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Spectral processing for algae monitoring and mapping (SPAMM): remote sensing methodologies for river ecology
(Montana State University - Bozeman, College of Engineering, 2024) Logan, Riley Donovan; Chairperson, Graduate Committee: Joseph A. Shaw; This is a manuscript style paper that includes co-authored chapters.
Inland water quality is a growing concern to public health, riparian ecosystems, and recreational uses of our waterways. Many modern water quality programs include measures of the presence and abundance of harmful and nuisance algae. In southwestern Montana, large blooms of the nuisance algae, Cladophora glomerata, have become common in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of contaminants through wastewater and anthropogenic activity along its banks. To improve understanding of bloom dynamics, such as algal biomass and percent algae cover, and their effects on water quality, a UAV-based hyperspectral imaging system was used to monitor several locations along the Upper Clark Fork River. Image data were collected across the spectral range of 400 - 1000 nm with 2.1 nm spectral resolution during field sampling campaigns across the entirety of the project, beginning in 2019 and ending in 2023. In this dissertation, methodologies for monitoring water quality were developed. These methods include estimating benthic algal pigment abundance using spectral band ratios achieving R 2 values of up to 0.62 for chlorophyll alpha and 0.96 for phycocyanin; creating spatial algae distribution maps and estimating percent algae cover using machine learning classification algorithms with accuracies greater than 99%; combining spatial algae distribution maps and improved pigment estimation using machine learning regression algorithms for creating chlorophyll alpha abundance maps, achieving an R 2 of 0.873, while also comparing abundance values to Montana water quality thresholds; and identifying salient wavelengths for monitoring and mapping algae to inform the design of a low-cost and compact multispectral imager. Throughout all field campaigns, significant spatial variations in algal growth within each river reach and frequent violations of current water quality standards were observed, demonstrating the need for high-spatial resolution monitoring techniques to be incorporated in current water quality monitoring programs.
Applying advanced materials characterization techniques for an enhanced understanding of firn and snow properties
(Montana State University - Bozeman, College of Engineering, 2024) Schehrer, Evan Nicholas; Chairperson, Graduate Committee: Kevin Hammonds; This is a manuscript style paper that includes co-authored chapters.
Understanding snow microstructure and stratigraphy is critical for enhancing modeling efforts and instrument validation for the polar regions and seasonal snow. Controlled laboratory experiments help with these efforts and are essential for enhanced comprehension of polar firn densification, snow metamorphism, avalanche mechanics, snow hydrology, and radiative transfer properties. This dissertation aims to characterize snow and ice as they relate to the mechanical and sintering properties of simulated firn subject to trace amounts of sulfuric acid (H 2SO 4). Studies were also developed to characterize faceted snow crystallographic orientation using electron backscatter diffraction (EBSD) and understand the observed reflectance of remote sensing instruments related to mapping changing snow microstructure. To investigate the effects of soluble impurities, 50 ppm H 2SO 4 and impurity-free ice grains were developed to simulate polar firn and then subjected to a series of unconfined uniaxial compression to monitor the effect in mechanical strength at different temperatures and strain rates. Meanwhile, the role of sintering is less defined for ice grains that contain impurities. Two experiments were developed to quantify sintering rates with H 2SO 4. One experiment tracked the changes in microstructure at isothermal conditions using X-ray computed microtomography over 264 days. A second experiment used angle of repose tests to characterize the subsecond sintering between H 2SO 4 and impurity-free ice grains. In addition, it is well known that snow has constantly changing microstructure once deposited during precipitation events. These changes have an immediate impact on the crystallographic and optical properties. Faceted snow crystals, collected from the field and artificially grown, were analyzed using EBSD to map vapor-deposited growth along the three ice (Ih) crystallographic planes. Moreover, validation of remote sensing techniques such as near-infrared hyperspectral imaging (NIR-HSI) and lidar is essential for accurate field measurements. In the laboratory, an intercomparison test was conducted for NIR-HSI and lidar to analyze bidirectional reflectance returns, mapping the effective grain size of snow under different microstructural conditions and during melt/freeze events and surface hoar growth.
When and where does irrigation water originate? Leveraging stable water isotopes and synthetic aperture radar to assess the complex hydrology of a snow-dominated catchment in southwestern Montana
(Montana State University - Bozeman, College of Letters & Science, 2023) Rickenbaugh, Eliza Apple; Chairperson, Graduate Committee: Eric A. Sproles; This is a manuscript style paper that includes co-authored chapters.
