Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments
The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting...
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MDPI AG
2025-05-01
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| author | Ricardo Abreu-Dias Juan M. Santos-Gago Fernando Martín-Rodríguez Luis M. Álvarez-Sabucedo |
| author_facet | Ricardo Abreu-Dias Juan M. Santos-Gago Fernando Martín-Rodríguez Luis M. Álvarez-Sabucedo |
| author_sort | Ricardo Abreu-Dias |
| collection | DOAJ |
| description | The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. However, significant challenges persist, particularly in heterogeneous forest environments with high species diversity and complex canopy structures. This systematic review explores the latest research on drone-based data collection and AI-driven classification techniques, focusing on studies that classify specific tree species rather than generic tree detection. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, peer review studies from the last decade were analyzed to identify trends in data acquisition instruments (e.g., RGB, multispectral, hyperspectral, LiDAR), preprocessing techniques, segmentation approaches, and machine learning (ML) algorithms used for classification. Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. The integration of LiDAR with hyperspectral imaging further enhances classification accuracy but remains limited due to cost constraints. Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. This review provides a comprehensive synthesis of existing methodologies, highlighting both advancements and persistent gaps in AI-driven forest monitoring. |
| format | Article |
| id | doaj-art-22c328e7b96843d2b22b59ead3c58144 |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-22c328e7b96843d2b22b59ead3c581442025-08-20T03:48:01ZengMDPI AGTechnologies2227-70802025-05-0113518710.3390/technologies13050187Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest EnvironmentsRicardo Abreu-Dias0Juan M. Santos-Gago1Fernando Martín-Rodríguez2Luis M. Álvarez-Sabucedo3Department of Computer Engineering and Multimedia, Polytechnic Institute of Viana do Castelo (IPVC), 4900-347 Viana do Castelo, PortugalatlanTTic (Research Center for Telecommunication Technologies), University of Vigo, 36310 Vigo, SpainatlanTTic (Research Center for Telecommunication Technologies), University of Vigo, 36310 Vigo, SpainatlanTTic (Research Center for Telecommunication Technologies), University of Vigo, 36310 Vigo, SpainThe classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. However, significant challenges persist, particularly in heterogeneous forest environments with high species diversity and complex canopy structures. This systematic review explores the latest research on drone-based data collection and AI-driven classification techniques, focusing on studies that classify specific tree species rather than generic tree detection. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, peer review studies from the last decade were analyzed to identify trends in data acquisition instruments (e.g., RGB, multispectral, hyperspectral, LiDAR), preprocessing techniques, segmentation approaches, and machine learning (ML) algorithms used for classification. Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. The integration of LiDAR with hyperspectral imaging further enhances classification accuracy but remains limited due to cost constraints. Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. This review provides a comprehensive synthesis of existing methodologies, highlighting both advancements and persistent gaps in AI-driven forest monitoring.https://www.mdpi.com/2227-7080/13/5/187drone technologyunmanned aerial vehiclesartificial intelligencemachine learningdeep learningtree species classification |
| spellingShingle | Ricardo Abreu-Dias Juan M. Santos-Gago Fernando Martín-Rodríguez Luis M. Álvarez-Sabucedo Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments Technologies drone technology unmanned aerial vehicles artificial intelligence machine learning deep learning tree species classification |
| title | Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments |
| title_full | Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments |
| title_fullStr | Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments |
| title_full_unstemmed | Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments |
| title_short | Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments |
| title_sort | advances in the automated identification of individual tree species a systematic review of drone and ai based methods in forest environments |
| topic | drone technology unmanned aerial vehicles artificial intelligence machine learning deep learning tree species classification |
| url | https://www.mdpi.com/2227-7080/13/5/187 |
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