G.Hudson Mastering GIS

 In the final assignment of Photo Interpretation and Remote Sensing graduate students were tasked with applying the skills learned in the course to a topic of their choice. I decided to perform  land use/land cover change classification on a settlement in Abaco, The Bahamas based on the impact of Hurricane Dorian in 2019. The maps below display the study area, classification performed on imagery before the hurricane and classification performed on imagery after the hurricane. There are some noticeable changes in the classes, that have been quantified and can be seen in the table following the maps.  Map displaying the study area.  Map displaying land use/land cover classification of  Marsh Harbor, Abaco, The Bahamas, before hurricane Dorian.  Map displaying land use/land cover classification of  Marsh Harbor, Abaco, The Bahamas, after hurricane Dorian.  Table displaying the quantified change as a result of hurricane Dorian. 

  The focus of this week's class and lab was unsupervised and supervised classification. Once we covered all of the theory during lectures we were tasked with working through a guided unsupervised and supervised classification. We then had to perform our very own supervised classification on Germantown, Maryland. This involved creating areas of interest (AOI's) which act as training sites, performing the classification and then recoding the classification to limit the number of classes. The map above is my end result.  This lab was pretty enjoyable and it was nice to work through the classification process. My downfall, however, is sometimes overthinking steps and processes. While it is important to be careful and attentive, it's also important to trust the process. 

 As the title of this week's blog suggests, week 4 of Remote Sensing and Photo Interpretation was focused on various types of spatial enhancement, multispectral data and band indices. Spatial enhancement is a key preprocessing step that usually takes place before using or conducting detailed analysis of imagery. Some of the spatial enhancement processes explored include low pass filter, high pass filter and focal statistics. In addition to spatial enhancement, geospatial professionals also usually view the properties of the multispectral data to help understand what the imagery is depicting. To garner more information about the imagery we explored the histogram and manipulated it to bring attention to features that may have otherwise not been seen, and we learned about the inquire cursor that displays information on individual pixels in an image. Lastly, we learned how to create a Normalized Differential Vegetation Index (NDVI) and the various band combinations used to highlight ce...

The second week of Photo Interpretation and Remote Sensing was focused on Land Use/Land Cover Classification and Accuracy Assessments. Students were tasked with using the USGS Land Use/Land Cover classification system to classify a provided image of Pascagoula, Mississippi at level I and II. Once the classification was complete we then had to determine the accuracy by 'ground truthing'. As this is an online course we used Google Maps/Earth to verify random points and then calculated the accuracy.  I ended up with a fairly low accuracy. This was mostly due to errors in my residential class. I had a few buildings that were on the cusp of a residential and commercial area but ended up in the wrong class. I also had an instance of a church being located in the middle of a residential area. My highest accuracy was in the water class, 100%. 

Exercise 1 deliverable Exercise 2 deliverable Welcome to the first week of GIS5027 - Photo Interpretation and Remote Sensing! The first week of class introduced students to Remote Sensing, its fundamentals and the basis of interpreting images, inclusive of identifying features. As such, the lab assignment helped students learn how to identify various tones and textures in an image, as well as identify features based on shape/size, shadow, pattern and being associated with another feature. We also had the opportunity to distinguish features based on true color imagery and false color infrared imagery. Pictured above are the deliverables for exercise 1 and 2. For exercise 1 I identified the various tones and textures found in the image. For exercise 2 I identified 3 objects based on shape/size, shadow, pattern and association.