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

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 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. 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.