My Background

My education is based in the realm of earth science and analysis. During my undergraduate, major courses that shaped my career in data analysis were: 

• Statistics I & II

• Spatial Statistics & Analysis

• GIS I & II

• Remote Sensing I & II 

• Climate Modelling 

My time in grad school allowed me to expand these skills into a research project of my own design. ​This includes snow cover analysis via visible and infrared remote sensing methods. Visible remote sensing is heavily hindered by the presence of clouds. In order to remedy this, I implemented a multi-step cloud reduction algorithm to gain a better understanding of snow cover variability in my study region. This methodology is as follows:

• Same day combination of MODIS Terra/Aqua and VIIRS satellite images 

• Adjacent temporal deduction of +/-2 days to replace cloud pixels

• Spatial filter featuring a 3x3 kernel/moving window 

• SNOWL- a regional snow line method using elevation to replace cloud pixels based on their relative elevation value 

Further information on the methodologies stated here are available in the Rstudio tab (link below). ​

This process required management of large datasets as the study period was between 2003-2019. Data clean up and manipulation was required in many stages. Preprocessing steps like reprojection, cropping, masking, mosaicking etc. were all done in QGIS, Rstudio and FME. 

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Results showed patterns of snow off date throughout the study period were analyzed with information gathered from NOAA on the Oceanic Nino Index, Pacific North Atlantic Index and the Pacific Decadal Oscillation. Understanding the extent of these indices influence on snow cover variability was done using multiple linear regression analysis. 

Overall, this process is where my love for data and analytics came from. This site is dedicated to displaying my processes, simplifying them and presenting them as well as expanding my skill set to other platforms and datasets. 

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Thank you for visiting! 

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