AI and Machine Learning in Remote Sensing for Tropical Forest Monitoring: Applications, Challenges, and Emerging Solutions

Tropical forests are critical for global climate, biodiversity conservation, and supporting local livelihoods, yet they remain highly vulnerable to human-induced pressures (deforestation and degradation) and climate change impacts (diseases, fires, and drought). The overarching aim of this review is to assess how artificial intelligence (AI) and machine learning (ML) are transforming remote sensing-based monitoring of tropical forests, with a focus on their potential to enhance the detection and estimation of forest change and support tropical forest-related climate policy frameworks. The strengths of this review lie in its comprehensive synthesis of technical, institutional, and governance dimensions, achieved by systematically analyzing evidence from operational forest monitoring platforms and peer-reviewed literature (2010–2025). Using structured search and qualitative analysis, the review evaluates advances in AI/ML applications, identifies technical and institutional barriers, highlights emerging solutions, and provides practical, policy-relevant recommendations. This review identifies critical gaps and proposes a roadmap for scaling AI/ML for tropical forest monitoring. It finds that AI/ML tools, particularly supervised and unsupervised classifiers, deep learning models, time-series analytics, and multi-sensor data-fusion approaches, have become central to advancing remote sensing— enhancing accuracy, automation, and scalability for monitoring deforestation, forest degradation, biomass change, and forest dynamics. However, effective adoption of these technologies still faces persistent barriers—such as limited access to high-quality training data, reliance on proprietary platforms, technical capacity gaps, and unresolved ethical and governance challenges. The review concludes that overcoming these barriers through open training datasets, platform-agnostic infrastructures, capacity building, and inclusive governance is essential for scaling robust, transparent, and locally owned AI-enabled forest monitoring systems. Advances in AI/ML in remote sensing will support climate mitigation, biodiversity conservation, and equitable decision-making in tropical forest countries.