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Opto-mechanical design and analysis for coherent active imaging
(Montana State University - Bozeman, College of Engineering, 2022) Neeley, Jaime Branson; Co-chairs, Graduate Committee: Wm. Randall Babbitt and Joseph A. Shaw
The objective of this thesis project was to design a monostatic lidar transmit (Tx) and receive (Rx) opto-mechanical apparatus for remote sensing at a variable range of 50 m - 500 m. The scope of this project begins from the fiber output of a pre-designed Frequency-Modulated Continuous Wave (FMCW) lidar system. After design criteria for the lidar module are given, the optical and mechanical design is presented, opto-mechanical tolerancing is presented, and assembly, alignment, and testing procedures are covered as well. This thesis shows that the required design criteria of diffraction-limited optical performance was achieved while accounting for predictable manufacturing and assembly errors modeled using a Monte Carlo tolerance analysis. Furthermore, this thesis shows that the modeled and measured optical performance results were in good agreement and recommendations are given for improvements for the next-generation revision of the lidar Tx/Rx module.
Using sparse coding as a preprocessing technique for insect detection in pulsed LIDAR data
(Montana State University - Bozeman, College of Engineering, 2022) Zsidisin, Connor Reece; Chairperson, Graduate Committee: Brad Whitaker
This research proposes using sparse coding as a preprocessing technique on insect lidar based data. This preprocessing technique will be used in conjunction with the Adaptive Boosting (AdaBoost), Random UnderSampling Boosting (RUSBoost), and neural network algorithms to automatically detect insects. The project aims to increase the effectiveness of these algorithms by using new images created by sparse coding. The K-Singular Value Decomposition (KSVD) algorithm will be used to train a dictionary on images that contain the majority class (non-insects). This trained dictionary will be used along with Orthogonal Matching Pursuit (OMP) to reconstruct all lidar images. The difference between the original image and the reconstructed image will be taken and processed by the feature extraction function and then used to train and test the models. Using a complete and an overcomplete dictionary our results show that the algorithms are able to detect insects at a higher rate. Using an overcomplete dictionary we are able to classify 93.18% of insect containing images in the testing dataset. Using the complete dictionary we were able to maintain 99.70% of non-insect images while increasing the percentage of insects classified to 84.09%.
Diode-laser-based high spectral resolution LIDAR
(Montana State University - Bozeman, College of Engineering, 2021) Colberg, Luke Stewart; Chairperson, Graduate Committee: Kevin S. Repasky
This thesis describes the design, construction, and testing of a high spectral resolution lidar (HSRL) as a part of a combined HSRL and differential absorption lidar (DIAL) system. The combined HSRL and DIAL instrument is constructed using the MicroPulse DIAL (MPD) architecture and uses distributed Bragg reflector lasers. The MPD architecture is unique because it is eye-safe and cost-effective; therefore, it is ideal for creating a network of ground-based lidars. This instrument is designed for thermodynamic profiling of the lower troposphere. A network of these instruments would be helpful for wide-scale atmospheric monitoring for weather forecasting and climate science. The purpose of the HSRL is to retrieve the optical properties of aerosols in the lower troposphere. The HSRL uses the DIAL offline laser, which has a wavelength of 770.1085 nm, and a potassium vapor cell as the spectral filter. The data retrieved from the HSRL provides the aerosol backscatter coefficient and the backscatter ratio up to an altitude of 7 km during nighttime operation and 5 km during daytime operation. The time resolution for these measurements is 5 minutes, and the range resolution is 150 m. These aerosol optical properties are valuable for aerosol studies and climate modeling; aerosols introduce the most significant degree of uncertainty in modeling the heat flux of the atmosphere. Additionally, these aerosol optical properties can be used to find the planetary boundary layer height (PBLH). The planetary boundary layer controls the exchange of heat, water vapor, aerosols, and momentum between the surface and the atmosphere. It has been demonstrated that the PBLH strongly affects turbulent mixing, convective transport, and cloud entrainment, which makes the PBLH an important parameter for weather forecasting and climate modeling. Despite its significance in atmospheric science, there is no standard method for defining the PBLH. A retrieval method for finding the daytime PBLH using HSRL data is proposed, and data comparisons to radiosonde PBLH retrievals are provided. The algorithm shows a good agreement with the radiosonde retrievals for conditions with a well-behaved boundary layer.
