Biodiversity Assessment and Monitoring

Biodiversity assessment and monitoring are essential for understanding ecosystem health, detecting environmental change, and guiding sustainable conservation strategies. Rapid biodiversity loss caused by habitat destruction, climate change, pollution, and overexploitation has intensified the need for reliable, continuous, and scalable monitoring systems. Recent advances in Artificial Intelligence (AI), including machine learning, remote sensing, and geospatial analytics, have significantly improved the efficiency and accuracy of biodiversity monitoring across large spatial and temporal scales. Parallel to these technological developments, Indian Knowledge Systems (IKS) represent a rich body of traditional ecological knowledge rooted in long-term observation, cultural practices, and sustainable resource management. This chapter examines the concepts, methods, and indicators of biodiversity assessment and monitoring, emphasizing the integration of AI-based tools with Indian Knowledge Systems. It highlights how combining modern technology with indigenous wisdom can enhance ecological understanding, support community participation, and strengthen conservation outcomes. Case studies from India illustrate practical applications of AI-assisted and community-driven monitoring approaches. The chapter also discusses challenges, ethical concerns, and future prospects for developing inclusive, resilient, and policy-relevant biodiversity monitoring frameworks.