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<p>Forest ecosystems provide essential services for both humans and nature, yet their sustainable management requires precise, large-scale monitoring to ensure their long-term integrity. While deep learning (DL) and computer vision (CV) offer transformative potential, their methodological advances must be firmly embedded within ecological questions to meaningfully advance forest monitoring, management, habitat reconstruction and conservation of biodiversity. A systematic review of 190 peer-reviewed studies (2011–2026) was conducted to evaluate how DL architectures—including 3D-Convolutional Neural Networks and Vision Transformers (ViTs)—transition from computational novelties to operational technologies for ecological applications. Their efficacy was assessed across critical ecological tasks, including biomass estimation, species classification, and disease detection using multi-sensor data fusion (Light Detection and Ranging [LiDAR], Unmanned Aerial Vehicle [UAV], hyperspectral imagery). Quantitative meta-analysis reveals that ViT-based models achieve a pooled species-classification accuracy of 96.3% (95% CI: 95.0–97.5%), outperforming standard convolutional neural networks (CNNs) (91.4%). However, the synthesis identifies three critical barriers to operational deployment: an absence of standardized benchmarking (73% of studies), a "transferability paradox" causing 20–45% performance degradation when applied across diverse biomes, and prohibitive computational overhead preventing real-time, edge-device field interventions. To bridge the deployment gap between computational research and applied ecological engineering, a computational complexity-performance trade-off analysis is introduced. Furthermore, a practitioner’s decision framework is proposed to strategically align hardware constraints with the logistical realities of field deployment. The novelty and rigor of this review lie in moving beyond static test-set accuracy, providing a robust roadmap to deploy transparent, climate-resilient artificial intelligence (AI) solutions for sustainable global forest ecosystem monitoring, management and conservation.</p>

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Deep learning-based computer vision in forest monitoring and management: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Gabriel Osei Forkuo,
  • Stelian Aleandru Borz

摘要

Forest ecosystems provide essential services for both humans and nature, yet their sustainable management requires precise, large-scale monitoring to ensure their long-term integrity. While deep learning (DL) and computer vision (CV) offer transformative potential, their methodological advances must be firmly embedded within ecological questions to meaningfully advance forest monitoring, management, habitat reconstruction and conservation of biodiversity. A systematic review of 190 peer-reviewed studies (2011–2026) was conducted to evaluate how DL architectures—including 3D-Convolutional Neural Networks and Vision Transformers (ViTs)—transition from computational novelties to operational technologies for ecological applications. Their efficacy was assessed across critical ecological tasks, including biomass estimation, species classification, and disease detection using multi-sensor data fusion (Light Detection and Ranging [LiDAR], Unmanned Aerial Vehicle [UAV], hyperspectral imagery). Quantitative meta-analysis reveals that ViT-based models achieve a pooled species-classification accuracy of 96.3% (95% CI: 95.0–97.5%), outperforming standard convolutional neural networks (CNNs) (91.4%). However, the synthesis identifies three critical barriers to operational deployment: an absence of standardized benchmarking (73% of studies), a "transferability paradox" causing 20–45% performance degradation when applied across diverse biomes, and prohibitive computational overhead preventing real-time, edge-device field interventions. To bridge the deployment gap between computational research and applied ecological engineering, a computational complexity-performance trade-off analysis is introduced. Furthermore, a practitioner’s decision framework is proposed to strategically align hardware constraints with the logistical realities of field deployment. The novelty and rigor of this review lie in moving beyond static test-set accuracy, providing a robust roadmap to deploy transparent, climate-resilient artificial intelligence (AI) solutions for sustainable global forest ecosystem monitoring, management and conservation.