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Forests
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Remote Sensing Monitoring and Analysis of Forest Structure and Function...
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Remote Sensing Monitoring and Analysis of Forest Structure and Function in Relation to Climate Regulation

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: 16 September 2026 | Viewed by 2128

Special Issue Editors

Dr. David Gwenzi

E-Mail Website
Guest Editor
Department of Environmental Science & Management, Cal Poly Humboldt State University, Arcata, CA 95521, USA
Interests: remote sensing; environmental sustainability; carbon accounting; climate action
Dr. Tawanda W. Gara

E-Mail Website
Guest Editor
Department of Environmental Science and Management, California State Polytechnic University Humboldt, Arcata, CA 95521, USA
Interests: geographic information science; remote sensing; forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring forest structure and function involves systematically observing and analyzing key components such as canopy density, tree height, species composition, and biomass distribution to assess the physical framework of a forest. Simultaneously, it includes tracking ecological processes like carbon sequestration, nutrient cycling, water regulation, and biodiversity dynamics to evaluate a forest’s functional health.

This Special Issue highlights cutting-edge advancements in spaceborne and airborne remote sensing technologies, including lidar, radar, multispectral, and hyperspectral imaging and their applications in gathering data on these parameters to provide critical insights into forest resilience, productivity, and responses to environmental changes, supporting informed management and conservation strategies related to climate regulation.

By integrating machine learning, big data analytics, and multi-source satellite observations, this collection of articles addresses testimonies, opportunities, and pressing challenges in monitoring forest structure and function relative to climate regulation, offering innovative solutions for sustainable forest management and conservation. We invite contributions that explore novel methodologies, case studies, and future directions in monitoring static and dynamic forest structure, with an emphasis on improving our understanding of the functions of forests in relation to climate regulation.

Dr. David Gwenzi
Dr. Tawanda W. Gara
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

  • remote sensing
  • forest structure
  • biomass
  • forest growth
  • carbon sequestration
  • climate regulation

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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

16 pages, 7374 KB  
Article
Optimizing UAV-LiDAR Point Density for Eucalyptus Height Estimation in Agroforestry
by Ernandes Macedo da Cunha Neto, Emmanoella Guaraná, Marks Melo Moura, Hudson Franklin Pessoa Veras, Angélica Maria Almeyda Zambrano, Eben North Broadbent, Emanuel Maia, Allan Libanio Pelissari, Luciano Rodrigo Lanssanova, Carlos Roberto Sanquetta and Ana Paula Dalla Corte
Forests 2025, 16(11), 1747; https://doi.org/10.3390/f16111747 - 19 Nov 2025
Viewed by 957
Abstract
The demand for forest materials necessitates advancements in forest management and inventory practices. We explore the integration of Unmanned Aerial Vehicles (UAVs) equipped with LiDAR sensors as a cost-effective alternative for precise forest monitoring. It evaluates the impact of varying point cloud densities [...] Read more.
The demand for forest materials necessitates advancements in forest management and inventory practices. We explore the integration of Unmanned Aerial Vehicles (UAVs) equipped with LiDAR sensors as a cost-effective alternative for precise forest monitoring. It evaluates the impact of varying point cloud densities on the accuracy of individual tree height estimation in Eucalyptus benthamii within Crop–Livestock–Forestry systems (15.9 ha and 357 individuals·ha−1). We use a DJI M600 Pro UAV with a Velodyne 32c Ultra Puck LiDAR sensor at the Center for Technological Innovation in Agriculture (NITA) in Brazil. The resulting point clouds were processed to generate Digital Terrain Models and Canopy Height Models at densities ranging from 5 to 2000 points per square meter (pts·m−2). Statistical analyses, including Pearson correlation, root mean square error, and bias, were conducted to compare UAV-LiDAR-derived heights with field measurements. We found that reduced point densities, particularly around 100 pts·m−2, maintained high accuracy in height estimation (RMSE = 17.129%, BIAS = −7.889%), with more than 90% in trees’ detection. UAV-LiDAR systems with optimized point cloud densities offer a viable solution for forest monitoring. 100 pts·m−2 is an optimal density, promoting faster data collection, lower battery consumption, and reduced computational costs on trees’ height estimates. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring and Analysis of Forest Structure and Function in Relation to Climate Regulation)
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19 pages, 4225 KB  
Article
Storm Damage and Planting Success Assessment in Pinus pinaster Aiton Stands Using Mask R-CNN
by Ivon Brandao, Beatriz Fidalgo and Raúl Salas-González
Forests 2025, 16(11), 1730; https://doi.org/10.3390/f16111730 - 15 Nov 2025
Viewed by 659
Abstract
In Portugal, increasing wildfire frequency and severe storm events have intensified the need for advanced monitoring tools to assess forest damage and recovery efficiently. This study explores the application of deep learning neural network techniques, specifically the Mask R-CNN architecture, for the automatic [...] Read more.
In Portugal, increasing wildfire frequency and severe storm events have intensified the need for advanced monitoring tools to assess forest damage and recovery efficiently. This study explores the application of deep learning neural network techniques, specifically the Mask R-CNN architecture, for the automatic detection of trees in Pinus pinaster stands using RGB and multispectral imagery captured by a drone. The research addresses two distinct forest scenarios, resulting from disturbances intensified by climate change. The first concerns the detection of fallen trees following an extreme weather event to support damage assessment and inform post-disturbance forest management. The second focuses on segmenting individual trees in a newly established plantation after wildfire to evaluate the effectiveness of ecological restoration efforts. The collected images were processed to generate high-resolution orthophotos and orthomosaics, which were used as input for tree detection using Mask R-CNN. Results showed that integrating drone-based imagery with deep learning models can significantly enhance the efficiency of forest assessments, reducing the need for fieldwork effort and increasing the reliability of the collected data. Results demonstrated high performance, with average precision scores of 90% for fallen trees and 75% for recently planted trees, while also enabling the extraction of spatial metrics relevant to forest monitoring. Overall, the proposed methodology shows strong potential for rapid response in post-disturbance environments and for monitoring the early development of forest plantations. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring and Analysis of Forest Structure and Function in Relation to Climate Regulation)
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