Application of Active and Passive Remote Sensors in the Forest Inventory
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: 20 December 2024 | Viewed by 207
Special Issue Editors
Interests: geomatics applied to forest and environmental resources; management of geoinformatics (GIS); passive and active remote sensors; forest management; forestry; multivariate analysis and machine learning with spectral information
Special Issues, Collections and Topics in MDPI journals
Interests: forestry and environmental sciences; analysis of information on forest growth; LiDAR; biomass; forest fires; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: forest management; natural resource management; carbon cycle; silviculture; ecosystem ecology; forest ecology; forest and stand structure modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The application and analysis of the information derived from active and passive remote sensors in forest inventories allow for the development of new methods of estimation of forest parameters at a lower cost and in less time, in addition to obtaining data from inaccessible areas and covering large areas of land. This Special Issue aims to compile research papers with an overview of the application of active and passive remote sensors with different spatial and spectral resolution capacities as an innovative tool in forest inventory, as well as to provide selected contributions on methodologies, analysis techniques and applications of active and passive remote sensing used in forest inventory. Potential topics include, but are not limited to, the following: ground and airborne LiDAR technology applications; use and applications of satellite images in the forest inventory; use of unmanned aerial vehicles in the forest inventory; evaluation of the error in the estimation of forest attributes; and deep learning algorithms for the estimation of forest parameters with remote sensors.
Prof. Dr. Pablito Marcelo López-Serrano
Prof. Dr. Daniel J. Vega-Nieva
Prof. Dr. José Javier Corral-Rivas
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
- forest inventory
- diameter at breast height estimation: forest height
- remote sensing
- satellite images
- LiDAR
- unmanned aerial vehicles
- deep learning
