Remote‐Sensing Innovations for Mapping Biodiversity Patterns and Forest Ecosystem Functions

Forests are among the most biologically diverse and functionally complex ecosystems on Earth, yet they are experiencing unprecedented pressures from deforestation, land-use change, climate variability, and increasing disturbance regimes. Understanding spatial and temporal patterns of forest biodiversity and ecosystem functions is therefore critical for conservation, climate mitigation, and sustainable forest management. This chapter examines how recent innovations in remote sensing have transformed biodiversity assessment and ecosystem function mapping from localized, field-based observations to scalable, spatially explicit, and dynamic analyses. It synthesizes the theoretical foundations and practical applications of optical, hyperspectral, thermal, light detection and ranging (LiDAR), radar, and unmanned aerial system (UAS) data for capturing taxonomic, functional, and structural dimensions of forest biodiversity. The chapter highlights how spectral heterogeneity, canopy structural complexity, and time-series vegetation dynamics serve as ecologically meaningful proxies for species richness, functional diversity, productivity, biomass, and carbon cycling. Advances in active sensors such as LiDAR and synthetic aperture radar have substantially improved the estimation of forest structure and carbon stocks, particularly in dense and cloud-prone regions, while hyperspectral and high-resolution optical data enable functional trait and phenological analyses. The integration of multi-sensor remote sensing with machine learning, artificial intelligence, and data fusion techniques is shown to enhance predictive accuracy and reduce uncertainty in biodiversity–ecosystem function assessments. The chapter also discusses current limitations, including sensor saturation, scaling challenges, and the need for ecological interpretability, and outlines future directions toward integrated, multi-scale monitoring systems. Overall, this work demonstrates that remote sensing has become a central scientific framework for forest ecology, providing essential insights for biodiversity conservation, ecosystem resilience assessment, and evidence-based environmental policy under accelerating global change.