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Forests
Special Issues
Emerging Advances in Digital Forest Monitoring, Analysis, and Modeling
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Emerging Advances in Digital Forest Monitoring, Analysis, and Modeling

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: closed (31 December 2024) | Viewed by 2153

Special Issue Editor

Dr. Fa Li

E-Mail Website
Guest Editor
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
Interests: earth system modeling; ecosystem biophysics and biogeochemistry; wildfires; natural greenhouse gases and nature-based climate solutions; machine learning and causal inference; remote sensing and GIS

Special Issue Information

Dear Colleagues,

Forests are critical in global energy–water–carbon cycles, biodiversity, climate mitigation and adaptation, and human socioeconomics. While there have been substantial investigations focusing on forests, the roles of forests in ecosystems, the climate, and human systems remain largely uncertain. Emerging advances in digital techniques, analysis, and modeling enable better the assessment and understanding of forest properties and processes across various spatial and temporal scales under environmental changes.

This Special Issue aims to synthesize and present current advanced digital techniques and their applications in the monitoring, analyzing, and modeling of forests, including, but not limited to, carbon stock, vegetation structure and traits, biophysical and biogeochemical processes, biodiversity, disturbances, forest resistance and resilience, forest management, human–forest interactions, natural climate solutions, and sustainable development. Research utilizing the following techniques are highly relevant to this Special Issue: remote sensing, Geographic Information Systems (GIS), air and ground observations (e.g., eddy covariance, chamber, and isotope), statistical analysis, machine/deep learning, digital twin, and Earth system modeling/terrestrial ecosystem modeling.

Dr. Fa Li
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.

Keywords

  • forests
  • digital techniques
  • remote sensing and GIS
  • ground and air observations
  • machine learning
  • ecosystem analysis and modeling

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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

26 pages, 33213 KiB  
Article
From Crown Detection to Boundary Segmentation: Advancing Forest Analytics with Enhanced YOLO Model and Airborne LiDAR Point Clouds
by Yanan Liu, Ai Zhang and Peng Gao
Forests 2025, 16(2), 248; https://doi.org/10.3390/f16020248 - 28 Jan 2025
Cited by 1 | Viewed by 972
Abstract
Individual tree segmentation is crucial to extract forest structural parameters, which is vital for forest resource management and ecological monitoring. Airborne LiDAR (ALS), with its ability to rapidly and accurately acquire three-dimensional forest structural information, has become an essential tool for large-scale forest [...] Read more.
Individual tree segmentation is crucial to extract forest structural parameters, which is vital for forest resource management and ecological monitoring. Airborne LiDAR (ALS), with its ability to rapidly and accurately acquire three-dimensional forest structural information, has become an essential tool for large-scale forest monitoring. However, accurately locating individual trees and mapping canopy boundaries continues to be hindered by the overlapping nature of the tree canopies, especially in dense forests. To address these issues, this study introduces CCD-YOLO, a novel deep learning-based network for individual tree segmentation from the ALS point cloud. The proposed approach introduces key architectural enhancements to the YOLO framework, including (1) the integration of cross residual transformer network extended (CReToNeXt) backbone for feature extraction and multi-scale feature fusion, (2) the application of the convolutional block attention module (CBAM) to emphasize tree crown features while suppressing noise, and (3) a dynamic head for adaptive multi-layer feature fusion, enhancing boundary delineation accuracy. The proposed network was trained using a newly generated individual tree segmentation (ITS) dataset collected from a dense forest. A comprehensive evaluation of the experimental results was conducted across varying forest densities, encompassing a variety of both internal and external consistency assessments. The model outperforms the commonly used watershed algorithm and commercial LiDAR 360 software, achieving the highest indices (precision, F1, and recall) in both tree crown detection and boundary segmentation stages. This study highlights the potential of CCD-YOLO as an efficient and scalable solution for addressing the critical challenges of accuracy segmentation in complex forests. In the future, we will focus on enhancing the model’s performance and application. Full article
(This article belongs to the Special Issue Emerging Advances in Digital Forest Monitoring, Analysis, and Modeling)
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17 pages, 4704 KiB  
Article
Selection of Trees for Thinning Using Machine Learning Algorithms and Competition Indices
by Yong-Kyu Lee, Jung-Soo Lee, Sang-Kyun Han, Hyo-Vin Ji and Jin-Woo Park
Forests 2025, 16(1), 65; https://doi.org/10.3390/f16010065 - 2 Jan 2025
Viewed by 766
Abstract
In artificial forests, regular thinning is required to promote diameter growth for producing high-quality, large-diameter timber. However, selecting trees for thinning often relies on qualitative and subjective assessments by field workers. The purpose of this study was to develop a quantitative method for [...] Read more.
In artificial forests, regular thinning is required to promote diameter growth for producing high-quality, large-diameter timber. However, selecting trees for thinning often relies on qualitative and subjective assessments by field workers. The purpose of this study was to develop a quantitative method for selecting trees for thinning by combining machine learning algorithms and competition indices. Our study site included the Pinus koraiensis area within a Kangwon National University research forest in the Republic of Korea. Data from a model development site were used for the basic crown classification model for Pinus koraiensis. The model was optimized by adjusting hyperparameters. Different algorithms, including Random Forest, XGBoost, and LightGBM (LGBM), were improved using Random Search. LGBM showed the highest accuracy of 71.6%. LGBM—in combination with the competition indices—was used to classify the crown class in the application site and select trees for thinning. Compared to the combination of Braathe and Martin-EK indices, the combination of LGBM and Hegyi index enabled the even distribution of the residual stand in the entire site after thinning. It lowered the distribution of hot spots, which represent competition. Thus, the combination of LGBM and Hegyi index was the most effective option to improve the spatial distribution of trees after thinning. Our findings can improve forest management by providing a quantitative and objective method for selecting trees for thinning. Full article
(This article belongs to the Special Issue Emerging Advances in Digital Forest Monitoring, Analysis, and Modeling)
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