Applications in GIS Module 5

This weeks module involved completing a damage analysis of the land parcels contained within the study area (blue square). In order to do this a point feature class was created and domains were setup (structure damage, wind damage, inundation, and structure type) to restrict the fields to only allow for the desired entries. Then points were created on each parcel and populated using the built in swipe function to compare the pre and post-storm photos. Once these were all created the estimated coastline was digitized using the pre-storm photo and three buffers were created (100 meter, 200 meter, and 300 meter) on the coastline. I created query fields on the damage structure layer and using select by location and select by attribute queries I was able to determine the quantities of the buildings in each zone which were used for the analysis seen below.

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Module 5 - Unsupervised and Supervised Image Classification

This weeks module focused on classifying images using multispectral signatures. Above you can see the completed classified land cover of Germantown, Maryland. To create this image above signatures were collected that correlated to each required feature. Then bands were chosen (R:4 G:6 B:5) that contained the largest separation amongst features to minimize spectral confusion. In the above image roads and urban areas were often confused leading to a much larger area of roads than actually exist. The inset map contains a classification distance map which displays the distance each cell is (spectrally) from the sample points with brighter pixels being further than darker pixels. This indicates that brighter areas have a higher chance of error.

Lab 3: M 1.3 Assessment

Lab 2: M1.2 Standards

The task for this module was to perform an accuracy assessment on two versions of the same road network in Albuquerque. One was mapped by the city of Albuquerque (yellow) and one by Street Map USA (red). In order to perform the accuracy assessment twenty points per network needed created at matching intersections on each network. Then a reference point was digitized based on satellite imagery to represent the "actual" location for each intersection. XY coordinates were assigned to all sixty of the created points and the values were exported to an excel spreadsheet. From there the RMSE for each of the networks were calculated using the reference points. With this the accuracy assessment was completed resulting in the following: ABQ_Streets Positional Accuracy: Tested 14.84 feet horizontal accuracy at 95% confidence level. StreetMapUSA Positional Accuracy: Tested 158.56 feet horizontal accuracy at 95% confidence level.

GIS Portfolio - Michael Malone

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