Hunter Fishback - Academic GIS Portfolio

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on January 09, 2024

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GIS Certificate Capstone - Blog Post #3 - LinkedIn Update

When updating my LinkedIn profile, I made a conscious decision to highlight my growing specialization in GIS, as this field is what I am passionate about and actively pursuing more technical roles for. From the content of my work experience to the visual impact of my cover photo, I wanted GIS to stand out immediately to anyone visiting my profile. I carefully selected the skills section to reflect the tools and techniques I’m confident in, as I wanted to ensure that I can speak genuinely and competently about each of them in conversation with recruiters or interviewers. Most importantly, I approached this profile update as an honest reflection of my professional journey. I’ve worked in so many different industries, and I’m proud of each experience for shaping who I am today. Profile link: https://www.linkedin.com/in/hunter-fishback-88a08312b/

Module 2 - Land Use Land Cover Classification (LULC), Ground Truthing & Accuracy Assessment - Lab Assignment

This module tied in the visual interpretation methods I learned in module 1 to a real-world application: Land Use Land Cover Classification (LULC) assessments. I was to apply visual interpretation and recognition elements to an aerial photo of Pascagoula, MS to identify and classify areas along the photo and construct an LULC map. Next, I was to develop a sampling plan for ground truthing and implement it to properly assess my LULC map for ground truthing. To construct my LULC map of the aerial photo (provided as a .tif file), I first opened the photo in ArcGIS Pro. I looked at the photo both from large and small scales and developed a plan. I created a new feature class in my project's geodatabase with a polygon feature type and created fields for both the code I would classify each polygon, as well as a description of the land use or land cover. I began digitizing polygons, starting with larger scale features and working my way towards smaller ones. I did not get into extremely...

Module 5 - Unsupervised & Supervised Image Classification - Lab

As the title suggests, this module's exercise focused on the concepts and methods required to carry out unsupervised and supervised classification. While I gained experience completing a classification of both types using instruction from the lab assignment, the key focus was to complete a supervised classification on my own using a fresh image. For this assignment, I was given an aerial photo of Germantown, MD and tasked to create a land use map of the area using supervised classification. All of my data processing for this assignment was completed in ERDAS Imagine and then used to create a map layout in ArcGIS Pro. I was given coordinates for a few land use types: urban/residential, grasses, deciduous forest, mixed forest, fallow field, and agriculture. For each set of coordinates I set a cursor using the Inquire (Legacy) tool in ERDAS Imagine. At each cursor, I created an AOI (Area of Interest) using the Grow tool. Each AOI was carefully created by adjusting the N...

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