Many agricultural regions around the world rely on water stored in mountainous snowpacks for irrigation supply. Consequently, our current and future ability to produce food is threatened by more frequent, severe, and extended snow droughts. As these snow droughts intensify, water resource managers will need more efficient and accurate methods to characterize the snowmelt cycle and forecast water availability. Focusing on a montane headwater catchment in Southwestern Montana (423 km 2 in area, between 1465 m to 3270 m in elevation), we integrate in-situ and remotely sensed data to assess the relative contributions of groundwater and the current season's snowmelt to irrigation supply for water year (WY, Oct 1 - Sep 30) 2023. To understand the period over which snow contributes to stream water in this catchment, we analyze backscatter data from Sentinel-1 Synthetic Aperture Radar (SAR). This provides approximate dates of snowmelt runoff onset at 10 m resolution every twelve days. We find that the median date of snowmelt runoff onset in WY 2023 in this catchment was April 20, six days later than the 7-year median date of snowmelt runoff onset. To assess relative contributions to streamflow we compare stable water isotope ratios (deltaH2 and deltaO18) from biweekly samples of stream water at low elevations against monthly samples of snow and groundwater. Samples range in elevation from 1,475 m to 2,555 m. We find that stream water below the highest diversion point is predominantly composed of groundwater. Results demonstrate alignment between two disparate approaches for estimating temporal trends in snowpack contribution to stream flow. While our work focuses on a catchment in Montana, the efforts and approaches used are potentially applicable globally for agricultural regions that rely on snowmelt for irrigation.
A framework for the quantitative assessment of new data streams in avalanche forecasting
(Montana State University - Bozeman, College of Letters & Science, 2023) Haddad, Alexander Sean; Co-chairs, Graduate Committee: Eric A. Sproles and Jordy Hendrikx
Data used by avalanche forecasters are typically collected using weather stations, manual field-based observations (e.g., avalanche events, snow profiles, stability tests, personal observations, public observations, etc.) and weather forecasts ("traditional observations"). Today, snow cover observations can be delivered via remote sensing (e.g., satellite data, UAV, TLS, time-lapse camera etc.). Forecasting operations can also use statistical forecasting, weather models, and physical modeling to support decisions. This paper presents a framework and methodology to quantify the impact these new, complex data streams have on the formulation of, and associated uncertainty of, avalanche forecasting. We use data from a case study in Norway. Avalanche forecasters in Norway assessed size (D), likelihood, avalanche problem, and hazard level for a highway corridor in Grasdalen, Stryn Norway. The control groups were given access to traditional observations. The experimental groups were given access to the same traditional data, but also near-real-time snow surface LiDAR data ("RS+"). In case study one the RS+ (n=10) consensus findings were a hazard level two steps lower than the control group (n=10). In case study two the traditional (n=10) and RS+ groups' (n=7) consensus findings assessed the northeastern avalanche path at the same hazard level. Assessing the southwestern slide path, the traditional group (n=10) and RS+ group (n=9) had the same consensus finding for hazard level. In 2 of 3 case studies, the RS+ groups had fewer selections for size, likelihood, and avalanche problem which indicates reduced uncertainty in their forecasts. Throughout the 2022-2023 winter season Norwegian Public Roads Administration avalanche forecasters performed a real-time experiment throughout the season - with and without additional RS+ data when forecasting. They agreed on hazard level in 6 of 10 forecasts. In the other 4 forecasts, RS+ forecasters assessed the hazard level higher than traditional data forecasts. When RS+ data reveals aspects of conditions that traditional observations did not detail, RS+ forecasters adjust their selections in the hazard matrix, resulting in greater clustering of their predictions, indicating reduced uncertainty. Due to uncertainty associated with avalanche forecasting, this framework for assessment should be used to track avalanche forecast efficacy and build a qualitative and quantitative historical record.
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%.
Quanitfying snow depth distributions and spatial variability in complex mountain terrain
(Montana State University - Bozeman, College of Letters & Science, 2021) Miller, Zachary Stephen; Chairperson, Graduate Committee: Eric A. Sproles
The spatial variability of snow depth is a major source of uncertainty in avalanche and hydrologic forecasting. Identification of spatial and temporal patterns in snow depth is further complicated by the interactions of complex mountain topography and localized micro-meteorology. Recent studies have dramatically improved our understanding of snow depth spatial variability by utilizing increasingly accessible remote sensing technologies such as satellite imagery, terrestrial laser scanning, airborne laser scanning and uninhabited aerial systems (UAS) to map spatially continuous snow depths over a variety of spatiotemporal scales. However, much of this work focuses on relatively low-relief topographies or limited temporal frequencies. Our research presents a thorough evaluation of the evolution of snow depth spatial variability at the slope scale in steep complex mountain terrain (45.834 N, -110.935 E) using analysis from UAS imagery. We apply 13 spatially complete UAS-derived snow depth datasets collected throughout the course of the 2019/2020 winter to analyze spatial and temporal patterns of snow depth and snow depth change variability. Our results show greater spatial variability in steep complex mountain terrain than an adjacent mountain meadow both in the seasonal context and during individual meteorological periods. We analyze 2 cm horizontal resolution snow depth models by (i) comparing spatial patterns with coincident meteorological data, (ii) analysis of the temporal elevation specific patterns of snow depth, and (iii) a comprehensive multi-scalar evaluation of spatial variability. We quantify the unique spatial signature of four specific events: a major snow accumulation, a natural avalanche, a calm period, and a significant wind event. We find a non-linear relationship between elevation and snow depth, with upper elevations proving to be the most variable. We also verify that significant storm events result in the largest snow depth change variability throughout our study area, as compared to other meteorological events. The synthesis of these findings illustrate the dynamic spatial and temporal snow depth distribution patterns observed in complex mountain terrain during the course of a winter season. These findings are relevant to avalanche forecasters and researchers, snow hydrologists and local water resource managers, and downstream communities dependent on snow as a hydrologic reservoir.
