Emily McRae's GIS Blog

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

This map demonstrates, through a grey scale aerial image, how one can classify features on a landscape visually using tone and texture. Tone being the scale of lightness to darkness and texture being a scale of very coarse to very fine. These classifications were done by eye, and may be subjective to the analysts interpretation, as well as the images clarity, scale, and resolution. This map identifies features in a greyscale aerial photo by 4 means: pattern (Objects/places were chosen that are clearly visible and interpretable as they are part of a larger repeating pattern, but that had they been presented in isolation or at a very large scale, would not be clear as to what they are), shadows (Objects were chosen which could not have been identified had they not been casting a shadow which revealed information about what the object is), association (Objects were chosen that could not be identified without looking at the surrounding environment and making logical connections about what

This lab explores the complexities in representing real geographic phenomena accurately, both in terms of physical features and socioeconomic or other patterns, based on decision made by the cartographer. It looks at the distortions that can occur when data are displayed at varying scales, resolutions, and the very interesting and perplexing issue of the modified area unit problem (MAUP). The MAUP is an of yet, unsolved issue in spatial analysis, by which aggregation at different boundaries can yield very different results for analysis. This can even be exploited with gerrymandering, a practice of drawing political boundaries to intentionally to group voters and representation in favor of a political party.  This lab looked at the affects of scale on vector data, resolution on raster data, and measuring the degree of gerrymandering in congressional districts within the contiguous US. I will briefly detail the analysis process for each, and provide examples and explanations of the findi

This weeks module focused on surface interpolation using the Spline, Thiessen, and Inverse Distance Weighted (IDW) methods.  In the example below, water quality sample points taken across Tampa Bay were used as input data and run through the 3 surface interpolation methods to get an understand of water quality across the entire Tampa Bay study area. The methods are compared based on their ability to achieve a good impression of what water quality may actually look like across the surface.  I will detail how each method works,  their strengths and limitations as it applies to this example, and the result of this method when applied to Tampa Bay water quality assessment.  I will note that none of these methods take into account other highly influential factors of the water landscape including flow direction, elevation, watershed, aquifer boundaries, etc.  Spline Conceptually, how it works: The spline method interpolated the value of the surface where data points are not recorded, by pas