Module 1 Lab: Visual Interpretations

The focus of this lab was to identify features in aerial images using various techniques. The top map used shows features that were identified using one of four methods, pattern, shape and size, association, and shadows. The bottom identified areas using texture and tone from fine to course and light to dark.

Comments

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.

Applications in GIS Module 4

This weeks module focused on running flooding and damage analysis in New Jersey and in Florida (seen above) This required turning LAS files into TIN and manipulation with the Raster calculator. The Region Group tool was a new tool used to exclude the low lying areas not attached to flood zone immediately on the coast. The most difficult part for me was setting up the symbology for the structures seen above. I ended up setting the primary symbology to Unique Values and then using the expression builder to designate the above classes that searched for the query fields created earlier in the assignment.

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.

GIS Portfolio - Michael Malone

Search This Blog