Forest Disturbance Monitoring by Remote Sensing: Advancements and Applications
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: 25 March 2026 | Viewed by 120
Special Issue Editors
Interests: forest disturbance; disaster risk assessment; pest and disease; climate change
Interests: satellite time series analysis; change detection; vegetation phenology; wildfires; land degradation
Special Issues, Collections and Topics in MDPI journals
Interests: vegetation dynamics; spatial analysis; vegetation pests and diseases monitoring
Interests: spatial and environmental big data processing methods; multisource spatial and environmental data assimilation and information fusion methods; digital soil mapping with remote sensing data; deep learning for land cover and land use mapping; Gaussian process for forest trait mapping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Monitoring forest disturbances is essential for understanding and sustaining ecosystem health and resilience. As environmental pressures intensify, the accurate detection, quantification, attribution, and impact assessment of disturbances using remote sensing technologies have become increasingly critical. This Special Issue aims to showcase recent advancements in methodologies, emphasizing innovative approaches that enhance detection accuracy and operational applicability. We welcome cutting-edge research that addresses a wide range of forest disturbance—from abrupt events (e.g., wildfires and logging) to gradual processes (e.g., pest outbreaks and drought stress). Emphasis is placed on bridging science and forest management to support informed decision-making.
Topics of interest include, but are not limited to, the following:
- Advanced algorithms for disturbance detection and classification;
- Multi-source and multi-temporal data fusion for disturbance attribution;
- Near-real-time monitoring systems and early warning frameworks;
- Forest pests and diseases monitoring and ecological risk assessment;
- Disturbance severity and recovery assessment methodologies;
- Quantification of ecological impacts, including carbon dynamics and biodiversity;
- Operational applications in forest management and policy support.
Dr. Longlong Zhao
Dr. Chao Ding
Dr. Biyao Zhang
Prof. Dr. Jinsong Chen
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.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
- data fusion and AI
- forest disturbances
- fire risk and detection
- pest and disease detection
- disturbance attribution
- ecological impact assessment
- forest management
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