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Forests
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Modeling Forest Dynamics
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Modeling Forest Dynamics

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: 31 December 2025 | Viewed by 848

Special Issue Editors

Dr. Zongzheng Chai

E-Mail Website
Guest Editor
College of Forestry, Guizhou University, Guiyang 550025, China
Interests: forest management; forest growth and yield models; vegetation ecology; forest quality assessments; forest ecosystems
Dr. Xianzhao Liu

E-Mail Website
Guest Editor
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forest management; forest growth and yield models; vegetation ecology; forest quality assessments; forest ecosystems
Dr. Lingbo Dong

E-Mail Website
Guest Editor
College of Forestry, Northeast Forestry University, Harbin 150040, China
Interests: forest management; forest growth and yield models; vegetation ecology; forest quality assessments; forest ecosystems
Special Issues, Collections and Topics in MDPI journals
Dr. Zaixing Zhou

E-Mail Website
Guest Editor
Earth Systems Research Center (ESRC), University of New Hampshire, 468 Morse Hall, 8 College Road, Durham, NH 03824, USA
Interests: carbon and nitrogen cycles; forest modeling; ecophysiology; forest remote sensing

Special Issue Information

Dear Colleagues,

Forests are the cornerstone of terrestrial ecosystems, providing a plethora of ecological services and hosting a remarkable diversity of life; however, they are subject to a multitude of natural and anthropogenic factors that drive continuous change. “Modeling Forest Dynamics” is a dedicated Special Issue of Forests that will delve into the crucial realm of understanding and predicting these complex forest transformations. This Special Issue will assemble a collection of high-caliber research papers that employ various modeling techniques to decipher the multifaceted aspects of forest dynamics. It will feature studies ranging from the microscopic level of the growth and mortality patterns of individual trees to the macroscopic scale of entire forest landscapes and their responses to large-scale disturbances and long-term environmental shifts. The contributions herein will explore how different models capture the essence of forest succession, the impact of climate variability on forest productivity and species distribution, and the role of biotic interactions such as competition and symbiosis in shaping forest community structures. By focusing on modeling forest dynamics, this Special Issue aims to bridge the gaps between theoretical ecological research and practical forestry applications. It offers a platform for scientists, researchers, and practitioners to exchange ideas and insights, ultimately paving the way for more informed and sustainable forest management and conservation strategies.

Dr. Zongzheng Chai
Dr. Xianzhao Liu
Dr. Lingbo Dong
Dr. Zaixing Zhou
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

  • forests
  • ecological services
  • natural factors
  • anthropogenic factors
  • modeling forest dynamics
  • modeling techniques
  • forest dynamics
  • tree growth
  • mortality patterns
  • forest landscapes
  • forest productivity
  • forest management
  • forest conservation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

27 pages, 10030 KiB  
Article
Enhancing Deforestation Detection Through Multi-Domain Adaptation with Uncertainty Estimation
by Luiz Fernando de Moura, Pedro Juan Soto Vega, Gilson Alexandre Ostwald Pedro da Costa and Guilherme Lucio Abelha Mota
Forests 2025, 16(5), 742; https://doi.org/10.3390/f16050742 (registering DOI) - 26 Apr 2025
Viewed by 101
Abstract
Deep learning models have shown great potential in scientific research, particularly in remote sensing for monitoring natural resources, environmental changes, land cover, and land use. Deep semantic segmentation techniques enable land cover classification, change detection, object identification, and vegetation health assessment, among other [...] Read more.
Deep learning models have shown great potential in scientific research, particularly in remote sensing for monitoring natural resources, environmental changes, land cover, and land use. Deep semantic segmentation techniques enable land cover classification, change detection, object identification, and vegetation health assessment, among other applications. However, their effectiveness relies on large labeled datasets, which are costly and time-consuming to obtain. Domain adaptation (DA) techniques address this challenge by transferring knowledge from a labeled source domain to one or more unlabeled target domains. While most DA research focuses on single-target single-source problems, multi-target and multi-source scenarios remain underexplored. This work proposes a deep learning approach that uses Domain Adversarial Neural Networks (DANNs) for deforestation detection in multi-domain settings. Additionally, an uncertainty estimation phase is introduced to guide human review in high-uncertainty areas. Our approach is evaluated on a set of Landsat-8 images from the Amazon and Brazilian Cerrado biomes. In the multi-target experiments, a single source domain contains labeled data, while samples from the target domains are unlabeled. In multi-source scenarios, labeled samples from multiple source domains are used to train the deep learning models, later evaluated on a single target domain. The results show significant accuracy improvements over lower-bound baselines, as indicated by F1-Score values, and the uncertainty-based review showed a further potential to enhance performance, reaching upper-bound baselines in certain domain combinations. As our approach is independent of the semantic segmentation network architecture, we believe it opens new perspectives for improving the generalization capacity of deep learning-based deforestation detection methods. Furthermore, from an operational point of view, it has the potential to enable deforestation detection in areas around the world that lack accurate reference data to adequately train deep learning models for the task. Full article
(This article belongs to the Special Issue Modeling Forest Dynamics)
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19 pages, 3084 KiB  
Communication
forestPSD: An R Package for Analyzing the Forest Population Structure and Numeric Dynamics
by Jiaxing Lei and Zongzheng Chai
Forests 2025, 16(2), 303; https://doi.org/10.3390/f16020303 - 9 Feb 2025
Cited by 1 | Viewed by 616
Abstract
Forest population structure and dynamics represent core research areas in forest ecology, encompassing multiple components such as quantitative analyses of population changes, age structure, life tables, and species dynamics within specific spatial and temporal contexts. These elements provide crucial insights into tree adaptation [...] Read more.
Forest population structure and dynamics represent core research areas in forest ecology, encompassing multiple components such as quantitative analyses of population changes, age structure, life tables, and species dynamics within specific spatial and temporal contexts. These elements provide crucial insights into tree adaptation mechanisms and inform evidence-based strategies for population conservation and management. However, traditional analyses of forest population structure and dynamics face significant challenges due to the absence of specialized analytical software. This limitation not only increases data processing complexity and workload but also elevates the risk of analytical errors. To address these challenges, we developed forestPSD, a novel R package based on established principles of forest population structure and dynamics analysis. This package provides researchers with an efficient and user-friendly tool for analyzing forest population structures and their temporal changes, thereby facilitating advancement in this field. Full article
(This article belongs to the Special Issue Modeling Forest Dynamics)
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