Lab 4: M 2.1 TINs and DEMs

This module focused on exploring triangular irregular networks (TINs) and digital elevation models (DEMs). The project culminated to creating a TIN and a DEM from the same set of points and then comparing the contours created from each. While I see the value and versatility of the using a TIN to quickly symbolize many different aspects of the data quickly, I believe the contours created from the DEM (red) are more accurate than those created using the TIN (black). The TIN contours are restricted to the surfaces of the TIN hence in the final product they appear jagged which lowers their overall accuracy.

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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 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.

GIS Portfolio - Michael Malone

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