Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown-Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.
The increased availability of unmanned aerial vehicles (UAVs) has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping. The development and utilisation of UAV platforms requires broad technical skills covering the three major facets of remote sensing: data acquisition, data post-processing, and image analysis. In this study, UAV image data acquired by a miniature 6-band multispectral imaging sensor was corrected and calibrated using practical image-based data post-processing techniques. Data correction techniques included dark offset subtraction to reduce sensor noise, flat-field derived per-pixel look-up-tables to correct vignetting, and implementation of the Brown-Conrady model to correct lens distortion. Radiometric calibration was conducted with an image-based empirical line model using pseudo-invariant features (PIFs). Sensor corrections and radiometric calibration improve the quality of the data, aiding quantitative analysis and generating consistency with other calibrated datasets.
The capacity for additional textural derivatives to compensate for the lack of broader spectral sensitivity of consumer grade digitial cameras is established within a UAV context. A texture selection framework utilising random forest machine learning, was developed for application with ultra-high spatial resolution UAV imagery limited to the visible spectrum. The framework represents an adaptive approach, providing a rapid assessment of different texture measures relative to a specific user-defined application. This framework is illustrated within the context of UAV salt marsh mapping. This study highlights the importance of texture selection for improving classification of UAV imagery exhibiting high local spatial variance.
The fine-grained nature of some tailings materials can lead to dusting on tailings storage facilities (TSFs). Dusting has the potential to negatively affect the health of workers, nearby communities, and the surrounding environment which may adversely affect a mining operator's ability to operate. Mitigation measures to prevent dusting can be expensive and aren't always successful. Advances in monitoring systems allow the relative surface moisture of tailings beaches to be monitored during operations providing the opportunity to identify and forward plan the depositional strategy to minimise the risk of dusting. This paper presents the outcomes of a study that utilised predictive methods based on advanced synthetic aperture radar and multispectral data coupled with Google Earth Engine to develop a model for particular TSFs based on historical records, observations, and material types.Google Earth Engine brings the first opportunity to use a systematic and comprehensive combination of radar and visual-infrared satellite data. It was found that the synergy between the two data types could be used to offset the individual ambiguities of each, and the resulting method delivered a predictive dryness probability map and visual moisture/water depth/presence indicators that were able to be verified and made operational almost immediately.On-ground visual records and aerial imagery provided qualitative verification of the approach. The methodology allows TSF operators a free, open source platform with which to monitor and map surface moisture, enabling proactive deposition decision-making to mitigate the risk of tailings dusting. Additional benefits realised include increased data on beach formation, channel and pond location (extent and to some degree depth), improving the accuracy of the TSF water balance. For the particular TSFs studied, the water balance is a critical control from a safety perspective to mitigate potential failure mechanisms, and from an operational perspective to maximise tailings density and water return to the plant.
Mine closure relies on proof of best practice in both design and performance of rehabilitation. Field techniques have been the traditional approach for producing detailed supporting empirical evidence for mine closure. Although field sampling provides a detailed snapshot of key performance criteria within small areas, these areas themselves may not be representative of the overall performance of rehabilitation. Additionally, their limited scale may miss broader spatial characteristics that could further strengthen arguments for relinquishment. Remote sensing is a complimentary approach to field sampling that can produce an entire census of a rehabilitation site at a reduced scale. However, uncertainty still surrounds the adoption of a remote sensing approach, and whether such techniques can capture key performance indicators accurately and consistently. This paper provides both a demonstration of capacity, and quantification of accuracy, of remotely sensed data analytics for the production of empirical evidence to support mine closure management. Using rehabilitated landforms in the Western Australian Goldfields as case studies, remote sensing was adopted in two supporting roles: the validation of landform construction, and the ongoing monitoring of landform performance. The geometry of constructed landform surfaces was measured through photogrammetric techniques and assessed against design specifications. Ongoing monitoring assessed both vegetative colonisation and relative stability of established landform surfaces. Coupled together, the broader scale impact of non-compliant areas upon local rehabilitation performance was explored and discussed. Underpinning these data analytics is the accuracy of remotely sensed data. The quantification of uncertainty within the data was derived through a comparison against precision field measurements. Quantification of this uncertainty allowed the establishment of confidence intervals on derived measurements. Furthermore, the impact of changing environmental complexity upon analysis performance was quantified. This allowed for the modelling of compensation factors that dynamically counterbalance the increased uncertainty of complex environments. The result of the study demonstrates the capacity for a remote sensing approach to empirically support mine closure and relinquishment.
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