Future Work Required for Effective Field Application

The laboratory model developed by Zwissler et al. (in review) was not proven to be directly applicable to predict moisture content for field data, based on the field data collected for the MI-mag tailings impoundment. However, the data presented in Figure 4-3 and Figure 4-5 shows promise that there should be a way to use thermal remote sensing to predict moisture content and surface strength for tailings in the field. Likely, a model would need to be developed based on field-based data, rather than using a model developed with laboratory data. Beyond troubleshooting the issues with the laboratory

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based model, there are other challenges that need to be overcome. The largest challenges include collecting and processing the thermal imagery.

The thermal images used in this study were discrete images collected with a hand- held thermal camera, rather than using spatially-continuous imagery over the entire tailings impoundment. The thermal images used did satisfy the objective of assessing whether the laboratory relationships between thermal remote sensing and moisture content/strength can be applied to field scales. However, for full-scale industrial application, it would be more useful to collect spatially-continuous imagery over an entire impoundment so that maps can be generated to identify spatial and temporal changes in moisture content/strength for tailings impoundments, which could prove to be useful dust management tools to tailings impoundment managers. Developing methods to produce such maps was outside of the scope of this study, but some proof-of concept work on such applications was conducted.

Remote sensing data with continuous spatial coverage is typically collected using satellite, airborne, UAV, and ground-based imagery. Due to concerns about

implementation cost, spatial coverage, spatial resolution, and temporal resolution of the imagery, UAV remote sensing look to be the most promising techniques for use at tailings impoundments. Benefits of UAV-based remote sensing include that imagery can be flown on-demand with any sensor that the UAV can carry, meaning that pre- and post- heating thermal imagery could be collected on the same day. Additionally, the low flying height of UAVs means that clouds do not affect data acquisition like the do for satellite data, and the spatial resolution of all imagery collected is high (often cm scale). However, a major drawback of UAV-based remote sensing is the intensive data processing that is required, especially if the UAV flying height varies and if the UAV imagery is collected without GPS tracking. This processing is complex because UAV-based remote sensing yields overlapping, discrete images that need to be scaled, mosaicked, and georeferenced prior to interpretation.

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To assess the feasibility of using a UAV to collect thermal images, a FLIR Tau 2 thermal imaging camera attached to a DJI Phantom quadcopter UAV was flown on May 26, 2015 and used to collect thermal images of the surface of the MN-mag tailings basin. The Tau 2 thermal camera collects a single band of data (7.5-13.5μm) that could easily be calibrated with surface conditions and used to map spatial and temporal changes in thermal properties, moisture content, and strength for the tailings.

While the UAV-based thermal imagery was not processed to calculate surface temperature, it was qualitatively assessed, and sample images can be seen in Figure 4-7. A remarkable level of detail was distinguishable, and variations in relative temperature were observed, so the proof-of-concept UAV flight indicates that this technology could be used to map spatial and temporal changes of the surface of tailings impoundment with a high level of detail. This UAV imagery, when processed and paired with atmospheric and ground data, shows promise that it would enable the mapping of spatial and temporal changes in moisture content and strength across entire tailings impoundment(s).

The reason that this proof-of-concept UAV thermal imagery was not processed to calculate surface temperature is because of issues with scaling and stitching together the discrete thermal images to create a mosaic and georeference. The pattern recognition algorithms typically used by software designed to create image mosaics from traditional RBG visible imagery was not found to be effective at matching these thermal images. Due to a low flying height, ground control targets were not visible in every thermal image, and without automated image matching to assist in the image stitching, the task of creating a single scaled and georeferenced mosaic was found to be virtually impossible.

In order for these UAV-based thermal remote sensing methods to be applied, the technology needs to be developed to process this thermal imagery effectively, to yield a georeferenced thermal mosaic that can be compared to ground truth data. When this exists, UAV-based thermal imagery can be collected and applied to much larger surface areas very easily.

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Figure 4-7 | Thermal infrared imagery of MN-mag tailings impoundment collected with UAV in May 2015. Dark colors represent lower relative temperatures, and bright

colors represent higher relative temperatures. Detail including tire tracks can be distinguished in this imagery (left).