Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

Abstract

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

“…It occurs because UAV-derived CH data, unlike point-wise manually measured ones, represent a mean plant height value of an entire plot accounting for the plant density resulting from zonal statistics. Another solution to provide CH data for reliably estimating crop traits might be utilizing UAV-LiDAR data [71][72][73] and should be investigated in future research.…”

Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR

“…For example, Wang et al [74], using a slope-and angle-based filtering method [75] to classify UAV-LiDAR point clouds into ground and grassland, found that LiDAR underestimated the canopy height, whereas at the locations where the grassland was less than 5 cm, LiDARderived heights were overestimated. To separate ground points from points representing winter wheat as acquired by UAV-LiDAR [33], the authors argued that the cloth simulation filtering algorithm [76] had to be parameterized according to the temporal variations in the vegetation density. Since criteria for choosing the most appropriate filtering method and the optimized parameters of every filtering algorithm are lacking, we used the calculation of the total errors of morphological and interpolated-based filtering algorithms by setting different values of their parameters.…”

“…In a similar study, the 3D characterization of individual plant species of a shrubland area was achievable at the submeter scale using a UAV-LiDAR system [32]. However, the technology of UAV-LiDAR is not currently used in precision farming, despite its ability to effectively monitor canopy density [33] and fine-scale variations in crops attributes compared to UAV-optical imagery [34][35][36], which is widely employed in such applications [37][38][39][40].…”

UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds

“…Unmanned aerial vehicles (UAVs) equipped with high-resolution imaging sensors, LiDAR, multi-spectral, and hyperspectral cameras [15][16][17][18][19][20][21] have been widely used for supporting precision agriculture and digital farming applications, such as plant phenotyping [22,23], leaf area density (LAD) [24,25], leaf chlorophyll content (LCC) [26], and breeding [27] due to their versatility, flexibility, and low operational costs. A conceptual illustration of a UAV-based image acquisition system for estimation of crop parameters along with other in situ sensors and manual measurements is shown in Figure 1.…”