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1. Geotechnical Characterization of Underground Mine Excavations from UAV-Captured Photogrammetric & Thermal Imagery

   Turner, R.M.; Bhagwat, N.P.; Galayda, L.J.; Knoll, C.S.; Russell, E.A.; MacLaughlin, M.M. (June 2018, Proceedings of the 52nd US Rock Mechanics / Geomechanics Symposium)

   Geotechnical characterization of rock masses in underground mines often involves physical measurements in supported excavations. However, unsupported stopes and drifts prevent safe access for mapping by geotechnical personnel. The advent of inexpensive, open platform unmanned aerial vehicles (UAVs) allows geotechnical personnel to characterize hazardous rock masses by utilizing traditional photogrammetric and FLIR (forward looking infrared) imagery techniques. The photogrammetric imagery can be used to capture geological structural data from the rock mass for kinematic and numerical analyses, as well as for generating geological models. In particular, the FLIR imagery has the potential to assist in identifying areas of loose rock, which typically goes unnoticed until it becomes a hazard. This paper summarizes the results of a study involving UAV flights underground at the Barrick Golden Sunlight Mine, the generation of 3D models from UAV-captured imagery, and the identification of geological data from photogrammetry models. Results confirm that the combination of off-the-shelf technologies used in this study can be successfully employed as a geotechnical tool in the underground mining environment. 

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2. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles

   https://doi.org/10.1139/as-2021-0044  

   Orndahl, Kathleen M.; Ehlers, Libby P.; Herriges, Jim D.; Pernick, Rachel E.; Hebblewhite, Mark; Goetz, Scott J. (April 2022, Arctic Science)

   Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m^-2, and per-PFT RMSE ranged from 17.14-164.3 g m^-2. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery. 

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3. Collaborative multi-robot multi-human teams in search and rescue.

   Williams, R. (April 2020, Proceedings of the International ISCRAM Conference)

   Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy. 

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4. Strategies for effective unmanned aerial vehicle use in geological field studies based on cognitive science principles

   https://doi.org/10.1130/GES02440.1  

   Bateman, Kathryn M.; Williams, Randolph T.; Shipley, Thomas F.; Tikoff, Basil; Pavlis, Terry; Wilson, Cristina G.; Cooke, Michele L.; Fagereng, Ake (November 2022, Geosphere)

   Abstract Field geologists are increasingly using unmanned aerial vehicles (UAVs or drones), although their use involves significant cognitive challenges for which geologists are not well trained. On the basis of surveying the user community and documenting experts’ use in the field, we identified five major problems, most of which are aligned with well-documented limits on cognitive performance. First, the images being sent from the UAV portray the landscape from multiple different view directions. Second, even with a constant view direction, the ability to move the UAV or zoom the camera lens results in rapid changes in visual scale. Third, the images from the UAVs are displayed too quickly for users, even experts, to assimilate efficiently. Fourth, it is relatively easy to get lost when flying, particularly if the user is unfamiliar with the area or with UAV use. Fifth, physical limitations on flight time are a source of stress, which renders the operator less effective. Many of the strategies currently employed by field geologists, such as postprocessing and photogrammetry, can reduce these problems. We summarize the cognitive science basis for these issues and provide some new strategies that are designed to overcome these limitations and promote more effective UAV use in the field. The goal is to make UAV-based geological interpretations in the field possible by recognizing and reducing cognitive load. 

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5. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles, 2018-2019

   https://doi.org/10.18739/A2R785Q5B  

   Orndahl, Kathleen (January 2022, Arctic Data Center)

   Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data. 

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