Machine Learning Methods in Forest Ecosystem Sciences

Dear Colleagues,

In recent years, the development and application of machine learning methods have gained a great deal of attention due to the ever-growing possibilities these data-driven methods offer to nearly every field of applied and theoretical sciences. Machine learning methods are also increasingly being applied in research addressing the physical properties of forest environments and processes within these complex systems. The rapid development of deep-learning models and the required computational resources, for instance, is currently triggering a surge of topic studies utilizing multispectral imagery from satellites, UAVs, and other remote sensing sources. However, machine learning methods are as versatile and diverse as forest system science itself and can be applied to process simulations, pattern recognition, classification tasks, or the detection of features and changes thereof. This flexibility fosters interdisciplinary research, often combining various disciplines of forest research such as meteorological modeling; the quantification of carbon, water, and energy cycles climate impact assessments; canopy structure analyses; species distribution models; or assessments of wildfire hazard or pest and disease hazards, to name a few. Therefore, the journal Forests is dedicating a Special Issue featuring this exciting section of cutting-edge research methods. We invite contributions addressing the development, analysis, and/or application of machine learning methods, with a focus on forest sciences.

Potential topics include, but are not limited to:

• Forest meteorology and micrometeorology;
• Turbulent structures in the boundary layer including sub-canopy structures;
• Atmospheric carbon, water, or energy exchange processes above and within forest ecosystems;
• Machine-learning methods in forest remote sensing;
• Climate change effects on forest ecosystems;
• Risk assessments and predictions regarding forest resources.

Dr. Andres Schmidt
Guest Editor

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.

• changing forests
• image analyses
• 3D forest structure
• shallow and deep network topologies in forest science
• atmospheric exchange processes
• climate change effects
• forest composition modeling
• species distribution modeling
• benefits and hazards of forest wildfires