Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic Review

Environmental engineering relies on the precise, large-scale design and monitoring of ecosystems to ensure the mutual benefit of humans and nature. However, traditional forest assessment methods are constrained by limited spatial and temporal resolution, impeding dynamic habitat reconstruction and ecosystem rehabilitation. This paper presents a systematic review of 186 peer-reviewed articles (2011–2026) to evaluate how Deep Learning (DL) and Computer Vision (CV) are transitioning from observational tools to actionable ecotechnologies for forest restoration. By automating the extraction of multi-modal structural and spectral data, advanced architectures—such as 3D Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)—are directly empowering evidence-based ecological engineering tasks, including climate-resilient carbon accounting, the tracking of biodiversity shifts during habitat recovery, and early-stage disease mitigation. Quantitative meta-analysis reveals that ViT-based models achieve a pooled species-classification accuracy of 96.3% (95% CI: 95.0–97.5%), offering an absolute improvement of 4.9% over standard CNNs (91.4%). Despite these algorithmic advances, the review identifies three critical barriers to operational deployment in restoration ecology: (1) the absence of standardized benchmarking protocols (73% of studies), (2) a "transferability paradox" causing 23–45% performance degradation when models are applied across diverse ecological biomes, and (3) a profound lack of model interpretability. To bridge the gap between computational research and field-based ecosystem restoration, this study provides a novel computational complexity-performance trade-off analysis and a practitioner’s decision framework. These tools offer a roadmap to overcome edge-deployment limitations, enabling engineers and ecologists to implement robust, real-time AI solutions for the sustainable rehabilitation and management of global forest ecosystems.