Scholarly Work - Electrical & Computer Engineering
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/8814
Browse
3 results
Search Results
Item Toward polarization-enhanced water quality remote sensing measurements from UAVs(SPIE, 2024-05) Morgan, P. Flint; Weller, Wyatt W.; Maxwell, Dylan J.; Hamp, Shannon M.; Venkatesulu, Erica; Shaw, Joseph A.; Whitaker, Bradley M.; Roddewig, Michael R.Montana and similar regions contain numerous rivers and lakes that are too small to be spatially resolved by satellites that provide water quality estimates. Unoccupied Aerial Vehicles (UAVs) can be used to obtain such data with much higher spatial and temporal resolution. Water properties are traditionally retrieved from passively measured spectral radiance, but polarization has been shown to improve retrievals of the attenuation-to-absorption ratio to enable calculation of the scattering coefficient for in-water particulate matter. This feeds into improved retrievals of other parameters such as the bulk refractive index and particle size distribution. This presentation will describe experiments conducted to develop a data set for water remote sensing using combined UAV-based hyperspectral and polarization cameras supplemented with in-situ sampling at Flathead Lake in northwestern Montana and the results of preliminary data analysis. A symbolic regression model was used to derive two equations: one relating DoLP, AoP, and the linear Stokes parameters at wavelengths of 440 nm, 550 nm and 660 nm, to chlorophyll-a content, and one relating the same data to the attenuation-to-absorption ratio for 440 nm, 550 nm and 660 nm. Symbolic regression is a machine learning algorithm where the inputs are vectors and the output is an analytic expression, typically chosen by a genetic algorithm. An advantage of this approach is that the explainability of a simple equation can be combined with the accuracy of less explainable models, such as the genetic algorithm.Item Lidar measurements of the diffuse attenuation coefficient in Yellowstone Lake(2020-03) Roddewig, Michael R.; Churnside, James H.; Shaw, Joseph A.Airborne lidar study of lake ecosystems is still a relatively unexplored field. In this paper we present measurements of the diffuse attenuation coefficient of downwelling irradiance (𝐾𝑑) obtained using a 532 nm airborne lidar in flights during 2004 and 2016 over Yellowstone Lake, Yellowstone National Park, Wyoming, USA. We compare the lidar measurements with MODIS 𝐾𝑑 data, discuss the impact that local weather and river inflows/outflows may have had on the data, compare to previous models of the diffuse attenuation coefficient, and examine several published relationships converting 𝐾𝑑 to Secchi disk depth.Item Airborne lidar detection of an underwater thermal vent(2017-08) Roddewig, Michael R.; Churnside, James H.; Shaw, Joseph A.We report the lidar detection of an underwater feature that appears to be a thermal vent in Yellowstone Lake, Yellowstone National Park, USA, with the Montana State University Fish Lidar. The location of the detected vent was 30 m from the closest vent identified in a United States Geological Survey of Yellowstone Lake in 2008. A second possible vent is also presented, and the appearance of both vents in the lidar data is compared to descriptions of underwater thermal vents in Yellowstone Lake from the geological literature. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)