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UAV Applications for Forest Management: Wood Volume, Biomass, and Mapping...
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UAV Applications for Forest Management: Wood Volume, Biomass, and Mapping (Second Edition)

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 1687

Editors

Dr. Mauro Maesano

E-Mail Website
Guest Editor
Department for Innovation in Biological, Agro-Food and Forest Systems DIBAF, University of Tuscia, via S. Camillo de Lellis snc, 01100 Viterbo, Italy
Interests: UAV; forest management; remote sensing; urban forest
Special Issues, Collections and Topics in MDPI journals
Dr. Federico Valerio Moresi

E-Mail Website
Guest Editor
Department for Innovation in Biological, Agro-Food and Forest Systems DIBAF, University of Tuscia, via S. Camillo de Lellis snc, 01100 Viterbo, Italy
Interests: GIS; land use change; lidar; urban forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest ecosystems are critical in global carbon sequestration, biodiversity conservation, and sustainable resource management. The accurate and efficient monitoring of forest parameters, such as wood volume, biomass, and spatial distribution, is essential for ecological studies, climate change mitigation, and sustainable forestry management. While reliable, traditional field-based forest inventory methods are often time-consuming, labour-intensive, and limited in scalability, particularly in remote or inaccessible areas.

This Special Issue seeks to gather innovative applications of UAVs (unmanned aerial vehicles) in forest-related research. Contributions may focus on forest inventory and management, as well as studies involving forest canopy height measurement, attribute assessment, biomass estimation, disease detection, forest and biodiversity mapping, canopy gap analysis, and wildfire monitoring among other relevant topics

This Special Issue aims to showcase cutting-edge research on UAV-based approaches for forest assessment, emphasizing innovations in remote sensing techniques, machine learning applications, and data integration methods to enhance the accuracy and efficiency of forest resource management. Contributions may include, but are not limited to, the following:

  • Wood volume and biomass estimation using UAV-derived data;
  • High-resolution forest mapping for biodiversity and conservation;
  • AI and deep learning for automated tree detection and health assessment;
  • Multi-sensor fusion (LiDAR, hyperspectral, thermal) for improved forest analytics;
  • UAV applications in wildfire risk assessment and post-fire recovery.

Dr. Mauro Maesano
Dr. Federico Valerio Moresi
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • UAV
  • drone
  • remote sensing
  • forest management
  • forest inventory
  • precision forestry

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Related Special Issue

Published Papers (1 paper)

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Research

34 pages, 7649 KB  
Article
SMOTE-Data-Augmented Machine Learning for Enhancing Individual Tree Biomass Estimation Using UAV LiDAR
by Sina Jarahizadeh and Bahram Salehi
Remote Sens. 2026, 18(5), 729; https://doi.org/10.3390/rs18050729 - 28 Feb 2026
Cited by 4 | Viewed by 1194
Abstract
Estimating individual tree Above-Ground Biomass (AGB) is essential for assessing ecological functions and carbon storage in both forest and urban environments. Traditional field-based methods, such as plot measurements, are costly and impractical for large-scale applications. However, satellite- and aerial-based techniques lack the spatial [...] Read more.
Estimating individual tree Above-Ground Biomass (AGB) is essential for assessing ecological functions and carbon storage in both forest and urban environments. Traditional field-based methods, such as plot measurements, are costly and impractical for large-scale applications. However, satellite- and aerial-based techniques lack the spatial resolution for individual-tree-level analysis. Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) data, combined with machine learning (ML), offers a powerful alternative for detailed tree structure measurement and AGB estimation. Leveraging advances in deep-learning-based individual tree detection and geometric structure estimation including Height (H), Surface Area (SA), Volume (V), and Crown Width (CW), this study develops ML regression models for estimating individual tree AGB. We explore three objectives: (1) evaluating four regression models including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Feed-Forward Neural Network (FFNN); (2) sensitivity assessment of different geometric feature combinations on model accuracy; and (3) improving model robustness using Synthetic Minority Over-sampling Technique (SMOTE) data augmentation for addressing imbalanced data. Results show that the RF model outperforms others that achieved the lowest RMSE and most balanced residual distribution. CW was the strongest single predictor of AGB and, in combination with H, yielded to the most accurate results. This combination improved RMSE and R2 by 14.2% and 89.3% with respect to single-variable-based models. The integration of SMOTE and RF further improved model performance since it lowered RMSE by 225.6 kg (~22.1%) and increased R2 by 0.76 (~49.0%). This was particularly evident in underrepresented low and high AGB ranges. The proposed RF-SMOTE approach is a cost-effective and scalable approach for generating high-quality ground truth data to enable large-scale satellite-based biomass estimation and help forest carbon accounting and planning in cities and forests. Full article
(This article belongs to the Special Issue UAV Applications for Forest Management: Wood Volume, Biomass, and Mapping (Second Edition))
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