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.
Highlights
Unmanned aerial systems/vehicles (UASs/UAVs), paired with structure-from-motion (SfM) image processing workflows, have lately emerged as a popular strategy for various geoscience applications [1,2]
The results revealed that the SfM point clouds, despite their relatively low point density, could provide high-quality digital elevation models (DEMs) and crop height models (CHMs), which were comparable to their light detection and ranging (LiDAR) counterparts
We found that both SfM and LiDAR point clouds achieved a high accuracy for assessment of crop height (CH) and row width (RW)—we obtained root mean squared error (RMSE) of ~0.02 m for CH
Summary
Unmanned aerial systems/vehicles (UASs/UAVs), paired with structure-from-motion (SfM) image processing workflows, have lately emerged as a popular strategy for various geoscience applications [1,2]. In precision agriculture, the SfM point cloud approach can be used to derive structural parameters of crops, such as plant height [3,4], canopy volume [5,6], and leaf area coverage [7,8], all of which could significantly help farmers to enhance agricultural management decisions [9]. Another widely used approach to generating 3D point clouds is light detection and ranging (LiDAR). LiDAR point clouds have obvious advantages when compared with SfM point clouds, including high point density, robustness to illumination changes, and the ability to obtain below-canopy information, because of its multiple return
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