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

Abstract

Abstract. Recently, remotely sensed measurements of the near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation have emerged as indicators of vegetation structure and function with potential to enhance or improve upon commonly used indicators, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). The applicability of these remotely sensed indices to tropical forests, key ecosystems for global carbon cycling and biodiversity, has been limited. In particular, fine-scale spatial and temporal heterogeneity of structure and physiology may contribute to variation in these indices and the properties that are presumed to be tracked by them, such as gross primary productivity (GPP) and absorbed photosynthetically active radiation (APAR). In this study, fine-scale (approx. 15 cm) tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad and by lidar-derived height is investigated and compared to NIRv and EVI using unoccupied aerial system (UAS)-based hyperspectral and lidar sensors. By exploiting near-infrared signals, NIRv, FCVI, and NIRvrad captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI) and then the normalized difference vegetation index (NDVI). Wavelet analyses showed the dominant spatial scale of variability of all indicators was driven by tree clusters and larger-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-based surface model (∼ 70 m). We show that spatial and temporal patterns of NIRv and FCVI were virtually identical for a dense green canopy, confirming predictions in earlier studies. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated more strongly with GPP and PAR than did other indicators. NIRv, FCVI, and NIRvrad, which are related to canopy structure and the radiation regime of vegetation canopies, are promising tools to improve understanding of tropical forest canopy structure and function.

The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

“…Based on the idea that the near‐infrared radiation reflected by plants is proportional to the PAR absorbed by plants, the near‐infrared reflectance of vegetation (NIR v ) has shown strong linear relationships with GPP and can be correlated with fAPAR (Badgley et al., 2017, 2019; Baldocchi et al., 2020; Wu et al., 2020). Furthermore, a radiance based (NIR v ) was also correlated with GPP and APAR across agricultural sites and tropical forest canopies (Merrick et al., 2021; Wu et al., 2020). On the ground, temperature‐respiration relationships and light response curves calculated from solar radiation incident on the surface are widely used to partition Net Ecosystem Exchange (NEE) from eddy covariance towers into GPP and ecosystem respiration (R eco ; Lasslop et al., 2010; Reichstein et al., 2012; Stoy et al., 2006).…”

New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave

“…The three vegetation indices (NDVI, EVI, and NIRv) showed comparable performance for estimating GPP. In the following analyses, we only provide the results from NDVI because it is a simple and most widely used vegetation index, and previous studies also showed its smaller diurnal variations and lower sensitivity to BRDF effects compared to EVI or NIRv ( 64 , 65 ).…”

Past human‐induced ecological legacies as a driver of modern Amazonian resilience

“…NDVI, Normalized Differential Vegetation Index; VOD, vegetation optical depth; UAS, unmanned aerial system. and near-infrared (c. 830-850 nm) data that can be converted to standard satellite-based resilience metrics such as NDVI (Merrick et al, 2021;Figure 5). Hyperspectral imagery on UASs can also be integrated with optical imagery and used to detect tree species (Nevalainen et al, 2017).…”