Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass

Abstract

Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs). Images captured with red–green–blue cameras mounted on UAVs in two missions varying in ground sample distances were used as input for generating three-dimensional point clouds using the structure-from-motion approach. These point clouds were then compared to manual ground measurements. The results showed that the agreement between the methods was closest when CC presented dense and smooth canopies. However, stands with rough canopies or gaps showed substantial differences between the UAV method and ground measurements. We conclude that the UAV method is substantially more precise and accurate in determining CH than measurements taken with a ruler since the UAV introduces additional dimensions with greatly increased resolution. CH can be a reliable indicator of biomass yield, but no differences between the investigated methods were found, probably due to allometric variations of different CC species. We propose the presented UAV method as a promising tool to include site-specific information on CC in crop production strategies.

Plant phenotyping relevance

UAV画像とSfM点群を用いて被覆作物の群落高を推定する手法を提示し、地上測定と比較検証しており、植物形質取得法が研究の中心である。

Code and data availability

The paper's canopy height and biomass measurements (UAV-derived CH, ruler measurements, DMY) are openly available as a Zenodo dataset, explicitly stated in the Data Availability Statement. The Metashape scripts GitHub link and CRAN raster package are generic third-party tools, not authors' analysis code.