Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture

Abstract

3D geodata play an increasingly important role in precision agriculture, e.g., for modeling in-field variations of grain crop features such as height or biomass. A common data capturing method is LiDAR, which often requires expensive equipment and produces large datasets. This study contributes to the improvement of 3D geodata capturing efficiency by assessing the effect of reduced scanning resolution on crop surface models (CSMs). The analysis is based on high-end LiDAR point clouds of grain crop fields of different varieties (rye and wheat) and nitrogen fertilization stages (100%, 50%, 10%). Lower scanning resolutions are simulated by keeping every n-th laser beam with increasing step widths n. For each iteration step, high-resolution CSMs (0.01 m2 cells) are derived and assessed regarding their coverage relative to a seamless CSM derived from the original point cloud, standard deviation of elevation and mean elevation. Reducing the resolution to, e.g., 25% still leads to a coverage of >90% and a mean CSM elevation of >96% of measured crop height. CSM types (maximum elevation or 90th-percentile elevation) react differently to reduced scanning resolutions in different crops (variety, density). The results can help to assess the trade-off between CSM quality and minimum requirements regarding equipment and capturing set-up.

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

“…The methodology of spatial plant height mapping can be scaled to field level, as long as the maximum range of the scanner is regarded and the point density is above the required minimum. As shown by Hä mmerle and Höfle [63], the coverage of the field and attained mean heights are influenced by the point density. The approach of pixel-wise calculating plant height from TLS-derived CSMs has already shown good results at the field level for monitoring a maize field, about 80 m by 160 m in size [74] and a sugar beet field, about 300 m by 500 m in size [43] captured from four and eight scan positions, respectively.…”

“…Usually, the factors time and cost have a major influence on choosing a system. As shown by Hä mmerle and Höfle [63] the appropriate point density for generating a CSM varies depending on the application. In further studies, cost-efficient systems, such as the Velodyne HDL-64E [64], should be considered to investigate their potential for capturing crop surfaces in an adequate resolution.…”

Method for estimating rice plant height without ground surface detection using laser scanner measurement

“…It is possible to determine the ground surface shortly before transplanting or after harvesting the crop; however, in this study, the laser pulse easily reaches the rice canopy, and therefore we choose a new reference point at the top of the rice plant to determine H. The proposed method utilizes percentile analysis, which has been commonly used for analyzing plant heights obtained with a laser scanner. 25,26 Therefore, a procedure using percentile ranks and vertical distance was performed, whereby the closest percentiles to the top (p t ) and bottom (p b ) of the rice plant were determined. On the basis of this, the position of the top (D t ) and the bottom of the rice plant (D b ) were then located (Fig.…”

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

“…Previous studies focused on the acquisition of plant height [25], post-harvest growth [26], leaf area index [27], crop density [28,29], nitrogen status [30], or the detection of individual plants [31,32]. Moreover, the potential of TLS for estimating the biomass of small-grain cereals was emphasized [33][34][35][36]. Regarding the accuracy, Lumme et al [33] found that estimated heights of cereal plants correlated with tape measurements.…”