Following undergraduate UW students from the applied remote sensing classes (fall and spring) have presented their research work in Wyoming Undergraduate Research Day:
Total: 42 and counting
Gettinger A, Knowles D, 2018. Estimating water surface area from satellite images: Effects of data preprocessing.
Wirsching E, 2017. Quantitative Rating System for Imagery Acquired with Unmanned Aerial Vehicle.
McCarragher C, 2017. Quantifying Analyst Bias in Mapping Flooded Areas from Landsat Images.
Elbert C, 2017. Mapping Variations in Crop Growth Using Satellite Data.
Collins J, 2016. Mapping Differences in Alfalfa Growth Patterns using Landsat 8 images.
Friday C, 2016. Estimating surface area of three Wyoming Reservoirs using multi-year Landsat data.
Jakopak R, 2016. Evaluating the spectral separability of crop classes in multi-temporal Landsat imagery.
Sloan C, 2016. Selecting optimal thresholds for mapping water bodies in the Powder River Basin using Landsat images .
Wirshing E, Alexandar J 2016. Mapping beetle infested stands in Medicine Bow National Forest: Importance of spatial and attribute accuracy of field data.
Galli G, 2015. Influence of Sun Angle differences in extracting surface area of water bodies.
Goodman I, 2015. Effect of Sun Incidence Angle on Classifying Water Bodies in Landsat Images.
Swoboda-Colberg S, Sheets C, 2015. Quantifying Producer Error in the Unsupervised Classification of Reservoirs.
Balzan B, 2015. Delineating crop management zones within alfalfa fields using Landsat images.
Talbert P, Neislanik N, Bales H, 2015. Evaluation of spectral variability of irrigated crops in Landsat 8 images.
Lermon R, 2014. Prescribed burn severity mapping using Landsat 8 data.
Richardson E, 2014. Monitoring Aspen Phenology along an Elevation Gradient using MODIS data.
Tuthill Z, 2014. Mapping Water Bodies in Thin Layer-Cloud Contaminated Landsat Imagery.
Allen E, 2013. Monitoring crop growth before and under center-pivot irrigation system using multi-temporal Landsat images.
Booth C, 2013. Estimating intra-annual changes in the surface area of Sand Mesa Reservoir #1 using multi-temporal Landsat images.
Collier E, 2013. Mapping Burn Severity of the Marking Pen Prescribed Burn in the Seminoe Mountains using pre- and post-fire Landsat Thematic Mapper images.
Hessenthaler C, 2013. Tracking sugar beet/corn growth in a Wyoming farm using Landsat images.
Richardson K, 2013. Limitations in Delineating Lake Shoreline in Cloud Contaminated Landsat Images.
Steinhoff C, 2013. Mapping Changes in Reservoir Surface Area Using Landsat Thematic Mapper Images.
Terry B, Beaman B, 2013. Characterizing analyst bias in unsupervised classification of Landsat images.
Thoman M, McCollum K, 2013. Assessing Transferability of Landsat-derived NDWI Values across Space and Time.
Hutchinson O, 2012. Monitoring post-wildfire vegetation regeneration in the Northern Black Hills of Wyoming using Landsat images.
Pindell J, 2012. Mapping aspen phenology with MODIS 8-day composites
Schiche B, 2012. Mapping Changes in the Surface Area of Woodruff Narrows using Landsat Images.
Thoman M, 2012. Mapping wheat growth in dryland fields in SE Wyoming using Landsat images.
Cobb B, 2011. Mapping Forest Burn Severity Using Non Anniversary Date Satellite Images.
Hudson C, 2011. Remote Sensing of Vegetation Response following Bark Beetle Attack in the Snowy Range Mountains, Wyoming.
Perry T, 2011. Spectral Reflectance of Salt Cedar and Cottonwood along the Powder River, Sheridan, WY.
Arendt P, 2010. Utilizing Landsat TM and Forest Service aerial survey data for mapping Mountain Pine Beetle outbreak in Medicine Bow National Forest, WY.
Jolivet M, 2010. Mapping Changes in Asian Migratory Locust Habitat in Central Asia using Moderate Resolution Landsat Imagery.
Shepperson K, 2010. Assessing the suitability of Landsat satellite data for distinguishing cheatgrass infested sites near Midwest WY.
Stephens AJ, Stanton BJ, 2009. Mapping Burn Severity within the Grizzly Gulch Fire Using Remote Sensing Techniques.
Salerno V, 2008. Satellite Remote Sensing Technology for Identifying Variability in Sugar Beet Growth.
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