Emily McRae's GIS Blog

This module focuses on methods of classifying raster images, including supervised and unsupervised classifications. The above map shows an example of supervised image classification completed in ERDAS IMAGE and mapped in ArcGIS Pro. By creating features in an image to feed an algorithm, an image was classified based on several polygons created manually. These images were matched across the entire image to create classes. Spectral bands were then compared through histograms to identify the bands which need to be separated due to overlap in like features. The image was then displayed based on these classes, as well as acres belonging to that class.

This lab follows methods of spatial enhancement using various techniques in both ERDAS IMAGE and ArcGIS Pro. The process summary for the different sections can be seen below, as well as some finished map products that were produced using these methods.  Exercise 1: In this exercise, using the glovis.usgs.gov site, I explored the different freely available satellite data from various satellites and how to view the metadata. I did make an account with the website to be able to download data, however I did not receive the confirmation email yet so I am using the data that was provided already in the UWF drive and did not download any data from the website. The image file in the folder was in a .tar.gz format. To convert it into a .tif file, the process involved unzipping the file into a .tar folder, and then once again unzipping that folder into a folder which contains 7 tif files and then supporting documents. It seems this process progressively sheds layers of the original file into

This lab provided an introduction to using the ERDAS software for processing and deriving information about image and digital satellite data. Additionally, it provided an introduction to calculating and understanding the electromagnetic radiation as it related to satellite imagery.  There were two parts to this lab. See process summary below for more information about the activities performed in the lab, and struggles encountered along the way. Part A This lab largely focuses on practicing simple movements in the ERDAS program. To begin, I opened the ERDAS IMAGE 2020 32-bit program and added the files using the folder icon. To open files, the process involves selecting the file type, in this case raster, and then setting the file properties with opening them, including selections for the display, for multiple file types, and for framing such as fit to frame, and clear display. I explored the pan and zoom options, including zooming in and out, panning using the mouse, and using the

In this lab, a study area in Pascagoula, MS is classified up to the 1 st  and 2 nd  classifications under the USGS Standard Land Use / Land Cover Classification System, at a minimum mapping unit of 1:2000 inches, using an aerial photograph. The process involves standardizing best judgement in a way that can be replicable and reliable. Once all land parcels are drawn and classified, 30 random points are placed across the area and ground truthing is performed using Google Maps Street View. The percent accuracy is calculated based on the number of correctly identified points divided by the total number of sample points, multiplies by 100. The ground truth results yielded a 73% accuracy result.  The map below shows the results of the classification and ground truthing.  The following classifications were identified based on their characteristics: 11- Residential: Areas with regular patterns of small homes, green or grassy yards, and following small streets. 12- Commercial and Services: Are