Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Advancing airborne and spaceborne synthetic aperture radar measurements of ice and snow in the northern Great Plains(Montana State University - Bozeman, College of Letters & Science, 2023) Palomaki, Ross Theodore; Chairperson, Graduate Committee: Eric A. Sproles; This is a manuscript style paper that includes co-authored chapters.The cryosphere is responding to climate change in ways that have negatively impacted socio-environmental systems. Accurate and timely observations of the cryosphere are critical to adapting our infrastructure to these rapid changes. This dissertation contributes novel approaches to validating synthetic aperture radar (SAR) measurements over river ice and seasonal prairie snow. Previous C-band SAR-based river ice studies typically validate regional ice cover maps using aerial photos of frozen rivers. This qualitative approach relies on the principle that visually rougher ice should result in stronger SAR backscatter. In Chapter 2 of this dissertation I present the first systematic, quantitative investigation of the effect of river ice surface roughness on C-band Sentinel-1 backscatter. I employ Random Forest algorithms first to replicate qualitative classification results from previous studies, and then as regression models to explore relationships between Sentinel-1 backscatter and novel, quantitative surface roughness metrics derived from drone-based Structure-from-Motion datasets. Classification accuracies are similar to those reported in previous studies, but poor regression performance indicates a weak relationship between river ice roughness and Sentinel-1 backscatter. In Chapter 3, I extend these drone-based surface measurements of river ice with GPR-based subsurface measurements. Results from this smaller, richer dataset demonstrate that Sentinel-1 VV backscatter is correlated with ice thickness and VH backscatter with structural properties, but results are site-specific and more work is necessary to create generalized river ice models from Sentinel-1 measurements. Interferometric SAR techniques have been used to estimate snow water equivalent (SWE) using L-band measurements from the UAVSAR platform. These methods have been developed in mountainous areas and have not been investigated over prairie snowpacks, which typically feature exposed agricultural vegetation and greater spatial variability than found in mountain snowpacks. In Chapter 4 I develop a rigorous statistical framework to demonstrate that UAVSAR measurements over prairie snowpacks are sensitive to small changes in SWE, and are relatively unaffected by exposed agricultural vegetation. However, sub-pixel snow depth variability decreases the accuracy of SWE estimates derived from UAVSAR measurements. The upcoming NISAR satellite mission provides an opportunity to extend this work with repeated L-band measurements over a wider range of prairie snow conditions.Item An operational methodology for validating satellite-based snow albedo measurements using a UAV(Montana State University - Bozeman, College of Letters & Science, 2021) Mullen, Andrew Louiselle; Chairperson, Graduate Committee: Eric A. Sproles; Eric A. Sproles, Jordy Hendrikx, Joseph A. Shaw and Charles K. Gatebe were co-authors of the article, 'An operational methodology for validating satellite-based snow albedo measurements using a UAV' submitted to the journal 'Frontiers in remote sensing' which is contained within this thesis.The albedo, or reflectivity, of seasonal snowpack directly controls the timing and magnitude of snowmelt and runoff. Snow albedo is affected by a large number of snow physical and environmental properties that vary considerably at multiple spatiotemporal scales. This variability introduces a high degree of uncertainty into existing modeling techniques. Models for snowmelt that require snow albedo can be improved by incorporating satellite measurements to inform and update estimates of this snow property. However, satellite measurements are susceptible to a multitude of error sources, which requires them to be calibrated and validated by means of ground-based measurements. Ground-based measurements from automated weather stations are often located at sparsely-distributed monitoring sites in homogeneous meadow environments. These spatially restricted in-situ data provide biased validation and calibration data that are not representative of the heterogeneous landscapes that comprise many seasonally snow-covered watersheds. In order to provide comprehensive validation and calibration of satellite albedo products, multiple near-surface measurements should be taken across large areas to capture the high degree of spatial variability that snow albedo can exhibit. UAV albedo measurements can be used to bridge the scaling gap between satellite and point-based measurements. Since these platforms are in a novel stage, the requisite methodologies for topographic correction and comparison to gridded albedo products do not exist. Additionally, there lacks a general understanding of the spatial scaling of albedo measurements in heterogeneous terrain. This research aims to develop these methodologies and provide a comprehensive understanding of how to deploy these platforms and properly interpret their measurements. We first developed and validated a topographic correction using ground-based measurements of snow albedo in a sloping alpine meadow. Sensitivity analyses on both ground validation measurements and UAV-based albedo surveys in our alpine study area highlight the implications of using different user-defined parameters for the proposed topographic correction and satellite comparison methods. Improvements to the methodology can be made in the way it accounts for trees, shading, and cloud cover. This research develops the initial steps requisite to the operationalization of UAV albedo measurements and standardization of the techniques.