Applications in GIS Module 3

This weeks lab was formatted differently then previous modules. The assignment required that four Esri learning courses be completed with minimum of 80% on each of the final quizzes. The courses required were as follows: Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, Sharing 3D Content Using Scene Layer Packages. The overall theme in this module was the use of 3D layers within a local or global scene. In manipulating 3D data the most used tool was Feature To 3D By Attribute, which converted 2D data to 3D using a specific attribute. For our purposes the Z attribute was used to determine the height of the 3D symbol. The Create 3D Object Scene Layer Package tool was used to package and export the scene for publishing. For Line of Sight analysis Construct Sight Lines was used to draw lines from an observer to the target feature. These lines were then run through the Line of Sight tool to determine which positions on the target feature were visible from the observation point. For viewshed analysis the attribute table was modified with offset, azimuth, and radius data then ran through the Viewshed tool to determine what terrain points were visible from the observation point. The height points were manipulated and it was ran again to achieve the desired results.

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Lab 5: M 2.2 Interpolation

This weeks module focused on identifying the best interpolation method for modeling the air quality over Tampa Bay. Four methods were tested using the same set of sample points Thiessen, Inverse Weighted Distance (IDW), Tensioned Spline (seen above), Regularized Spline. Thiessen Interpolation assigns all cells in the raster with the value of the nearest sample point. IDW calculates the value of all cells by considered multiple sample points nearby and giving closer points a higher weight than further points. Both Spline methods create a smooth surface over the sample points but the regularized version creates a smooth curvature regardless of the range of values in the sample meaning cell values can end up both above and below the minimum and maximum values found in the sample. The tension model attempts to fix this by constricted the curvature of values to the ranges found in the sample points.

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.

GIS Portfolio - Michael Malone

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