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

Hiroyuki Obanawa,Rena Yoshitoshi,Seiichi Sakanoue,Nariyasu Watanabe

doi:10.3390/s20174809

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.

Highlights

In order to increase forage yield and quality in grassland farming, it is necessary to decide the appropriate harvest time based on accurate growth assessment
We considered the creation of the digital surface model (DSM) of the surface of grass-canopy, and the ground used for grass height calculation
When converting height information from the point cloud to DSM for grass height measurement by structure from motion (SfM) and light detection and ranging (LiDAR) methods, we found that accuracy was maximized by using the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting as data for each DSM cell

Summary

Introduction

In order to increase forage yield and quality in grassland farming, it is necessary to decide the appropriate harvest time based on accurate growth assessment. Grass height, which is an important indicator of growth conditions, has been measured by various methods. The simplest, most economical and frequently used method is direct measurement with a ruler. It is time-consuming, and is not practicable for detailed plant height distribution with high-density measurement. The structure from motion and multi view stereo (SfM-MVS) photogrammetric method has been successfully used to estimate the height and biomass of plants. Cooper et al [1] carried out digital camera photography from eye height level, on 11 grass plots in

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