GIS Portfolio: Ross Wygmans

GIS Day is an annual global event dedicated to celebrating and sharing Geographic Information Systems with the world. Since GIS Day isn't until November 16 this year, I decided to create my own event. The audience for this event will be my close friends and family because I oftentimes have difficulty concisely explaining what GIS is to them. This will be an informal gathering hosted at my friend Michael's house since he has a huge projector screen set up in his living room. This event would start off with an introductory video of a TEDx talk by Dan Scollon explaining what GIS is and how it is revolutionizing the way we view the world. I would then show some of my own examples of some really cool applications of GIS and explain some of the types of questions that GIS can help us solve. Finally, I would serve an amazing taco dinner! 

LinkedIn is an online social network where professionals can connect, share, and perhaps even find that dream job (or dream employee if you're recruiting). It's like a more courteous and less political Facebook for your career. One of the tasks for our internship this week was to update our LinkedIn profiles to include our GIS internships and highlight skills learned throughout the GIS certificate program. This was helpful for me as I did not even consider adding my GIS internship to my LinkedIn page. I can definitely see the value of including this practical GIS experience since employers are oftentimes looking for experience, even for entry-level positions.  One challenge I have with LinkedIn in finding the balance between being original and creative without forfeiting professionalism. Many profiles I have seen are very generic with few distinguishing characteristics. One opportunity to stand out immediately is with a catchy headline. My current headline is:  GIS Nerd | Tree

Over the last two weeks, we have been learning more about the various industries using GIS and exploring specific positions within some of these industries. It is inspiring and exciting to see how GIS is utilized by such a wide range of industries, from natural resources to energy to defense. I spent some time looking for GIS opportunities within the forestry/conservation sector since this is the direction that I hope to take my career. I came across an Enterprise GIS Specialist position with F&W Forestry Services, an international forest management and consulting firm, that I decided to apply for. I may not be entirely qualified for this position, but I figured it never hurts to "toss my name in the hat". Besides, I previously worked for F&W as a Field Forester after completing my undergraduate degree in forestry, so perhaps this will give me a slight edge over other applicants with more GIS experience than me. 

My internship this semester is being merged into my current Environmental Specialist position with Alachua County's Office of Land Conservation. Since GIS is used regularly within our program to produce maps and perform basic geospatial analysis, completing my GIS internship through my employer was the logical choice for me. I'm grateful that our Director and Program Manager were so supportive in facilitating me in this arrangement.  One of the main projects I will be working on throughout this internship is creating an updated feature class of all the properties that have been nominated for acquisition through our conservation program, Alachua County Forever, since the program's inception in 2000. One of the previous Program Managers was maintaining a database with this information until 2015, but his database was so convoluted and cobbled together that once he left the program, no one was able to decipher how to use the database. I will need to dig through a lot of data a

One of the most common uses of remotely sensed imagery is to determine the extent and distribution of various land uses and land cover classes across a particular area. There are two broad categories of digital image classification -- unsupervised and supervised. Unsupervised classification relies on clustering algorithms to determine which land cover type each pixel represents, while supervised classification uses carefully selected training sites to guide the computer's classification.  In this weeks lab, we experimented with both unsupervised and supervised classification techniques. Below is a land cover map of Germantown, MD, that was created using supervised classification within ERDAS Imagine. Training sites were generated using the provided lat/longs for various land cover types including agriculture, fallow fields, water, urban areas, deciduous and mixed forests, and grass. The smaller inset map shows the Output Distance file. Brighter pixels in this image indicate areas t

There are several techniques that can be employed within ERDAS Imagine to identify specific features using multispectral imagery. These features can then be displayed using different color band combinations to make them easier to distinguish visually. In general, the four steps used to identify features in ERDAS are: Examine the histogram for shapes and patterns in the data. Visually examine the image as grayscale for light or dark shapes and patterns. Visually examine the image as multispectral, changing the band combinations to make certain features stand out. Use the Inquire Cursor to find the exact brightness value of a particular area. In this lab, we were asked to identify three features within a multispectral image using these methods of interpretation based a given set of criteria. We were then asked to select an appropriate color band combination to display these features in a way that makes them clearly stand out from their surroundings.  The map below displays the first feat

This week's lab introduced us to ERDAS Imagine, a raster-based software used to extract information from aerial imagery. We learned the basics of adding layers, examining metadata for each layer, creating subset images, and exporting images to be further manipulated in ArcGIS Pro.  The map below shows the land cover of an area within Olympic National Park. This area is a subset of a larger Landsat Thematic Mapper (TM) image that was resampled to 30m and had the thermal band removed. This image was pre-processed using ERDAS Imagine and was exported to ArcGIS Pro to create the final map output. Each color on the map represents a different land cover class, and the total acreage represented by each cover class is provided in the legend.