Machine Learning Techniques in Forest Mapping and Vegetation Analysis

Dear Colleagues,

Understanding and analyzing forest vegetation are crucial for both the conservation and optimal utilization of natural resources. Due to rapidly advanced technologies and computational capacities, in recent years, machine learning and deep learning have emerged as powerful tools in the realm of forest mapping. This Special Issue aims to delve deep into innovative applications of these techniques for understanding forest terrains and vegetation dynamics. We invite research that uses machine learning and deep learning methodologies to analyze, categorize, and monitor forest vegetation and its various features across different scales.

The areas of interest for this Special Issue encompass:

• Deep learning models for vegetation classification and monitoring;
• Machine learning algorithms for forest segmentation and object detection;
• Predictive models for species identification using machine learning;
• Applications of UAV, airborne, or satellite data in conjunction with machine learning;
• Analysis using RGB, multispectral, or hyperspectral imagery paired with deep learning algorithms;
• The integration of LiDAR, optical, infrared, and radar data with machine learning models;
• Machine learning in mapping wildfire or deforestation patterns;
• Deep learning approaches to urban and agricultural forest analyses;
• Machine learning techniques for submerged or underwater forest mapping;
• Predictive modeling for carbon storage assessment using deep learning;
• Dimensional analysis of forests using machine learning techniques;
• Applications of machine learning in local, regional, and global forest mapping.

This Special Issue aspires to bring together an interdisciplinary amalgamation of research, highlighting the transformative power of machines and deep learning in the domain of forest mapping and vegetation analysis. We particularly welcome novel methodologies and state-of-the-art models that revolutionize traditional mapping techniques. 

Prof. Dr. Lucas Prado Osco
Dr. Ana Paula Marques Ramos
Dr. Lúcio André De Castro Jorge
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.

• artificial intelligence
• deep learning
• remote sensing
• monitoring
• image processing