Keith Cunningham

To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection

Monitoring unstable slopes with terrestrial laser scanning (TLS) has been proven effective. However, end users still struggle immensely with the efficient processing, analysis, and interpretation of the massive and complex TLS datasets. Two recent advances described in this paper now improve the ability to work with TLS data acquired on steep slopes. The first is the improved processing of TLS data to model complex topography and fill holes. This processing step results in a continuous topographic surface model that seamlessly characterizes the rock and soil surface. The second is an advance in the automated interpretation of the surface model in such a way that a magnitude and frequency relationship of rockfall events can be quantified, which can be used to assess maintenance strategies and forecast costs. The approach is applied to unstable highway slopes in the state of Alaska, U.S.A. to evaluate its effectiveness. Further, the influence of the selected model resolution and degree of hole filling on the derived slope metrics were analyzed. In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitude-frequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates. Optimal analysis results occur when appropriately balancing high modeling resolution with an appropriate level of hole filling.

Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Abstract Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections and facilitate bridge access. Any such inspection must consider the design of the UAV, the choice of cameras, data acquisition, geometrical resolution, safety regulations and pilot protocols. The Placer River Trail Bridge in Alaska recently served as a test bed for a UAV inspection methodology that integrates these considerations. The end goal was to produce a three-dimensional (3D) model of the bridge using UAV-captured images and a hierarchical Dense Structure-from-Motion algorithm. To maximise the quality of the model and its benefits to inspectors, this goal guided UAV design and mission planning. The resulting inspection methodology integrates UAV design, data capture and data analysis together to provide an optimised 3D model. This model provides inspection documentation while enabling the monitoring of defects. The developed methodology is presented herein, as well as analyses of the 3D models. The results are compared against models generated through laser scanning. The findings demonstrate that the UAV inspection methodology provided superior 3D models with the accuracy to resolve defects and support the needs of infrastructure managers.

UAS-Based Inspection of the Placer River Trail Bridge: A Data-Driven Approach

Over the last decade, unmanned aerial systems (UASs) technology has matured to the point that it is now a viable option for augmenting more traditional bridge inspection methods. Any UAS-based inspection must address questions regarding the design of the aerial platform, the choice of on-board inspection cameras, and the protocols for the human pilot to follow. The wide variety of currently available UASs and imaging systems, as well as a current lack of UAS inspection standards, can pose challenges to engineers attempting to design or test a new system. The Placer River Trail Bridge, located on the Alaskan Kenai Peninsula, has been chosen as a test site for the prototyping and testing of a new UAS bridge inspection system. The desired final inspection product guided the design and operation of the UAS. The research team emphasized a data-driven approach in developing the UAS and its associated inspection protocols. Goals included providing high-resolution scans of the bridge capable of detecting a variety of damage indicators, and producing a 3D virtual model of the bridge using Structure-from-Motion. The resulting designs of both the UAS and the inspection protocols are presented herein. The results indicate that such a data-driven approach can lead to UAS inspection systems that are more capable of meeting the needs of bridge asset managers and inspectors.

Unmanned Aircraft Systems for Geotechnical Monitoring of Pipelines in the Arctic

Pipelines in the Arctic must contend with a variety of geotechnical obstacles affecting their construction and long-term operation. For example, thawing permafrost leads to unstable terrain, especially on slopes and pipeline river crossings. Research funded by the US Department of Transportation is underway to investigate how unmanned aircraft systems (UAS) could be used to monitor permafrost and unstable soils in order to develop a proactive decision-support system for pipeline operators. Leading this research is the University of Alaska Fairbanks (UAF) in cooperation with the Alyeska Pipeline Service Company. The research will evaluate a variety of unmanned aircraft systems (UAS) to determine an optimal technology mix, and best practices for the persistent surveillance of thawing permafrost and shifting soils, thereby providing pipeline operators a broader picture of, and a means of mitigating, the geotechnical hazards that affect pipelines.

Unmanned Aircraft Systems in Alaskan Civil Research

In Alaska, unmanned aircraft systems (UAS) have garnered much attention, largely due to ongoing research at the University of Alaska Fairbanks (UAF). This research has focused on a multitude of civilian applications suitable for support by the technology. UAF has long held an advantage in this competitive research by virtue of its status as a non-profit research institution, a requirement for approval by the Federal Aviation Administration (FAA) to operate UAS. In addition, UAF has a long history in this field, managing airspace since the 1960’s as part of its duties as the only university-operated rocket range in the world. These factors have led many commercial entities to partner with UAF to conduct UAS research over the years.

Remote Sensing for the Audit and Assurance of the Carbon Market

The United Nations REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program has made forest preservation a priority, in part because forests sequester carbon and have made carbon value a new global currency. Large money transfers between nations for forest preservation are occurring, and credits for voluntary carbon standards (VCS) are already being traded. The 2011 market is US

Validating and calibrating a forecast model

30 billion and, by 2020, will include an estimated US

Rockfall Activity Index (RAI): A lidar-derived, morphology-based method for hazard assessment

100 billion in UN-administered funding [1]. Driving this market will be the voluntary trading of carbon as a commodity. Several academic and non-governmental organizations (NGOs) are now monitoring deforestation and forest degradation using satellite remote sensing. Imagery archives in Europe and the United States are publicly available and in use. These archives have been used to establish baseline forest measurements necessary to quantify deforestation. Quantifiable forest measurements allow experts to understand not only deforestation and forest degradation, but also the conservation performance of those Nations participating in the UN-administered REDD+ funding. This performance measurement is key to assuring the transparent function of the VCS market. Independent audits are standard accounting practice in the operation of global industries, and third-party assurance will be needed for the healthy functioning of global financial markets at the center of the REDD+ program. This paper will review the state of remote sensing for REDD+ and its role in the third-party audit and assurance processes used to validate the transparency of the VCS market.