Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

Abstract

Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectral bands and associated vegetation indices were used as predictors. We found that for most dense canopy grasses, using a simple linear model structure could explain most (R2 0.7) of the variability of the response variable. This was not the case for sparse canopy grasses, where a full multispectral dataset and a non-parametric model approach (random forest) were required to obtain a maximum R2 of 0.53. We developed transparent protocols to model forage yield where, in most circumstances, acceptable results could be obtained with affordable RGB sensors and UAV platforms. This is important as users can obtain rapid estimates with inexpensive sensors for most of the grasses included in this study.