Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats
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: 31 January 2025 | Viewed by 84
Special Issue Editors
Interests: earth and environmental sciences including environmental DNA (eDNA); vegetation monitoring; vegetation conditions; monitoring vegetation biophysical and biochemical properties using remote sensing; hyperspectral, multispectral, and thermal remote sensing; geostatistics; image analysis; classification
Interests: biodiversity and environmental sciences; monitoring vegetation biophysical and biochemical properties using remote sensing data such as hyperspectral, multispectral and thermal infrared remote sensing obtained from different platforms (e.g., UAV, airborne, spaceborne and spectroscopy); land cover classification
Interests: forestry; biodiversity; ecosystem dynamics; environmental disturbances such as forest fires and droughts; vegetation monitoring; functional traits; time series analysis; change detection; spaciotemporal modeling; remote sensing; multispectral; hyperspectral; LiDAR and UAV
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Special Issue Information
Dear Colleagues,
In the realm of forest management, the early detection of threats and stressors such as insect infestations, fires, and environmental stressors is paramount. The leveraging of remote-sensing data offers a powerful toolset for timely detection and proactive intervention. This Special Issue aims to explore innovative methodologies that integrate remote-sensing technologies into environmental DNA analysis for early detection and warning in forest ecosystems.
We are thrilled to announce a Special Issue of our journal dedicated to "Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats". This Special Issue aims to showcase cutting-edge research that explores the innovative applications of remote sensing in forest studies. We invite researchers from diverse disciplines, including remote-sensing scientists, ecologists, foresters, and environmental modelers, to contribute original, high-quality manuscripts to this endeavor.
Dr. Haidi Abdullah
Dr. Elnaz Neinavaz
Dr. Margarita Huesca
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
- early detection and warning of forest insect infestations, fires, and other environmental stressors
- integrating remote-sensing data into environmental DNA for the early detection of forest stress
- utilizing image spectroscopy and hyperspectral and other remote-sensing data for early detection and warning by identifying spectral signatures and temperature anomalies indicative of forest disturbances
- time series analysis of remote-sensing data to monitor temporal changes and predict future threats
- integration of novel platforms and sensors, such as unmanned aerial vehicles (UAVs), for high-resolution forest surveys
- development of predictive models for forecasting and mitigating forest disturbances based on historical data and environmental parameters
