Applications and Opportunities of Remote Sensing in Fire Ecology
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".
Deadline for manuscript submissions: 15 December 2024 | Viewed by 85
Special Issue Editors
Interests: computer vision; machine learning and artificial intelligence; multi-dimensional signal processing; intelligent systems and applications; environmental informatics and remote sensing; ICT for civil protection
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; computer vision; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
2. Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
Interests: computer vision and applications; real-time image processing; analysis and visualization; pattern recognition; machine learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Wildfires and particularly extreme events, such as mega-fires, have increased dramatically during the last decades, both in terms of frequency and magnitude. Such phenomena represent dangerous natural hazards with transboundary and devastating aftermaths. Understanding wildfire dynamics, including its risks, spatiotemporal propagation, and effects is important for studying its ecological footprint.
Modern remote sensing technologies combined with recently developed machine learning and AI algorithms have led to a significant increase in the types of signals and imagery available. Remote sensing technologies, such as terrestrial, airborne, and satellite systems using various sensors (e.g., multispectral, lidar, etc.) have already demonstrated great potential for understanding the complex nature and great challenges of fire hazards and producing powerful and cost-effective tools for wildfire management. Furthermore, large amounts of reliable information as well as accurate predictions need to be readily available and systematically analysed to support prevention, preparedness, suppression, and post-wildfire restoration efforts.
Therefore, this Special Issue titled “Applications of Remote Sensing in Fire Ecology” is focused on coordinated and collaborative research efforts as an urgent priority, which are required at the European and worldwide levels in order to provide a powerful means for studying these phenomena, improving fire predictability, and mitigating short- and long-term consequences. Examples of applications to be considered range from fuel mapping, fire risk mapping, fire and smoke detection and propagation estimation, burned area estimation, and post-fire vegetation recovery monitoring. Articles may address, but are not limited, to the following topics:
- Fire ecology;
- Fire risk assessment and mapping;
- Fuel mapping;
- Forest ecosystem monitoring;
- Forest fire prevention;
- Fire and smoke detection;
- Fire and smoke propagation;
- Burned area estimation.
Dr. Nikos Grammalidis
Dr. Kosmas Dimitropoulos
Dr. Panagiotis Barmpoutis
Guest Editors
Manuscript Submission Information
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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
- fire ecology
- forest fire prevention
- fire risk assessment and management
- fire/smoke detection sensors and algorithms
- fire/smoke modeling and prediction
- burned area estimation
- post-fire management and restoration
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