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.

Quality Assessment of DJI Zenmuse L1 and P1 LiDAR and Photogrammetric Systems: Metric and Statistics Analysis with the Integration of Trimble SX10 Data

“…However, a small note should be made regarding the economic aspect. Despite the actual panorama of aerial payloads being characterised by low-budget instruments [20][21][22][23][24], the L1 and P1 are not low-cost solutions, even if some payload sensors for UAVs/UASs have seen reductions in their prices allowing for new possibilities and strategies in surveying. In fact, in some cases the sensors' price can be a real drawback for surveys and research, also determining the outcome of projects.…”

“…Although the drone performed only a nadiral flight, the laser was able to penetrate well into the foliage. In fact, airborne LiDAR acquisitions are reliable strategies also for forest, tree, and crop mapping, keeping high levels of detail [19][20][21]. However, a more satisfactory result is given by the P1 sensor and photogrammetric process, which comprehensively reconstructed all of the vegetation details (Figure 33).…”

Evaluation of Leaf Area Index (LAI) of Broadacre Crops Using UAS-Based LiDAR Point Clouds and Multispectral Imagery

“…LiDAR sensing enables probing of both the horizontal and vertical structures of crops by actively emitting high-frequency laser pulses toward the object and recording the reflected responses as a function of time [46], [47]. While this design enables such active systems to capture more in-depth structural information than classical passive imagery sensors, it also creates specific challenges, such as complex data preprocessing, noise points, non-uniformity across different scanning angles, and a rapid increase in footprint size as the detection range increases.…”

A review of remote sensing for potato traits characterization in precision agriculture

“…Unmanned aerial vehicle (UAV)-based LiDAR systems have been applied to map the 3D structure of the plant canopy and topography of the landscape ( Lin et al, 2019 ; ten Harkel et al, 2020 ). UAV-based LiDAR devices were reported to deliver accurate estimations of plant height with the RMSE of 0.02, 0.07, and 0.12 m compared with the manual measurements on snap bean crops ( Zhang et al, 2021 ), sugar beet, and potato ( ten Harkel et al, 2020 ), respectively. However, LiDAR devices can be expensive and require longer imaging times, compared to other stereo vision methods from images for 3D mapping.…”

“…Precision agricultural management is the key to reducing environmental impact and enhancing agricultural sustainability ( Zhang et al, 2021 ). Within the context of potato management, a variety of activities are employed to ensure its productivity and quality throughout the growing cycle.…”