Machine learning pipeline for rare-event detection in synthetic-aperture radar and LIDAR data
(Montana State University - Bozeman, College of Engineering, 2021) Scofield, Trey Palmer; Chairperson, Graduate Committee: Brad Whitaker
In this work, we develop a machine learning pipeline to autonomously classify synthetic aperture radar (SAR) and lidar data in rare-event, remote sensing applications. Here, we are predicting the presence of volcanoes on the surface of Venus, fish in Yellowstone Lake, and select marine-life in the Gulf of Mexico. Given the efficiency of collecting SAR images in space and airborne lidar geographical surveys, the size of the datasets are immense. Immense training data is desirable for machine learning models; however, a large majority of the data we are using do not contain volcanoes or fish, respectively. Thus, the machine learning models must be formulated in such a way to place a high emphasis on the minority, target classes. The developed pipeline includes data preprocessing, unsupervised clustering, feature extraction, and classification. For each collection of data, sub-images are initially fed through the pipeline to capture fine detail characteristics until they are mapped back to their original image to identify overall region behavior and the location of the target class(es). For both sub-images and original images, results were quantified and the most effective algorithm combinations and parameters were assigned. In this analysis, we determined the classification results are not sufficient enough to propel a completely autonomous system, rather, some manual observing of the data will need to be performed. Nonetheless, the pipeline serves as an effective tool to reduce costs associated with electronic storage and transmission of the data, as well as human labor in manually inspecting the data. It does this by removing a majority of the unimportant, non-target data in some cases while successfully retaining a high percentage of the important images.
Results of a micro pulse differential absorption LIDAR for temperature profiling and analysis code
(Montana State University - Bozeman, College of Engineering, 2021) Cruikshank, Owen Daniel; Chairperson, Graduate Committee: Kevin S. Repasky
Thermodynamic profiling of the lower troposphere is necessary for the study of weather and climate. The micropulse DIAL (differential absorption lidar), or MPD, presented here is designed to fill the need. The MPD is eye-safe and can run autonomously for continuous measurements compared to technologies with similar measurement capabilities like Raman lidar. Using a temperature-sensitive absorption line of O 2, the MPD system can measure the absorption of O 2 in the lower troposphere as a function of range and convert that measurement to temperature as a function of range. This process relies on a perturbative correction to the absorption retrieval to account for the fact that the O 2 absorption spectral linewidth is similar to the molecular Rayleigh scattering linewidth. An ancillary measurement of the ratio of aerosol backscatter to molecular backscatter is required for the correction. The integrated high spectral resolution lidar (HSRL) uses a heated potassium vapor notch filter to make the aerosol-to-molecular ratio measurement. An analysis program in MATLAB was written to take in raw lidar data and produce a temperature product of range and time. Results presented from a campaign at the Atmospheric Radiation Measurements program Southern Great Plains site in Oklahoma in spring 2019 show temperature comparisons with radiosonde measurements with a mean difference between radiosonde and MPD measurements of -1.1K and a standard deviation of 2.7 K. Further results from an instrument on the Montana State University campus in Bozeman and at the National Center for Atmospheric Research in Boulder, Colorado have shown that the MPD instrument can produce measurements autonomously for periods of weeks to months.
Soil storage on steep forested and non-forested mountain hillslopes in the Bitterroot Mountains, Montana
(Montana State University - Bozeman, College of Letters & Science, 2018) Quinn, Colin Aidan; Chairperson, Graduate Committee: Jean Dixon
Mountain hillslopes are dynamic settings with discontinuous soils affected by a suite of variables including climate, lithology, hydrology, and vegetation. Our study seeks to understand how forest cover influences soil and rock distribution at decadal to century timescales. We focus on a series of post-glacial hillslopes in Lost Horse Creek of the Bitterroot Mountains, Montana. In this system, avalanche paths maintain parallel, topographically similar swaths of forested and non-forested slopes with uniform aspect, lithology, and climate. We combine field observations, fallout radionuclide analysis (210 Pbex & 137 Cs), and remote sensing data to understand both landscape- and fine-scale patterns in soil and rock distribution. Local soil and rock measurements indicate more extensive soil cover (forest = 94.4 + or = 2.6%; non-forest = 88.3 + or = 1.9%) and thicker soils (6cm greater median) in the forested system. We compare landcover-classified rock to topographic metrics from LiDAR data and find a doubling of rock cover (from 40% to 80%) as hillslope angles transition across slopes of ~24-42 ?. Topographic roughness, calculated as the standard deviation of slope, is predictive of only ~60% of total landscape rock cover, but can identify large boulders and coarse-scale outcrops with higher accuracy (79%). These calibrated remote sensing metrics indicate higher rock cover in non-forested regions (34%, compared to 20% in forested areas), though with high uncertainty. Additionally, we measure fallout-radionuclide inventories in soils to explore variations in decadal transport processes and soil residence times. We find distinct 210 Pb and 137 Cs behaviors in forested and non-forested systems, controlled both by unique partitioning of each nuclide within organic and mineral soil horizons, but also due to depth-driven differences in their physical mobility. Average 210 Pb ex inventories in non-forested soils are 33% lower, and half as variable as soils in the forested region (10.45 + or = 0.97 and 15.49 + or = 1.91 kBq/m 2 respectively), while 137 Cs inventories are indistinguishable (4.04 + or = 0.34 and 3.73 + or = 0.42 kBq/m 2). Together, our spatial, field, and isotope analyses suggest forested systems have greater soil storage and longer residence times than non-forested soils, mediated by differences in surface erosion processes within a larger fire disturbance landscape.
