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. Recently, remotely sensed measurements of thenear-infrared reflectance (NIRv) of vegetation, the fluorescence correctionvegetation index (FCVI), and radiance (NIRvrad) of vegetation have emergedas indicators of vegetation structure and function with potential to enhanceor improve upon commonly used indicators, such as the normalized differencevegetation index (NDVI) and the enhanced vegetation index (EVI). Theapplicability of these remotely sensed indices to tropical forests, keyecosystems for global carbon cycling and biodiversity, has been limited. Inparticular, fine-scale spatial and temporal heterogeneity of structure andphysiology may contribute to variation in these indices and the propertiesthat are presumed to be tracked by them, such as gross primary productivity(GPP) and absorbed photosynthetically active radiation (APAR). In thisstudy, fine-scale (approx. 15 cm) tropical forest heterogeneity represented byNIRv, FCVI, and NIRvrad and by lidar-derived height is investigated andcompared to NIRv and EVI using unoccupied aerial system (UAS)-basedhyperspectral and lidar sensors. By exploiting near-infrared signals, NIRv,FCVI, and NIRvrad captured the greatest spatiotemporal variability, followedby the enhanced vegetation index (EVI) and then the normalized differencevegetation index (NDVI). Wavelet analyses showed the dominant spatial scaleof variability of all indicators was driven by tree clusters andlarger-than-tree-crown size gaps rather than individual tree crowns. NIRv,FCVI, NIRvrad, and EVI captured variability at smaller spatial scales(∼ 50 m) than NDVI (∼ 90 m) and the lidar-basedsurface model (∼ 70 m). We show that spatial and temporalpatterns of NIRv and FCVI were virtually identical for a dense green canopy,confirming predictions in earlier studies. Furthermore, we show thatNIRvrad, which does not require separate irradiance measurements, correlatedmore strongly with GPP and PAR than did other indicators. NIRv, FCVI, andNIRvrad, which are related to canopy structure and the radiation regime ofvegetation canopies, are promising tools to improve understanding oftropical forest canopy structure and function.

Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations