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Integrating AI and Remote Sensing for Monitoring and Mapping Fire...
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Integrating AI and Remote Sensing for Monitoring and Mapping Fire Impacts on Agroforestry and Wildlife Systems

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Science Models, Remote Sensing, and Data".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 925

Special Issue Editors

Dr. Tommaso Orusa

E-Mail Website
Guest Editor
1. Istituto Zooprofilattico Sperimentale dell'Abruzzo e Molise "G. Caporale", Teramo, Italy
2. INVA Spa—Earth Observation Valle d'Aosta (eoVdA), Località L'Île-Blonde, 5, 11020 Brissogne, Italy
Interests: remote sensing; earth observation data; forestry; One Health; GIS; Google earth engine; machine learning; recovery
Special Issues, Collections and Topics in MDPI journals
Dr. Samuele De Petris

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, Italy
Interests: remote sensing; earth observation data; forestry; ecology; GIS; photogrammetry; statistics; fire
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo Sarvia

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, Grugliasco 10095, Italy
Interests: remote sensing; Earth Observation data; agronomy; classification; AI; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Annalisa Viani

E-Mail Website
Guest Editor
Azienda Sanitaria Locale della Valle d'Aosta (AUSL VdA), S.C. Animal Health, Località Amerique 7/F, 11020 Quart, Italy
Interests: GIS; wildlife; geostatistics; epidemiology; ecology; recovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfires are increasingly affecting both agroforestry systems and wildlife habitats, altering ecosystems and biodiversity. Landscapes are characterized by a complex interaction between agriculture and natural ecosystems and require innovative tools for effective post-fire monitoring and the planning of restoration efforts. Traditional methods of assessing fire impacts often fail in capturing the spatial and temporal complexities of environments, particularly in remote or inaccessible areas.

This Special Issue will focus on the use of artificial intelligence (AI) and remote sensing technologies to advance the monitoring and mapping of fire impacts in agroforestry and wildlife systems. Through the application of AI-driven algorithms, multispectral, hyperspectral, and SAR satellite imagery, UAV (drone) data, LiDAR, and machine learning models, researchers are able to see fire damage, vegetation loss, wildlife habitat degradation, and ecosystem resilience and restoration in high resolution. Key topics for this Special Issue include the AI-enhanced detection of burned areas, automated mapping of post-fire vegetation recovery, and predictive modeling of habitat and species vulnerability, as well as effects on wildlife and disease, in line with One Health. We also encourage submissions of research on species identification, the monitoring of wildlife migration patterns post fire, and innovative approaches to restoring critical habitats and biodiversity corridors, utilizing a broad range of AI machine learning and GIS/remote sensing techniques, as well as One Health approaches.

This Special Issue will include case studies from different geographic regions, highlighting successful applications of AI and remote sensing in agroforestry and wildlife conservation or One Health driven by GIS, remote sensing, and machine learning/AI. Original articles, reviews, communications, and reports are welcome. This Special Issue will also address challenges such as data availability, processing limitations, and the integration of AI models into land management and conservation strategies. By bridging AI technology and ecological monitoring, this Special Issue will provide cutting-edge methods for enhancing resilience, improving restoration practices, and supporting decision-making for sustainable agroforestry and wildlife management according to a One Health perspective in the face of increasing wildfire risks.

Dr. Tommaso Orusa
Dr. Samuele De Petris
Dr. Filippo Sarvia
Dr. Annalisa Viani
Guest Editors

Alessandro Farbo
Guest Editors 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. Fire is an international peer-reviewed open access monthly 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 2400 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

  • AI
  • remote sensing
  • agroforestry
  • GIS
  • wildlife and One Health
  • monitoring and mapping
  • machine learning and deep learning
  • fire impacts
  • recovery
  • vulnerability

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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

