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.

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GIS Programming Module 4

The below images are the output of the script for module 4 followed by the flowchart for the script. This module covered manipulating and editing data in feature class attribute tables. What you see below is the result of creating a new geodatabase, copying all shapefiles saved to the module 4 data folder to the new geodatabase, a search cursor identify the name, feature, and population of all New Mexico county seat cities populations. Finally it prints out a dictionary that has been populated with the county seat names and populations. Output: Flowchart:

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.

GIS 6005 - Module 6

Because the data provided for the above map contained positive and negative values which ArcMap does not support for proportional symbols, the data had to be modified. All states with positive values were exported to a new shapefile and the same was done with states with negative values. This was all that needed done with the positive valued states. However, for the negative valued states a new field needed added to the attribute table and it had to be populated with the absolute value of number of jobs lost to convert them to positive integers. To prepare the data for bivariate visualization classes needed to be made. To do this, three class quantification was applied to each desired variable to divide each into three relatively equal groups. Then all values in the first group of the first variable were classified with an 'A', then the second group was given a 'B', and the third was given a 'C'. the same thing was done with the second variable however 1, 2 ,...

GIS Portfolio - Michael Malone

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