026, 18(10), 1654; https://doi.org/10.3390/rs18101654 - 20 May 2026
Viewed by 995
Abstract
Wildfires are an increasingly recurrent disturbance in Mediterranean forest landscapes, yet fire severity assessment remains limited in data-scarce regions such as Lebanon. This study aims to assess wildfire severity patterns and identify the main environmental drivers influencing fire severity across the forests of [...] Read more.
Wildfires are an increasingly recurrent disturbance in Mediterranean forest landscapes, yet fire severity assessment remains limited in data-scarce regions such as Lebanon. This study aims to assess wildfire severity patterns and identify the main environmental drivers influencing fire severity across the forests of Akkar, northern Lebanon, within a Digital Landscapes framework. Fire severity was mapped using the Differenced Normalized Burn Ratio (dNBR) derived from multi-temporal Landsat-8 imagery (2013–2024) processed in Google Earth Engine. Vegetation productivity was assessed through annual Net Primary Productivity (NPP), while topographic variables (elevation, slope, and aspect) were derived from a Digital Elevation Model. The results reveal heterogeneous fire severity patterns over the study period and pronounced spatial variability in NPP, with no consistent linear relationship between productivity and fire severity. Principal Component Analysis (PCA) was applied to explore multivariate relationships between fire severity, productivity, and terrain. PCA results show that the first two components explain 77.4% of the total variance, indicating that fire severity is primarily structured by topographic factors, particularly elevation and solar exposure, while vegetation productivity plays a secondary role. These findings highlight the dominant influence of terrain on wildfire severity in Mediterranean mountainous landscapes, and demonstrate the value of integrating remote sensing, cloud-based platforms, and multivariate analysis for fire assessment in data-scarce regions. The study contributes to the advancement of Digital Landscapes approaches by providing a scalable and data-driven framework for understanding fire dynamics and supporting future landscape management and risk assessment strategies. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Burned Area Mapping)
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25 pages, 10489 KB  
Article
An Unsupervised Machine Learning-Based Approach for Combining Sentinel 1 and 2 to Assess the Severity of Fires over Large Areas Using a Google Earth Engine
by Ciro Giuseppe Riccardi, Nicodemo Abate and Rosa Lasaponara
Remote Sens. 2026, 18(6), 956; https://doi.org/10.3390/rs18060956 - 23 Mar 2026
Viewed by 1236
Abstract
Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and [...] Read more.
Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Burned Area Mapping)
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29 pages, 33246 KB  
Article
Regional Forest Wildfire Mapping Through Integration of Sentinel-2 and Landsat 8 Data in Google Earth Engine with Semi-Automatic Training Sample Generation
by Yue Chen, Weili Kou, Xiong Yin, Rui Wang, Jiangxia Ye and Qiuhua Wang
Remote Sens. 2025, 17(24), 4038; https://doi.org/10.3390/rs17244038 - 16 Dec 2025
Cited by 2 | Viewed by 2309
Abstract
Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping [...] Read more.
Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping under complex terrain conditions. A pseudo-invariant feature (PIFS)-based fusion of Sentinel-2 and Landsat 8 imagery was employed to generate cloud-free, gap-free, and spectrally consistent pre- and post-fire reflectance datasets. Burned and unburned samples were constructed using a semi-automatic SAM–GLCM–PCA–Otsu procedure and county-level stratified sampling to ensure spatial representa-tiveness. Feature selection using LR, RF, and Boruta identified dNBR, dNDVI, and dEVI as the most discriminative variables. Within the SNIC-supported GEOBIA framework, four classifiers were evaluated; RF performed best, achieving overall accuracies of 92.02% for burned areas and 94.04% for unburned areas, outperforming SVM, CART, and KNN. K-means clustering of dNBR revealed spatial variation in fire conditions, while geographical detector analysis showed that NDVI, temperature, soil moisture, and their pairwise interactions were the dominant drivers of wildfire hotspot density. The proposed workflow provides an effective and transferable approach for high-precision burned-area extraction and quantification of wildfire-driving factors in mountainous forest regions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Burned Area Mapping)
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18 pages, 5042 KB  
Article
Tree-Based Regressor Comparison for Burn Severity Mapping: Spatially Blocked Validation Within and Across Fires
by Linh Nguyen Van and Giha Lee
Remote Sens. 2025, 17(22), 3756; https://doi.org/10.3390/rs17223756 - 19 Nov 2025
Cited by 1 | Viewed by 1040
Abstract
Accurate, timely maps of post-fire burn severity are vital for rehabilitation, hydrologic hazard assessment, and ecosystem recovery in the western United States, where large, frequent wildfires and steep environmental gradients challenge model generalization. Machine learning models, particularly tree-based regressors, are increasingly used to [...] Read more.
Accurate, timely maps of post-fire burn severity are vital for rehabilitation, hydrologic hazard assessment, and ecosystem recovery in the western United States, where large, frequent wildfires and steep environmental gradients challenge model generalization. Machine learning models, particularly tree-based regressors, are increasingly used to relate satellite-derived spectral features to ground-based severity metrics such as the Composite Burn Index (CBI). However, model generalization across spatial domains, both within and between wildfires, remains poorly characterized. In this study, we benchmarked six tree-based regression models (Decision Tree-DT, Random Forest-RF, Extra Trees-ET, Bagging, Gradient Boosting-GB, and AdaBoost-AB) for predicting wildfire severity from Landsat surface reflectance data across ten U.S. fire events. Two spatial validation strategies were applied: (i) within-fire spatial generalization via Leave-One-Cluster-Out (LOCO) and (ii) cross-fire transfer via Leave-One-Fire-Out (LOFO). Performance is assessed with R2, RMSE, and MAE under identical predictors and default hyperparameters. Results indicate that, under LOCO, variance-reduction ensembles lead: RF attains R2 = 0.679, MAE = 0.397, RMSE = 0.516, with ET statistically comparable (R2 = 0.673, MAE = 0.393, RMSE = 0.518), and Bagging close behind (R2 = 0.668, MAE = 0.402, RMSE = 0.525). Under LOFO, ET transfers best (R2 = 0.616, MAE = 0.450, RMSE = 0.571), followed by GB (R2 = 0.564, MAE = 0.479, RMSE = 0.606) and RF (R2 = 0.543, MAE = 0.490, RMSE = 0.621). These results indicate that tree ensembles, especially ET and RF, are competitive under minimal tuning for rapid severity mapping; in practice, RF is a strong choice for an individual fire with local calibration, whereas ET is preferred when model transferability to unseen fires is paramount. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Burned Area Mapping)
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