In this webinar, we discuss the planning and execution of manned airborne data acquisition. We will review noteworthy data quality and efficiency considerations to be aware of from an airborne perspective. Then we’ll take a look at flight planning for helicopter missions featuring Phoenix’s own flight planner. The final portion of the webinar will dive into the acquisition portion and introduce Mission Guidance, PLS’s new real-time pilot’s navigation aid and data technician’s quality control tool.

Phoenix LiDAR Systems are capable of much more than point-clouds! In this webinar we team up with our premier imaging partner, PhaseONE to showcase how two of the world’s leading aerial surveying companies are synergizing the industry to push the limits of what’s possible. Also we’ll go over our other advanced imaging systems including dual-oblique, hyperspectral, thermal, NIR, and more!

The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficult to use classical statistical techniques to design accurate predictive models from these data for conducting forest inventories. Machine learning techniques have the potential to solve this problem of modeling forest attributes from remotely sensed data. This work tests four different machine learning approaches – namely Support Vector Regression, Random Forest, Artificial Neural Networks, and Ex-

treme Gradient Boosting – on high-density GatorEye UAV-Lidar point clouds for indirect estimation of individual tree dendrometric metrics (field-derived) such as diameter at breast height, total height, and timber volume.

Over recent years, light detection and ranging (lidar) sensor technology has rapidly evolved and miniaturized. The reduced sensor size and weight have opened more doors for lidar sensors to be carried onboard unmanned aircraft systems (UASs). Compared with traditional airborne lidar mapping, UAS platforms offer more flexibility in terms of flight design and data collection, rapid response capabilities, and potentially cost at local mapping scales.

UAS-based lidar studies have primarily been focused on monitoring vegetation structure, simultaneous localization and mapping (SLAM) and so forth. A comparison between UAS and terrestrial laser scanning (TLS)-derived plant height for crop monitoring was made in. Descriptive statistics derived from polygon grids were analyzed and a correlation R2 = 0.91 was found in plant height derived from both methods. A lidar-based perception and guidance system was built on a helicopter to perform obstacle detection and avoidance, terrain following, and close-range inspection, and a high success rate was claimed by the authors.

Under task order G17PD01249: Alaska Critical Infrastructure UAV Airfield Obstruction Survey the Dewberry team was tasked to perform a test of the sensors that would be utilized in the survey of the Kiana and Nulato Airfields. As part of this testing our partners Compass Data and Phoenix LiDAR performed the acquisition and post processing of the LiDAR data using two (2)

sensors each flown at two different heights above ground. These parameters were designed in order to determine each of the following items:

 General Ability to meet project specifications – These tests were used to determine if each sensor could meet the general project requirements for data formatting and LAS point cloud data. Items like smooth surface repeatability, relative accuracy, intensity values, and other were tested for each sensor and flying height.

 LiDAR Density – Because we are utilizing a UAS based approach the intent was to determine what sensor and flying height would yield an appropriate density of points to determine the heights of obstructions.

 Geometric Calibration – Tested to determine if each sensor was providing accurate and repeatable measurements from the two different flying heights.

 Radiometric Testing – Tested to determine if each sensor was capable of identifying small or low-reflectance obstructions such as poles or antennas.

 Measurement Consistency – This was tested across each of the 4 flights to determine how consistently the maximum elevation could be determined on the test apparatus as well as on trees and other vertical features in the AOI.

The following report documents the calibration testing performed by the Dewberry team.