Hunter Donaldson UAS

 Purdue Wildlife Area "Green Up" Operation      This post is going to cover our final project for the course. The goal for this project was to show our capability to take UAS data and put it into the context of a Geographic Information System software package for further analysis and use. The overall objective of this assignment was for us to engage in start to finish UAS mapping project. We had to collect the data, create and orthomosaic and DSM, engage in analysis with ArcGIS Pro, create maps, and then generate a final report from the view of a GIS specialist. This post is going to be an overview of the final report that I created as a GIS specialist that was paid to do this type of work. UAS Mission Background:      A group of researchers at Purdue Wildlife Area (PWA) are interested in quantifying what areas, and how fast, portions of a recently burned prairie plot ‘green up’ as we move from early to mid-spring. You have been asked to fly over this recently burned area whe

 3D Model Project For this operation, we were assigned a flight crew and were told to decide on a location and our own data collection method for a 3D model and then create a report at the end. This post will be an overview of our operation and the report that I made following the creation of our model. I have other pervious post that get into more detail in the 3D model process that you can find  here  and  here . Introduction:      Crew #6 consist of myself and Connor Klinkhamer and we were tasked with picking a building and creating a quality 3D model with whatever method we wanted. We picked the clubhouse at the Purdue Golf Course as our building and we decided to do a double grid mission overhead, followed by an orbit mission, and finally a walk-around to try and collect the underside of the patio/overhang. The reason we choose this method is because we learned from a previous operation that a double grid NADIR flight is a very successful method to capture the top and surrounding

  Data Collection Mission Workflow For this post, we will   review of the process needed for a successful data collection mission. We are going to going to write up the workflow of what needs to be done for a successful data collection mission using a mock set of instructions. We will be using the ESRI Field Map app to go and gather some points and will be creating a map of those points. We will also be drawing a mission on our mobile device and demonstrating that. Finally, we will be filling out a mock metadata form related to the mission. The overall point of this post is to show our ability to understand the data collection process, along with correctly submitting GCP points on the ESRI Field Map app. To start off, I was assigned to crew #6 which consist of myself and Connor Klinkhamer. We were provided the operation area along with the GCP locations; then we, along with all the other crews had to go out and mark the GCP locations ourselves. First, it's important to understand t

 Object Based Classification Using Arc Pro In a previous post we engaged in object based classification of multi-band aerial imagery. In this post we are going to engage in object based classification of an RGB image. Although this image is not of an airport, the classification we will engage in is very much something that those managing and maintaining an airport are interested in. Maintenance of airport runways is very much like that of maintaining a freeway or a highway, but with objects that weigh much more, and put more stress on certain areas. Therefore, finding and addressing issues with cracks is very important, along with understanding how much area is permeable vs. impermeable from a drainage standpoint. In todays post we are going to do a classification analysis on an RGB image where we first create a series of landcover categories. We will then simplify those categories into permeable and impermeable. We will then perform a field calculation to determine how many square met

 Object Based Classification      The data collected by UAS goes well beyond a pretty picture of what your house looks like from above. When gathered correctly, with the right type of sensor for the right job, your UAS data can be the most elite in the industry. It is what the operator and analyst know in terms of limits and potential that matters.  For this post, we are going to be working with an online tutorial that walks you through classifying an aerial image to determine surface types. Our tutorial is going to be from  The ArcGIS Lesson Gallery . Once there, search for the Calculate Impervious Surfaces from Spectral Imagery Tutorial.  The first step is to access your data and open it in ArcPro. To begin our classification, we will first want to extract the spectral bands. Extract spectral bands : Multiband imagery uses natural color band combinations in a way the human eye would see it. We will create a new image by extracting the three bands that we want to show from the origina