Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods

Abstract

Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50–100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.

A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland

“…The time it takes for each emitted light beam to be reflected back to the LiDAR sensor receiver is used to develop a point cloud dataset for each target object. Obanawa et al [117] reported an average absolute error of 12 mm (±10 mm) (R 2 = 0.93) at a 20 mm resolution for LiDAR prediction of grass height on Italian ryegrass pasture in Japan. Disadvantages of LiDAR include its relatively high cost and susceptibility to high measurement error in windy conditions [118,119].…”

Proximal Sensing in Grasslands and Pastures

“…Photographic determinations of pasture height may be carried out with a simple red-green-blue (RGB) camera mounted onto a UAV or other vehicle [19] or held on a pole [20]. Ground control points (GCPs) are high-visibility objects used to provide a frame of reference for determining the position of the UAV in the pasture being photographed and are georeferenced using GPS after they are placed in the pasture [21].…”

“…Light detection and ranging (LiDAR) generates point clouds much like SfM techniques but relies on a different mechanism to do so. With LiDAR, a laser aimed at the ground bounces back to a sensor, which records the time it took for the light to return, and from this interval calculates the distance from the LiDAR equipment [20].…”

“…Once the point cloud has been generated, a DSM is developed. The calibration of biomass prediction from height data is similar to the process used in SfM, with an initial comparison of LiDAR-generated height data with hand-harvested biomass samples [20].…”

“…The primary advantage of LiDAR is its accuracy. Obanawa and colleagues reported a margin of error for measured pasture height of 12 ± 10 mm for LiDAR compared to 24 ± 13 mm for SfM [20]. Additionally, ultrasonic sensors usually assume a fixed distance to the ground due to being mounted on a vehicle, yet terrain and bumpy driving may interfere with the validity of that assumption [17].…”