Geospatial and Geomorphological Analysis of Forest Using Machine Learning
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: 21 June 2026 | Viewed by 914
Special Issue Editors
Interests: GIS; remote sensing; cartography
Interests: GIS; optical remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancements in artificial intelligence and geospatial data analytics have yielded novel insights into the functioning of forest ecosystems and their integration within geomorphological contexts. Machine learning models are increasingly employed to examine terrain characteristics, evaluate topographic influences on forest distribution, and predict the impacts of geomorphic phenomena such as erosion, landslides, or slope instability on forest dynamics. The growing availability of multisource remote sensing data, which includes UAV photogrammetry, LiDAR, multispectral, and hyperspectral satellite imaging, has significantly advanced the field of machine learning. This technological advancement has enabled the extraction of substantial spatial patterns and the modeling of intricate interactions among landform, soil, hydrology, and vegetation structure. This Special Issue seeks to compile innovative research and case studies that integrate geomorphology, geospatial analysis, and artificial intelligence (AI) modeling in the domain of forest science. We hereby extend an invitation for the submission of research that explores novel methodologies, applications, and theoretical advancements that contribute to enhanced mapping, monitoring, and prediction of the interactions between geomorphic and biological systems within forested environments.
Background
Geomorphological processes have been shown to exert a significant influence on forest ecosystems, particularly with regard to soil formation, drainage, and vegetation patterns. Conventional geomorphic and ecological analyses have relied on manual interpretation and are constrained by a paucity of field data. Presently, the integration of high-resolution geospatial data and machine learning facilitates the identification of novel associations between the configuration of the terrain and the dynamics of the forest across diverse spatial scales.
History
The field of forest geomorphology has undergone significant transformations in recent years. The shift in approach has evolved from a predominantly descriptive mapping methodology to a more data-driven modeling framework. The increasing accessibility of LiDAR, radar, and optical remote sensing data, in conjunction with advancements in machine learning algorithms, has facilitated the ability of researchers to accurately study spatial complexity and the interrelationship between terrain and vegetation.
Aim and Scope
The objective of this Special Issue is to investigate the potential of geospatial and geomorphological data, when analyzed with machine learning methods, to enhance our comprehension of forest landscapes. The subsequent list contains a subset of potential subjects for discussion, although it is imperative to acknowledge that this list is not exhaustive. Potential subjects include the following:
Cutting-Edge Research
The Special Issue invites submissions that employ sophisticated algorithms for the extraction of terrain and forest features, spatial prediction, and process simulation. It is particularly encouraged that studies be conducted that emphasize the fusion of geomorphometric variables, spectral indices, and ecological parameters. Review papers that synthesize emerging trends in the field of artificial intelligence (AI) and forest geomorphology are also welcomed.
What Kind of Papers We Are Soliciting
The following types of research articles, reviews, methodological papers, and data-driven case studies are invited:
- Machine learning approaches to forest geomorphology and terrain analysis;
- The integration of geomorphometric and ecological datasets as a critical component of forest management;
- A comparative evaluation of artificial intelligence (AI) algorithms in geomorphological forest mapping.
Dr. Halil Şenol
Prof. Dr. Fusun Sanli
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 250 words) can be sent to the Editorial Office for assessment.
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 geomorphology
- geospatial analysis
- machine learning
- forest ecosystem mapping
- AI-based environmental modeling
- forest monitoring and management
- remote sensing
- LiDAR
- UAV photogrammetry
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