Nathan Berger Portfolio: Geographic Information Systems

  Landcover Classifying  Today's exercise was all about classifying multispectral imagery based on the pixel values of each spectral signature or layer in our image. An example would be trees, water, buildings etc... all getting assigned a certain value to be recoded in our image classification.  Original Image of Natural Color Same Image Color Infared  By classifying our image, it becomes a thematic raster file. We can "color code" our imagery to determine what types of land use exists in our image.  From there, we can calculate the total acreage of land use for trees, grass, buildings (urban development) and even the road ways in a given area. This is extremely powerful to provide to local government policy makers in determining different decision making techniques when deciding on urban development, deforestation, building new roads, protecting environmental waterways and seeing how much of an area ca change overtime.  Land Cover Analysis - Supervised Classified Raster

Erdas Imagine and Arc GIS Pro:  Spatial Enhancement for Multispectral Imagery Hello again! Today we are going to be using some Multispectral Imagery in Erdas Imagine and Arc GIS Pro. Our imagery has up to 6 bands, and can detect Infrared signatures (False Color for vegetation and other land features. We can classify our imagery and use different symbology based on the band colors and pixel values in our imagery to distinguish different land features.  The imagery used today was provided by UWF through the USGS Landsat 4-5 TM C1.\ https://glovis.usgs.gov/ Here are some steps that will need to happen in order to export out our maps... 1. Examine the histogram for shapes and patterns in the data. 2. Visually examine the image as grayscale for light or dark shapes and patterns. 3. Visually examine the image as multispectral, changing the band combinations to make certain features stand out. 4. Use the Inquire Cursor to find the exact brightness value of a particular area. Histograms are ke

 Remote sensing with Erdas Imagine Today's exercise involved me using Erdas Imagine for the first time, which was pretty cool!  Erdas Imagine is a remote sensing software program similar to a lot of other imagery analysis software programs, such as remote view and QGIS. Today, we were able to view different types of satellite imagery from LANDSAT of multispectral imagery and different resolutions. We also were able to change the colors of the different bands of our image to identify features better such as vegetation and water in our land cover.  We start with a near infrared color scheme, making the vegetation look red in color for the forests and tree areas.  In the image, below, we changed our colored bands to the RGB or red, green, blue color scheme to have a more natural look in which helps us identify the different land features. I then added a new field for area, and calculated the area for each type of land classification in hectares. This allows use to then export our rast