Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

Abstract

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

UAS-Based Plant Phenotyping for Research and Breeding Applications

“…UAS-mounted LiDAR-based phenotyping has been used for the estimation of canopy biomass and plant height, for example, canopy height in winter wheat to the effect on nitrogen fertilizer rates [ 64 ], sugarcane biomass estimation [ 65 ], and maize height tracking in lodged plots [ 66 ]. The current challenges with routine utilization of LiDAR on UAS are the cost vs. quality trade-off of data [ 67 ]. Additional challenges include data processing standardization and the large size of the data.…”

“…The current challenges with routine utilization of LiDAR on UAS are the cost vs. quality trade-off of data [67]. Additional challenges include data processing standardization and the large size of the data.…”

Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System

“…The recent development of sensor technology for mobile mapping systems in the field of airborne laser scanning offers the possibility to operate compact profile line scanners on an Unmanned Aerial Vehicle (UAV) [6]. The advantages of using a combination of UAV and laser scanner are numerous compared to traditional airborne laser scanning, and benefit, in particular, from a higher level of detail and more flexible flight planning.…”

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

“…As verification projects often balance the need for accuracy with project cost [21], the fusion of these two methods provides the flexibility to leverage plot-level observations to gain insights into the wider treatment area. This method should also become more accessible, particularly with the increased availability of multiple LiDAR acquisitions and the use of UAV-based LiDAR systems [65].…”