The Fusion of Spectral and Structural Datasets Derived from an Airborne Multispectral Sensor for Estimation of Pasture Dry Matter Yield at Paddock Scale with Time
Senani Karunaratne, Anna Thomson, Elizabeth Morse-McNabb, Jayan Wijesingha, Dani Stayches, Amy Copland, Joe Jacobs
REMOTE SENSING | MDPI | Published : 2020
This study aimed to develop empirical pasture dry matter (DM) yield prediction models using an unmanned aerial vehicle (UAV)-borne sensor at four flying altitudes. Three empirical models were developed using features generated from the multispectral sensor: Structure from Motion only (SfM), vegetation indices only (VI), and in combination (SfM+VI) within a machine learning modelling framework. Four flying altitudes were tested (25 m, 50 m, 75 m and 100 m) and based on independent model validation, combining features from SfM+VI outperformed the other models at all heights. However, the importance of SfM-based features changed with altitude, with limited importance at 25 m but at all higher a..View full abstract
This research was funded by Agriculture Victoria, Dairy Australia, and the Gardiner Foundation.