Tropical forests are the most biodiverse ecosystems on Earth and play a critical role in climate regulation, carbon storage, and the maintenance of ecosystem services. Yet, they are also among the most threatened, facing accelerating pressures from deforestation, land-use change, and climate variability. To safeguard these ecosystems, it is increasingly necessary to integrate technological innovation with local ecological knowledge and community engagement, enabling data-driven and socially inclusive conservation strategies.

Recent advances in remote sensing, drone technology, artificial intelligence (AI), the Internet of Things (IoT), and big data analytics have transformed how we observe, analyze, and restore tropical forests. These emerging tools provide unprecedented capabilities for high-resolution biodiversity monitoring, species and habitat mapping, and real-time detection of ecosystem change. When integrated into conservation planning, such technologies can improve the accuracy of restoration assessments, enhance early-warning systems for forest degradation, and support adaptive management based on ecological and social indicators.

Beyond technical efficiency, these innovations have profound implications for how science interacts with policy and people. In tropical regions, where monitoring resources are often limited, low-cost drones, open-source AI models, and participatory mapping platforms have allowed local communities and Indigenous Peoples to take a more active role in ecosystem management. The co-production of knowledge that merges Traditional Ecological Knowledge (TEK) with digital monitoring tools represents a promising frontier for building equitable, resilient, and context-adapted conservation systems.

Nevertheless, significant challenges remain. Technological approaches to forest conservation often operate in isolation from local governance structures, social dynamics, and ecological theory. The absence of standardized biodiversity metrics, interoperable data platforms, and scalable frameworks hinders the integration of these innovations into long-term conservation policies. Addressing these gaps requires a systemic and interdisciplinary approach, capable of aligning technology-based solutions with global frameworks such as the Kunming–Montreal Global Biodiversity Framework (GBF), the Paris Agreement, and the Sustainable Development Goals (SDGs 13, 14, and 15).

This Research Topic seeks to explore how emerging technologies can advance the monitoring, conservation, and restoration of tropical forests, while strengthening human-forest relationships. We welcome contributions, including original research, reviews, data reports, perspectives, policy, and reviews, that address (but are not limited to):

• Applications of drones, remote sensing, and AI for automated biodiversity monitoring and forest restoration;

• Predictive modeling and machine learning for species detection, ecosystem change, and resilience assessment;

• Integration of ecological, geospatial, and socio-economic data for adaptive management of tropical forests;

• Participatory monitoring and TEK integration for community-based conservation;

• Policy, governance, and ethical frameworks for technology-driven forest management;

• Case studies of innovative projects in tropical regions linking technology, restoration, and human well-being.

By promoting dialogue between researchers, data scientits, community practitioners and decision-makers, this Topic seeks to define a new frontier of technological and inclusive conservation, where emerging tools strengthen, rather than replace, human stewardship of tropical forests. The ultimate goal is to foster scalable, equitable, and evidence-based frameworks that contribute to the resilience of tropical ecosystems and the people who depend on them.

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Emerging Technologies for Biodiversity Conservation in Tropical Forests: From Remote Sensing to Artificial Intelligence for Monitoring, Restoration, and Community-Based Stewardship