This whitepaper details the use of unmanned aerial vehicle (UAV) LiDAR missions conducted in the Maya Lowlands between June 2017 and June 2018 to develop effective methods, procedures, and standards for drone LiDAR surveys of ancient Maya settlements and landscapes.

Testing at three site locations in the upper Usumacinta River region—Piedras Negras in Guatemala (2017) and Budsilha and El Infiernito in Mexico (2018)—provided a diverse range of natural and cultural contexts to evaluate the technology’s field utility. The study’s results, based on standard digital elevation and surface models, demonstrate the effectiveness of drone LiDAR for documenting ancient landscapes and settlements across the Maya Lowlands and Latin America.

This approach is adaptive, cost-effective, and suitable for targeted documentation, though it requires careful planning and sample evaluation. Future research will further refine the methods and techniques for data filtering and processing.

This whitepaper explores the application of Remotely Piloted Aircraft Systems (RPAS) equipped with RGB and LiDAR sensors for monitoring the Brazilian savanna, known as the Cerrado. The Cerrado is the most biologically diverse savanna globally, facing significant threats from anthropogenic activities, necessitating effective environmental policies. This study demonstrates the potential of these advanced sensing technologies for the physical characterization of landscapes within the Cerrado biome.

The research includes analyses of vegetation structure, where the automatic counting of trees was performed. Results indicated that the average tree height measured by RGB sensors was significantly lower than that obtained by LiDAR sensors, highlighting the limitations of Structure from Motion data in densely vegetated landscapes.

The LiDAR data enabled accurate tree counting, with 1,825 trees identified across the study area and 245 within specific ecological study parcels. This paper underscores the effectiveness of RPAS in reducing the costs and time associated with environmental surveys and evaluations, providing crucial insights for the development of conservation policies in the Cerrado.

This whitepaper presents the initial results from testing and evaluating a single-rotary Unmanned Aircraft System (UAS) integrated with a long-range, multi-return LiDAR sensor. Conducted at an airfield in South Texas, USA, the study explores the evolving capabilities of miniaturized LiDAR technology and its application in UAS platforms. Compared to traditional airborne LiDAR mapping, UAS platforms offer greater flexibility in flight design, rapid response capabilities, and potentially lower costs for local mapping.

The research focuses on describing the UAS platform and its enabling technologies (LiDAR, IMU, GPS), sensor calibration and initialization processes, and the methods for geospatial surveying, data processing, and analysis. The advantages of LiDAR, such as its pulsed ranging technique and multi-return detection capability, are highlighted, demonstrating its effectiveness in applications like vegetation structure monitoring, obstacle detection, and digital terrain model refinement.

This study underscores the potential of UAS LiDAR systems for fine-scale mapping and various environmental monitoring applications, paving the way for enhanced precision and efficiency in geospatial data collection.

This whitepaper details the calibration testing conducted under task order G17PD01249: Alaska Critical Infrastructure UAV Airfield Obstruction Survey. The Dewberry team, in collaboration with Compass Data and Phoenix LiDAR, performed LiDAR sensor tests for the Kiana and Nulato Airfields. The testing involved the acquisition and post-processing of LiDAR data using two sensors, each flown at two different heights above ground.

The study aimed to assess the sensors’ ability to meet project specifications, including data formatting, LAS point cloud data, smooth surface repeatability, relative accuracy, and intensity values. Additionally, the tests evaluated LiDAR density to determine the optimal sensor and flying height for identifying obstructions, geometric calibration for measurement accuracy and repeatability, radiometric testing for detecting small or low-reflectance obstructions, and measurement consistency across multiple flights.

The findings of this comprehensive testing are documented in this report, providing valuable insights into the performance and reliability of UAV-based LiDAR systems for airfield obstruction surveys.

This whitepaper outlines the objectives and results of a test campaign aimed at evaluating the accuracy of point clouds generated by Phoenix LiDAR Systems in a realistic survey mission scenario. The study also tested the performance of Terrasolid software, running on MicroStation CONNECT Edition, for calibrating and enhancing the data.

Data sets were acquired using a Camflight FX8HL UAS platform and pre-processed into trajectory and point cloud files using Phoenix LiDAR Systems software. The findings demonstrate the effectiveness of these technologies in producing accurate and reliable data for various survey applications, highlighting the potential for improved data calibration and processing with the integrated software solutions.

This report provides valuable insights into the capabilities and performance of UAV-based LiDAR systems for high-precision surveying and mapping tasks.