Remote sensing of wet snow processes in a controlled laboratory environment
(Montana State University - Bozeman, College of Engineering, 2022) Donahue, Christopher Paul; Chairperson, Graduate Committee: Kevin Hammonds; This is a manuscript style paper that includes co-authored chapters.
Water flow through snow, due to snowmelt or rain-on-snow events, is a heterogeneous process that has implications for snowmelt timing and magnitude, snow metamorphism, albedo evolution, and avalanche hazard. Remote sensing technologies, ranging from ground-based to satellite-borne scales, offer a non-destructive method for monitoring seasonal snowpacks, although there is no single technique that is ideal for monitoring snow. Wet snow, specifically, presents a challenge to both optical and radar remote sensing retrievals. The primary aim of this dissertation was to develop wet snow remote sensing methods from within a controlled laboratory environment, allowing for precise characterization of snow properties. Experiments were conducted by preparing laboratory snow samples of prescribed structures and monitoring them during and after melt using hyperspectral imaging and polarimetric radar. Snow properties were characterized using X-ray computed microtomography, a dielectric liquid water content sensor, and serial-section reconstructions. In addition to laboratory experiments, hyperspectral imaging snow property retrieval methods were developed and tested in the field during wet snow conditions at the ground-based scale. The primary outcomes from this work were three new remote sensing applications for monitoring wet snow processes. First, a new hyperspectral imaging method to map effective snow grain size was developed and used to quantify grain growth due to wet snow metamorphism. Second, the optimal radiative transfer mixing model to simulate wet snow reflectance was determined and used to map liquid water content in snow in 2- and 3- dimensions. Lastly, snow melt progression was monitored using continuous upward-looking polarimetric radar and it was found, by comparison to 3-dimensional liquid water content retrievals from hyperspectral imaging, that the cross-polarized radar signal was sensitive to the presence of preferential flow paths. The work presented here highlights the utility of using a multi-sensor fusion approach to snow remote sensing. Although these laboratory remote sensing experiments were at a small scale, the remote sensing instrument response to specific snow conditions directly translates to larger scales, which is valuable to support algorithm development for ground, airborne, and spaceborne remote sensing missions.
Spatial patterns in soil depth and implications for offseason nitrogen dynamics in dryland wheat systems of central Montana
(Montana State University - Bozeman, College of Agriculture, 2022) Fordyce, Simon Isaac; Co-chairs, Graduate Committee: Clain Jones and Craig Carr; Pat Carr, Clain Jones, Jed Eberly, Scott Powell, Adam Sigler and Stephanie Ewing were co-authors of the article, 'Exploring relationships between soil depth and multi-temporal spectral reflectance in a semi-arid agroecosystem: effects of spatial and temporal resolution' submitted to the journal 'Remote Sensing of environment' which is contained within this thesis.; Pat Carr, Clain Jones, Jed Eberly, Rob Payn, Adam Sigler and Stephanie Ewing were co-authors of the article, 'Spatiotemporal patterns of nitrogen mineralization in a dryland wheat system' submitted to the journal 'Agriculture, ecosystems, and environment' which is contained within this thesis.
Shallow soils (< 50 cm) under dryland wheat (Triticum aestivum L.) production lose large amounts of inorganic nitrogen (N) to leaching. Crops grown in shallow soils may be more responsive to N fertilizer due to lower fertilizer recovery and suppressed mineralization, raising questions as to whether standard practices of N fertilizer rate determination can increase risks of leaching and groundwater contamination in these environments. Mineralized N can be a major nutritional supplement for wheat crops in dryland agroecosystems, so accurate estimates of mineralization inputs can have important economic and environmental implications. To assess the potential for suppressed N mineralization in shallow soils, we used spectral reflectance from up to three sensors (unmanned aerial vehicle, National Agricultural Imagery Program, and Sentinel 2) to spatially characterize soil depth on three fields in Central Montana (Chapter 2) and compared surface (0-20 cm) carbon and N cycling indices across soil depth classes (Chapter 3). Carbon dynamics were stable across depth classes while N mineralization was lower in the shallow class. Results confirm multispectral imagery as a valuable tool for non-destructively characterizing fine-scale spatial patterns in soil depth and corroborate previous findings of lower N mineralization in shallow soil environments. Given the potential for heightened fertilizer responsiveness due to lower mineralization in these environments, decision support systems for site-specific fertility management (e.g., variable rate fertilizer application) should assess the environmental consequences of leaching alongside the economic benefits of applied fertilizer rates which maximize responses of yield, quality and same-year net revenue.
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.

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