GIS Portfolio: Ross Wygmans

One of the most common uses of remotely sensed imagery is to determine the extent and distribution of various land uses and land cover classes across a particular area. There are two broad categories of digital image classification -- unsupervised and supervised. Unsupervised classification relies on clustering algorithms to determine which land cover type each pixel represents, while supervised classification uses carefully selected training sites to guide the computer's classification.  In this weeks lab, we experimented with both unsupervised and supervised classification techniques. Below is a land cover map of Germantown, MD, that was created using supervised classification within ERDAS Imagine. Training sites were generated using the provided lat/longs for various land cover types including agriculture, fallow fields, water, urban areas, deciduous and mixed forests, and grass. The smaller inset map shows the Output Distance file. Brighter pixels in this image indicate areas t

There are several techniques that can be employed within ERDAS Imagine to identify specific features using multispectral imagery. These features can then be displayed using different color band combinations to make them easier to distinguish visually. In general, the four steps used to identify features in ERDAS are: Examine the histogram for shapes and patterns in the data. Visually examine the image as grayscale for light or dark shapes and patterns. Visually examine the image as multispectral, changing the band combinations to make certain features stand out. Use the Inquire Cursor to find the exact brightness value of a particular area. In this lab, we were asked to identify three features within a multispectral image using these methods of interpretation based a given set of criteria. We were then asked to select an appropriate color band combination to display these features in a way that makes them clearly stand out from their surroundings.  The map below displays the first feat

This week's lab introduced us to ERDAS Imagine, a raster-based software used to extract information from aerial imagery. We learned the basics of adding layers, examining metadata for each layer, creating subset images, and exporting images to be further manipulated in ArcGIS Pro.  The map below shows the land cover of an area within Olympic National Park. This area is a subset of a larger Landsat Thematic Mapper (TM) image that was resampled to 30m and had the thermal band removed. This image was pre-processed using ERDAS Imagine and was exported to ArcGIS Pro to create the final map output. Each color on the map represents a different land cover class, and the total acreage represented by each cover class is provided in the legend.