Multi-Sensor Remote Sensing and Ecological Models for Bird and Wildlife Conservation

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Ecology and Conservation".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 570

Special Issue Editors


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Guest Editor
Azienda Sanitaria Locale della Valle d'Aosta (AUSL VdA), S.C. Animal Health, Località Amerique 7/F, 11020 Quart, Italy
Interests: wildlife ecology; GIS; zoonoses; One Health; wildlife planning and management; risk maps; spillover
Special Issues, Collections and Topics in MDPI journals

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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

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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

Special Issue Information

Dear Colleagues,

Remote sensing and ecological modeling are revolutionizing wildlife conservation by providing critical insights into habitat changes, species distributions, and ecological processes. Many bird species are shifting their geographic ranges to find suitable conditions, making it essential to integrate various remote sensing technologies, including satellite, crewed, and uncrewed airborne imagery, to monitor these changes effectively. GIS and remote sensing tools allow scientists and managers to address the complex challenges facing bird and wildlife conservation. This type of spatial analysis plays a pivotal role in tracking disease spread, such as avian influenza, and supporting public health institutions.

Remote sensing has become an invaluable tool for ornithologists and conservation biologists in understanding bird distribution, migration patterns, and habitat use. By analyzing data from multiple remote sensing sources, researchers can identify and map various habitats—including forests, wetlands, grasslands, and coastal areas—that are crucial for different bird species. Monitoring changes in land cover and habitat status over time helps assess the impacts of habitat loss and fragmentation on bird and wildlife populations. By integrating multi-sensor remote sensing data with species occurrence records, researchers can develop ecological models to quantify habitat and behavioral changes and predict suitable areas for conservation efforts.

We welcome submissions exploring remote sensing and ecological models for bird and wildlife conservation, with a particular focus on analyzing relationships between species presence, environmental conditions, and disease dynamics.

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

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Keywords

  • birds
  • earth observation data
  • ecological model
  • remote sensing
  • wildlife conservation

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Published Papers (1 paper)

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Research

21 pages, 11783 KB  
Article
Spatio-Temporal Pattern Analysis of African Swine Fever Spreading in Northwestern Italy—The Role of Habitat Interfaces
by Samuele De Petris, Tommaso Orusa, Annalisa Viani, Francesco Feliziani, Marco Sordilli, Sabatino Troisi, Simona Zoppi, Marco Ragionieri, Riccardo Orusa and Enrico Borgogno-Mondino
Animals 2025, 15(19), 2886; https://doi.org/10.3390/ani15192886 - 2 Oct 2025
Abstract
African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key [...] Read more.
African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key factors considered include pig density, wildlife proximity, and environmental conditions. The spatial analysis revealed that central–western municipalities exhibited higher risk due to favorable environmental conditions and dense wild boar populations, while peripheral areas showed a temporal delay in outbreak emergence. Mapping the spreading rate and habitat interfaces allowed the development of a spatial risk model, which was further analyzed using geostatistical techniques to understand disease dynamics. The results demonstrate the effectiveness of geospatial modeling in identifying high-risk zones, characterizing spatio-temporal patterns, and supporting targeted prevention and surveillance strategies. These findings provide actionable insights for ASF management and resource allocation. Future studies may refine these models by integrating additional datasets and environmental variables, enhancing predictive capacity and applicability across different regions. Full article
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