Geometric Targets for UAS Lidar

Wilkinson, Benjamin; Lassiter, H. Andrew; Abd-Elrahman, Amr; Carthy, Raymond R.; Ifju, Peter; Broadbent, Eben N.; Grimes, Nathan

doi:10.3390/rs11243019

Cited by 18 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More details can be found at the GatorEye website (http://www.gatoreye.org). Absolute point accuracy has been tested using ground‐surveyed checkpoints, showing a root mean square error (RMSE) of 5 cm (Wilkinson et al., 2019). The autonomous flight was programmed to take place at a speed of 8 m/s at 60 m above ground.…”

Section: Methodsmentioning

confidence: 99%

Detecting successional changes in tropical forest structure using GatorEye drone‐borne lidar

et al. 2020

View full text Add to dashboard Cite

Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables' relationship to | 1157 de ALMeIdA et AL.

show abstract

Section: Methodsmentioning

confidence: 99%

Detecting successional changes in tropical forest structure using GatorEye drone‐borne lidar

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It has a 360-degree field of view, is capable of capturing 600,000 dual-return pulses per second, and has a maximum laser range of 220 m. The GatorEye uses an L1/L2 dual-frequency GNSS receiver for generating post processing kinematic (PPK) trajectories through integration with on-site base station data in Novatel Inertial Explorer software (Almeida et al, 2019). The system is capable of an absolute accuracy in the order of 3 cm RMSE [49]. In addition, the GatorEye includes a hyperspectral camera and visual sensor for lidar colorization which were not used in the current study.…”

Section: Lidar Datamentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…As for the non‐transformed point cloud, it results in the largest errors in training and validation, although it provides slightly smaller errors than the polynomial‐transformed point cloud in the test phase. The errors in the test phase obtained with a planar transformation are only slightly larger than the sum of the vertical error of the instruments used in the RTK‐GPS (RMSE~2 cm) and in the LiDAR survey (RMSE ~3 cm) (Wilkinson et al ., 2019). This confirms the good predictive capabilities of the method.…”

Section: Resultsmentioning

confidence: 99%

A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds

Pinton

Canestrelli

Wilkinson

et al. 2020

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

Salt marshes are transitional zones between ocean and land, which act as natural buffers against coastal hazards. The survival of salt marshes is governed by the rate of organic and inorganic deposition, which strongly depends on vegetation characteristics, such as height and density. Vegetation also favours the dissipation of wind waves and storm surges. For these reasons, an accurate description of both ground elevation and vegetation characteristics in salt marshes is critical for their management and conservation. For this purpose, airborne LiDAR (light detection and ranging) laser scanning has become an accessible and cost-effective tool to map salt marshes quickly. However, the limited horizontal resolution (~1m) of airborne-derived point clouds prevents the direct extraction of ground elevation, vegetation height and vegetation density without the coupling with imagery datasets. Instead, due to the lower flight altitude, UAV (unmanned aerial vehicle)-borne laser scanners provide point clouds with much higher resolution (~5cm). Although methods for estimating ground level and vegetation characteristics from UAV LiDAR have been proposed for flat ground, we demonstrate that a sloping ground increases prediction errors. Here we derive a new formulation that improves the estimation by employing a correction based on a LiDAR-derived estimate of local ground slope. Our method directly converts the 3D distribution of UAV LiDAR-derived points into vegetation density and height, as well as ground elevation, without the support of additional datasets. The proposed formulation is calibrated by using measured density and height of Spartina alterniflora in a marsh in Sapelo Island, Georgia, USA, and successfully tested on an independent dataset. Our method produces high-resolution (40×40cm 2) maps of ground elevation and vegetation characteristics, thus capturing the large gradients in the proximity of tidal creeks.

show abstract

Geometric Targets for UAS Lidar

Cited by 18 publications

References 14 publications

Detecting successional changes in tropical forest structure using GatorEye drone‐borne lidar

Detecting successional changes in tropical forest structure using GatorEye drone‐borne lidar

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

A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds

Contact Info

Product

Resources

About