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Module 3 - Land Use / Land Cover Classification

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This week we were instructed to differentiate land uses and classifications on a map using LULC classification. The following is information from my process summary explaining the classifications and how I went about creating each feature: 11 – Residential: (Where people live i.e. apartment complexes, subdivisions, trailer parks, neighborhoods) I labeled areas with small houses spaced sporadically or close together as residential. Because houses were located all over the map, this class takes up the largest portion. 12 – Commercial Services: (Places of business and public service i.e. stores, restaurants, schools, office buildings) I labeled areas by the major road as commercial services because this is typically where those services are located. I also found what appeared as a school, a retail store, and a cluster of uniform buildings with flat roofs as commercial.  14 – Transportation, Communications, and Utilities: ( Areas used for transport and travel ) I labeled the...
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Module 5b - Intro to ERDAS Imagine and Digital Data

There was not a map deliverable for this lab, but in exercise 4  we created a map that showed the percent area of the most erosive soils. This exercise reminded me of previous tasks I have completed with ArcMap, but it seemed to be an easier process. I can appreciate how this function will be useful for many different data types including stand type, population density, and other trends that can be uncovered by using formulas to select specific characteristics.
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Module 8 Lab: Isarithmic Mapping

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This week we created two isarithmic maps, one with continuous tones and the other with hypsometric tints. It was important to explore both of these mapmaking styles as they display the same data presented in different ways.  The first important thing to note is how this data is collected and interpolated.  By implementing data prepared using the PRISM method, the maps we created are interpolated using the relationship of  location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position,  and orthographic effectiveness of the terrain to precipitation. This algorithm, created by Chris Daly, takes  into account variants such as rain shadows that would otherwise skew data on a map focusing strictly on the  relationship of elevation to precipitation. The first map we created was one with continuous symbology. Since the data was already presented with a stretched color scheme, there was not much ...
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