Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (15 October 2025) | Viewed by 3604
Special Issue Editors
Interests: forest fires; wildfire assessment; target detection; remote sensing; deep learning
Interests: forest fires; land-use/land-cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
Special Issues, Collections and Topics in MDPI journals
Interests: environmental change monitoring; machine learning/deep learning; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing, utilizing sensors on satellites, aircraft or UAVs, is crucial for wildfire risk assessment, monitoring and recovery. It enables rapid active fire detection, timely mapping and tracking of burned areas and environmental impact assessment at various scales. Recent advancements in remote sensing technology have further enhanced its capabilities in the field of fire risk assessment, monitoring and recovery. Multi-source sensors, such as optical, thermal, radar and LiDAR, have made it possible to map forest structure, fuel type and arrangement, fire characteristics, etc. Additionally, artificial intelligence algorithms can learn high-level features from multi-source data and significantly improve the accuracy and efficiency of wildfire detection, mapping and recovery modelling.
We welcome research contributions that explore novel methods in remote sensing for risk assessment, monitoring and recovery of fires. We look forward to receiving journal manuscripts that focus on leveraging remote sensing data and artificial intelligence technologies to help analyze the impact of wildfires and advance the state-of-the-art research in this critical field. Topics of interest include, but are not limited to, the following:
- Utilizing remote sensing data for early fire detection and risk assessment;
- Monitoring fire spread and behavior using satellites, aircraft or UAVs;
- Assessing post-fire damage and recovery efforts through remote sensing techniques;
- Integrating remote sensing with other technologies for comprehensive fire management;
- Developing new algorithms and methods for processing and analyzing remote sensing data related to fires.
Dr. Xikun Hu
Dr. Ioannis Gitas
Dr. Puzhao Zhang
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 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. 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
- remote sensing
- geoscience and earth observation
- artificial intelligence
- machine learning
- forest fires
- post-fire assessment
- visualization and mapping
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