A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

Abstract

The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.

Accuracy Analysis of a New Data Processing Method for Landslide Monitoring Based on Unmanned Aerial System Photogrammetry

“…All the mentioned techniques compare high-resolution products generated by processing images with SfM and MVS algorithms from acquired images in two different UAS photogrammetric survey epochs [ 22 , 23 ]. In the entire SfM-MVS algorithm, processing using the MVS algorithm is a significantly more time-consuming and demanding part of the processing algorithm [ 34 , 35 , 36 ].…”

Design and Experimental Study on an Innovative UAV-LiDAR Topographic Mapping System for Precision Land Levelling

“…However, processing aerial images to generate photogrammetric DSMs usually needs strict camera calibration and a large number of spatially well-distributed GCPs (Ground Control Points), which in photogrammetry or the computer vision domain refers to such features that are easily recognizable and distinguishable in both the real world and the images. The spatial resolution, as well as the accuracy, of such topographic maps usually reaches several decimeters to tens of meters [34], which cannot meet the high requirements of topographic maps with centimeter-level accuracy for precision farmland levelling.…”