Thermal Remote Sensing For Moisture Content Monitoring of Mine Tailings: Laboratory Study

Abstract

Mining produces massive volumes of mine tailings that are deposited into large-scale mine tailings impoundments. A key environmental objective of managing these large impoundments is mitigating fugitive dust emissions by monitoring and controlling moisture, because moisture directly affects the tailings’ strength and the ability to apply dust control measures using motorized equipment. Therefore, understanding the spatial and temporal variations in moisture content for surface tailings is critical for characterizing dust susceptibility and trafficability. Remote sensing has been proven to be a useful tool for similar applications. This study utilized laboratory testing conducted on iron mine tailings to verify that: (1) a relationship exists between moisture content and strength for the surface of mine tailings, and (2) thermal remote sensing can be used to infer spatial variations in moisture content for surface tailings. Multivariate regressions were developed to identify the critical remote sensing and climatic variables and evaluate their influence in remotely measured moisture content. For tailings samples collected from two different North American iron mines, regressions using sample temperature and ambient humidity were able to predict surface moisture content (R2> 0.9).

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

“…Besides, dust emission of mine tailings has a big influence on surrounding environment of mine areas, which can be mitigated by monitoring and controlling moisture of mine tailings. In [198], thermal sensors are mounted on UAVs to acquire data of iron mine tailings to map the spatial and temporal variations in moisture content of surface tailings. The relationship between moisture and strength of mine tailings is analyzed to help management of mine tailings.…”

Characterizing Soil Stiffness Using Thermal Remote Sensing and Machine Learning

“…The albedo was collected using an ASD Spectral Radiometer Handheld Pro, ranging from 350-900 nm. The range from 350-399 nm had a lot of noise, which made the data less reliable, so the 400-900 nm range was used to calculate the albedo of each soil [25]. To ensure that averaging was reliable, the reflectance values were collected at five different spots on the soil and tested 10 times.…”

“…Archeologists have used remote sensing to locate buried structures using MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) hyperspectral airborne data [16]; hyperspectral sensors have detected soil gradation [17]; Landsat 8 imagery and Geographic Information Systems [18,19] have been used to model wildfire; Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data have generated lithological and mineral maps [20,21]; and remote sensing has been used for anomaly/target recognition [22]. Work has also been conducted on thermal remote sensing for a variety of applications like detecting buried objects [23] or landmines [24], quantifying moisture content in mine tailings [25], using ENVISAT AASTR datasets [26] to correlate thermal inertia (TI)/Apparent Thermal Inertia (ATI) to land use/land cover mapping and to soil moisture [27][28][29][30]. Further remote sensing studies have focused on soil strength estimation to characterize the lunar surface's stiffness by using greyscale images to examine the wheel sinkage [31].…”

Utilizing Hyperspectral Remote Sensing for Soil Gradation

“…The fiber optic cable is run from the device into a pistol grip, and then a (1 degree) scope was attached A high-resolution ASD Spectroradiometer was also used, which has a useable band range from 400 to 2500 nm. The range from 350 to 399 nm has a lot of noise, which makes the data unusable [38]. The device has a total of 512 bands in the VIS-NIR range (350-1000 nm), roughly 600 bands in the short wave infrared (SWIR) 1 range (1000-1830 nm), and similarly in the SWIR 2 range (1830-2500 nm).…”