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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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 2 Lab: Land Use / Land Cover Classification, Ground Truthing and Accuracy Assessment

The above map shows digitized land use/land cover (LULC) classifications for Pascagoula, MS. A set of 30 random sampling points were created post creation of the LULC layer and they were verified in google street view to determine accuracy. Based on these points the LULC model was found to be 86.6% accurate.

Applications in GIS Module 2

This weeks module focused on using LiDAR data to determine the height and density of a section of Shenandoah National Forest in Virginia. Mostly this involved converting the LiDAR data to a raster and running various tools to achieve the desired datasets. New tools included LAS Dataset To Raster, LAS to MultiPoint, Con, Plus, Float, and Divide. Additionally a histogram was created to display the heights of cells in the forest. Density and DSM: Height with Graph: LiDAR:

GIS Portfolio - Michael Malone

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