UAV-Based Multispectral Time-Series Imagery of Biomass Sorghum — 2019

Themes: Feedstock Production

Keywords: Biomass Analytics

Citation

Varela Quintela, S., Leakey, A.D.B. May 10, 2021. “UAV-Based Multispectral Time-Series Imagery of Biomass Sorghum — 2019.” University of Illinois Urbana-Champaign. DOI: 10.13012/B2IDB-0353090_V1.

Overview

Spatial extraction of geometric and spectral feature at each plot, temporal integration and smoothing via splines, extraction of time-point and dynamics features from spline continuous solution from each feature. Random Forest implementation for determination of variable importance and above-ground biomass (AGB) prediction. This last step is implemented for time-point and dynamic features at each of the predefined date as predictors of end-of-season AGB.

UAV-based high-resolution multispectral time-series orthophotos utilized to understand the relation between growth dynamics, imagery temporal resolution, and end-of-season biomass productivity of biomass sorghum as bioenergy crop. Sensor utilized is a RedEdge Micasense flown at 40 meters above ground level at the Energy Farm at University of Illinois Urbana-Champaign in 2019.

Data

3D Reconstructed Orthophotos (234 MB)

Spectral Orthophotos (6.38 GB)

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