Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

Abstract

The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points.

Plant phenotyping relevance

UAS-SfMとLiDARによる作物形状計測を比較・検証し、作物高と畝幅という植物形質の精度を評価しており、表現型取得手法が中心である。

Code and data availability

The supplied blocks describe UAS SfM/LiDAR point cloud collection and processing for snap bean structural phenotyping, but contain no public phenotype/trait dataset, imagery deposit, author analysis code, or trained model. All referenced URLs (CloudCompare, VDatum, LAStools, Propeller AeroPoints help page, Velodyne/GVI

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