Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Abstract

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data

“…To predict biophysically important forest attributes such as basal area, mean stem diameter, and AGB, LiDAR measurements derived from point clouds can be used in empirical models [57][58][59][60]. In our study, the metrics selected to compose the models are corroborated by previous studies [59,61].…”

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

“…However, if we are willing to adopt a different perspective, that views the potential reduction of fieldwork cost as compensation for ALS data acquisition, then multiplying the ALS data collection frequency can be treated as a reasonable initiative. Data fusion techniques that integrate LiDAR with other more affordable methods such as unmanned aerial vehicle (UAV) remote sensing or other low-cost, available cutting-edge technologies, can be deemed as an interesting strategy having great potential for forest plantation assessment [11,[72][73][74][75].…”

“…Light Detection and Ranging (LiDAR) remote sensing has been established as one of the more promising tools for broad-scale forest monitoring [9,10]. LiDAR data can be used to characterize local to regional spatial extents with high enough resolution to quantify the three-dimensional information of vertical and horizontal forest structures and the underlying topography, with the support of efficiently collected field data and several statistical methods [11][12][13][14].…”

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

“…In terms of the choice of sensor, light detection and ranging (LiDAR) is a promising technology to measure plant height and predict biomass [10]. LiDAR equipped UAVs have been demonstrated to work well in forestry [11,12], maize [13], and shorter annual crops such as wheat [14]. There is one study using tractor mounted LiDAR to characterize a bioenergy crop, Miscanthus giganteus, with good results (reported accuracy 92-98.2%) [15].…”