Estimating integrated measures of forage quality for herbivores by fusing optical and structural remote sensing data

• Published in: Environmental Research Letters
• Main Authors: J S Jennewein, J U H Eitel, K Joly, R A Long, A J Maguire, L A Vierling, W Weygint
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2021
• Subjects: hyperspectral; structure-from-motion; vegetation indices; integrated forage quality metrics; Arctic; wildlife management; Environmental technology. Sanitary engineering; TD1-1066; Environmental sciences; GE1-350; Science; Q; Physics; QC1-999; Climate change; Alaska
• Online Access: https://doi.org/10.1088/1748-9326/ac09af; https://doaj.org/article/8b76f17934894d7bb761b811464c1094

Northern herbivore ranges are expanding in response to a warming climate. Forage quality also influences herbivore distributions, but less is known about the effects of climate change on plant biochemical properties. Remote sensing could enable landscape-scale estimations of forage quality, which is of interest to wildlife managers. Despite the importance of integrated forage quality metrics like digestible protein (DP) and digestible dry matter (DDM), few studies investigate remote sensing approaches to estimate these characteristics. We evaluated how well DP and DDM could be estimated using hyperspectral remote sensing and assessed whether incorporating shrub structural metrics affected by browsing would improve our ability to predict DP and DDM. We collected canopy-level spectra, destructive-vegetation samples, and flew unoccupied aerial vehicles (UAVs) in willow ( Salix spp.) dominated areas in north central Alaska in July 2019. We derived vegetation canopy structural metrics from 3D point cloud data obtained from UAV imagery using structure-from-motion photogrammetry. The best performing model for DP included a spectral vegetation index (SVI) that used a red-edge and shortwave infrared band, and shrub height variability (hvar; Nagelkerke R ^2 = 0.81, root mean square error RMSE = 1.42%, cross validation ρ = 0.88). DDM’s best model included a SVI with a blue and a red band, the normalized difference red-edge index, and hvar (adjusted R ^2 = 0.73, RMSE = 4.16%, cross validation ρ = 0.80). Results from our study demonstrate that integrated forage quality metrics may be successfully quantified using hyperspectral remote sensing data, and that models based on those data may be improved by incorporating additional shrub structural metrics such as height variability. Modern airborne sensor platforms such as Goddard’s LiDAR, Hyperspectral & Thermal Imager provide opportunities to fuse data streams from both structural and optical data, which may enhance our ability to estimate and scale important foliar ...