Tropical forests are the most plant-diverse ecosystems on Earth, characterized by extremely high species richness and playing essential roles in ecosystem stability, carbon sequestration, and hydrological regulation. Although remote sensing has been widely applied to monitoring tropical forest plant diversity in recent decades, a systematic understanding of its actual monitoring capacity remains limited. Based on a bibliometric analysis of 15,878 publications from 1960 to 2025, this study draws several key conclusions: (1) Global research is highly unevenly distributed, with most studies concentrated in China’s tropical monsoon forests, Brazil’s Amazon rainforest, Costa Rica’s tropical rainforests, and Mexico’s tropical dry forests, while many other regions remain understudied; (2) The Sentinel-2 and Landsat series are the most widely used satellite sensors, and indirect indicators are applied more frequently than direct spectral metrics in monitoring models. Hyperspectral data, Light Detection and Ranging (LiDAR), and nonlinear models generally achieve higher accuracy than multispectral data, Synthetic Aperture Radar (SAR), and linear models; (3) Sampling scales range from 64 m2 to 1600 ha, with the highest accuracy achieved when plot size is within 400 m2 < Area ≤ 2500 m2, and spatial resolutions below 10 m perform best. Based on these findings, we propose four priority directions for future research: (1) Quantifying spectral indicators and models; (2) Assessing the influence of canopy structure on biodiversity remote sensing accuracy; (3) Strengthening the application of high-resolution data and reducing intraspecific spectral variability; and (4) Enhancing functional diversity monitoring and advancing research on the relationship between biodiversity and ecosystem functioning.
Sun et al. (Thu,) studied this question.