19 pages, 11371 KiB  
Article
Applying Remote Sensing to Assess Post-Fire Vegetation Recovery: A Case Study of Serra do Açor (Portugal)
by Noah Wassner, Albano Figueiredo and Adélia N. Nunes
Fire 2025, 8(5), 163; https://doi.org/10.3390/fire8050163 - 22 Apr 2025
Viewed by 192
Abstract
Wildfires in the Mediterranean basin, particularly in Portugal, pose significant ecological challenges by altering landscapes and ecosystems. This study examines vegetation recovery in Serra do Açor seven years after the 2017 wildfires, using remote sensing and field data to analyze post-fire dynamics. The [...] Read more.
Wildfires in the Mediterranean basin, particularly in Portugal, pose significant ecological challenges by altering landscapes and ecosystems. This study examines vegetation recovery in Serra do Açor seven years after the 2017 wildfires, using remote sensing and field data to analyze post-fire dynamics. The primary goal was to assess whether fire severity, measured via the dNBR index from Sentinel-2 imagery, impacts vegetation recovery or if site-specific factors and pre-fire floristic composition are more influential. Randomly assigned plots based on previous land use and fire severity were analyzed for floristic attributes. To quantify and classify cover changes, a supervised classification methodology based on the random forest algorithm was applied to Sentinel-2 data. The results showed no clear link between fire severity and recovery; instead, local factors like soil and topography, along with dominant pre-fire species, influenced recovery. Acacia and eucalyptus communities grew faster and increased the occupied area but exhibited lower diversity than native vegetation communities. Supervised classifications achieved high accuracy (Kappa > 0.90), showing increased shrubland areas and expansion of eucalyptus and acacia. The study highlights the methodology’s effectiveness and potential for broader applications in future research. Full article
(This article belongs to the Special Issue Integrating AI and Remote Sensing for Monitoring and Mapping Fire Impacts on Agroforestry and Wildlife Systems)
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18 pages, 3958 KiB  
Article
AI-Driven UAV Surveillance for Agricultural Fire Safety
by Akmalbek Abdusalomov, Sabina Umirzakova, Komil Tashev, Nodir Egamberdiev, Guzalxon Belalova, Azizjon Meliboev, Ibragim Atadjanov, Zavqiddin Temirov and Young Im Cho
Fire 2025, 8(4), 142; https://doi.org/10.3390/fire8040142 - 2 Apr 2025
Viewed by 436
Abstract
The increasing frequency and severity of agricultural fires pose significant threats to food security, economic stability, and environmental sustainability. Traditional fire-detection methods, relying on satellite imagery and ground-based sensors, often suffer from delayed response times and high false-positive rates, limiting their effectiveness in [...] Read more.
The increasing frequency and severity of agricultural fires pose significant threats to food security, economic stability, and environmental sustainability. Traditional fire-detection methods, relying on satellite imagery and ground-based sensors, often suffer from delayed response times and high false-positive rates, limiting their effectiveness in mitigating fire-related damages. In this study, we propose an advanced deep learning-based fire-detection framework that integrates the Single-Shot MultiBox Detector (SSD) with the computationally efficient MobileNetV2 architecture. This integration enhances real-time fire- and smoke-detection capabilities while maintaining a lightweight and deployable model suitable for Unmanned Aerial Vehicle (UAV)-based agricultural monitoring. The proposed model was trained and evaluated on a custom dataset comprising diverse fire scenarios, including various environmental conditions and fire intensities. Comprehensive experiments and comparative analyses against state-of-the-art object-detection models, such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and SSD-based variants, demonstrated the superior performance of our model. The results indicate that our approach achieves a mean Average Precision (mAP) of 97.7%, significantly surpassing conventional models while maintaining a detection speed of 45 frames per second (fps) and requiring only 5.0 GFLOPs of computational power. These characteristics make it particularly suitable for deployment in edge-computing environments, such as UAVs and remote agricultural monitoring systems. Full article
(This article belongs to the Special Issue Integrating AI and Remote Sensing for Monitoring and Mapping Fire Impacts on Agroforestry and Wildlife Systems)
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