Applications in GIS Module 6B

The second part of this weeks module focused on creating a corridor modeling the ideal locations for black bear travel between two protected areas in the Coronado National Forest, AZ. The factors considered for suitable travel zones were distance from roads, elevation, and landcover. Elevation and landcover rasters were given and were reclassified to fit the given criteria. The roads shapefile was ran through the Euclidian Distance tool and the resulting raster was reclassified to fit the given criteria. All were combined using the Weighted Overlay tool and the resulting raster was used as the cost raster in the Cost Distance tool with the two protected areas. The two resulting rasters from the Cost Distance tool were processed with Corridor Tool. A threshold was chosen to limit the suitable area to within 10% of the most suitable locations resulting in the corridor seen above.

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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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