Coherent imaging via temporal heterodyne and spatial shearing methods
(Montana State University - Bozeman, College of Letters & Science, 2018) Galloway, Ryan Moore; Chairperson, Graduate Committee: Wm. Randall Babbitt
Atmospheric turbulence rapidly decreases image quality at long ranges. Here multiple coherent imaging methods are discussed that lead to a new type of active imaging system, which may help mitigate the effects of atmospheric turbulence. This is accomplished via a self-referencing, linear frequency modulated laser signal, where the signal is both offset in transmitter location (spatial shearing), and is demultiplexed in the temporal frequency domain using unique time delays for each transmitter (temporal heterodyne). Spatial shearing allows one to capture a spatial derivative of the object's spatial frequency content, which if properly 'integrated' can be used to reconstruct an atmospheric phase-aberration-corrected image of the object. The system is illustrated from the starting point of temporal digital holography methods, and builds up to the self-referencing scheme. Various coherent imaging methods and situational parameters are compared.
Phase gradient averaging for holographic aperture LIDAR in the presence of turbulence
(Montana State University - Bozeman, College of Engineering, 2017) Blaszczyk, Christopher Ross; Chairperson, Graduate Committee: Joseph A. Shaw
The resolution of an image is dictated by the size and quality of the imaging system. The imaging system has a physical limit to the resolution dictated by the diffraction limited resolution. This limit can be improved by making the aperture larger on the imaging system. The increase in the physical aperture size can only be practical to a certain extent. However, to get beyond these physical constraints it is possible to use synthetic aperture methods to allow for the aperture to appear to be increased. Synthetic apertures are created by adding apertures coherently together to create a larger aperture that increases the diffraction limited resolution. To sum the aperture coherently the phase information needs to be available. One way to have access to the phase information is to capture the image as hologram. These holograms are captured by using a coherent light source with a reference beam to create an interference pattern that contains the phase information of the target. Holographic apertures can be used in a synthetic aperture method called Holographic Aperture Lidar (HAL). A problem that can arise while capturing images is turbulence in the atmosphere. Turbulence is a change in the index of refraction caused by a change in the temperature and pressure of the atmosphere. This causes the phase of the light to distort dynamically as it propagates making HAL imaging difficult. This thesis will cover a method to restore the original phase of the signal that has passed through turbulence so that it can be used in digital holography and HAL. This method uses averaging of the phase gradient to remove the dynamic turbulence and keep the phase information of the static target. The improvements observed in actual experiments were small, but the basics of the method worked, and the reason for only small improvement are discussed.
Airborne LIDAR applications in freshwater lakes
(Montana State University - Bozeman, College of Engineering, 2017) Roddewig, Michael Robbin; Chairperson, Graduate Committee: Joseph A. Shaw
In this dissertation we demonstrate a novel, low-cost, compact airborne lidar designed for marine fisheries research. We discuss the details of our design, show its application to management of invasive lake trout (Salvelinus namaycush) in Yellowstone Lake, and mapping of the lidar attenuation coefficient in lake water. Results from 2015 and 2016 are presented, and we also report the lidar detection of underwater thermal vents in Yellowstone Lake.
Scanning wing-beat-modulation LIDAR for insect studies
(Montana State University - Bozeman, College of Engineering, 2017) Tauc, Martin Jan; Chairperson, Graduate Committee: Joseph A. Shaw
The spatial distributions of flying insects are not well understood since most sampling methods - Malaise traps, sticky traps, vacuum traps, light traps - are not suited to documenting movements or changing distributions of various insects on short time scales. These methods also capture and kill the insects. To noninvasively monitor the spatial distributions of flying insects, we developed and implemented a scanning lidar system that measured wing-beat-modulation. Transmitting and receiving optics were mounted to a telescope that was attached to a scanning mount. As it scanned, the lidar collected and analyzed the light scattered from insect wings of various species. Mount position and pulse time-of-flight determined spatial location and spectral analysis of the backscattered light provided clues to insect identity. During one day of a four day field campaign at Grand Teton National Park in June of 2016, 76 'very likely' insects and 662 'somewhat likely' insects were detected, with a maximum range to the insect of 87:6m for 'very likely' insects.

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