Innovations in Forest Fire Detection and Monitoring: Integrating GISs, Remote Sensing, and AI
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 4786
Special Issue Editors
Interests: remote sensing; GIS; forest management; wildfire; disaster risk management; machine/deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; resources and environment monitoring; fire detection; deep learning; geographic information system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As the world faces an unprecedented surge in forest fire events, a renewed emphasis has been placed on fortifying our detection and monitoring capabilities. Within this global challenge lies a tapestry of complex variables: rapidly changing climate conditions, anthropogenic disturbances, and the innate unpredictability of wildfires. To address these intricacies, the integration of geospatial intelligence, advanced remote sensing, and burgeoning computational technologies has come to the forefront.
At the heart of this transformational shift is the fusion of Geographic Information Systems (GISs) and remote sensing technologies, creating a potent combination capable of providing real-time, high-resolution data of vast forested regions. Furthermore, with the integration of big data analytics, artificial intelligence (AI), and machine/deep learning algorithms, our capacity to predict, monitor, and respond to forest fires has been dramatically amplified.
This Special Issue seeks to consolidate recent advances, methodologies, and innovative applications at the nexus of GISs, remote sensing, AI, and forest fire management. I invite researchers, practitioners, and experts to contribute their insights, findings, and strategic visions to foster an interdisciplinary dialogue.
Topics of interest include, but are not limited to, the following:
- Advanced GIS applications in forest fire detection and mitigation;
- State-of-the-art remote sensing technologies in fire management;
- Leveraging big data analytics in predicting wildfire patterns;
- AI and machine/deep learning models for real-time fire monitoring;
- Integration of disparate data sources for enhanced forest fire response;
- Challenges and solutions in data fusion analysis for fire detection;
- Proactive forest fire management strategies using AI;
- Predictive analysis of forest fire susceptibility and risk;
- Novel forest fire surveying methods harnessing GISs and remote sensing;
- The role of advanced tech in post-fire recovery and landscape restoration.
Prof. Dr. Chao Ren
Dr. Maofang Gao
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. Forests is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- GIS and remote sensing
- forest surveying methods
- wildfires/forest fires
- big data analytics and artificial intelligence
- machine/deep learning
- data integration and fusion analysis
- monitoring and prediction
- susceptibility and risk mapping
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