Special Issue "Mapping Forest Vegetation via Remote Sensing Tools"
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 (15 March 2023) | Viewed by 5980
Special Issue Editors

Interests: remote sensing; land use land cover mapping; classification methods; vegetation mapping; change detection; land surface temperature analysis; air quality monitoring

Interests: GIS; optical remote sensing
Special Issues, Collections and Topics in MDPI journals

Interests: synthetic aperture radar; geophysical techniques; radar imaging; remote sensing by radar; geophysical image processing; vegetation; vegetation mapping; wildfires; deformation; geographic information systems

Interests: geodesy; ground deformations; synthetic aperture radar (SAR); interferometry SAR (InSAR) techniques; phase unwrapping; multi-track; satellite constellations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forests have an essential role in supporting the Earth's ecological balance and environmental health because they sustain the global carbon cycle, the quality of water resources, and recreational potential.
Recent advancements in a variety of remote sensing data availability, innovative image-processing methodologies, and cloud computing technologies have provided a significant opportunity to observe and monitor forest vegetation on different scales from local to global.
The Special Issue will cover the application of remote sensing data from multiple platforms. Original research papers are expected to use the recently developed techniques to process a wide variety of remote sensing data for forest vegetation mapping. Both research papers and innovative review papers are invited.
High-quality contributions emphasizing (but not limited to) the topics listed below are solicited for the Special Issue:
- Mapping and monitoring forest vegetation;
- Multispectral, hyperspectral, Synthetic Aperture Radar (SAR), InSAR and LiDAR applications;
- Multi-sensor integration for environmental assessment;
- Application of advanced image processing methodologies for mapping forest vegetation;
- Application of remote sensing systems to derive spatio-temporal information on forest distribution, forest vegetation discrimination, forest vegetation conditions, and deforestation.
Prof. Dr. Filiz Bektas Balcik
Prof. Dr. Fusun Balik Sanli
Dr. Fabiana Caló
Dr. Antonio Pepe
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 2000 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
- forest vegetation mapping
- advanced image processing
- image classification
- multispectral data
- hyperspectral data
- SAR
- LİDAR