Nathan Berger Portfolio: Geographic Information Systems

 Today is a blog post on Coordinate Systems! There are two main types of coordinate systems:  Geographic (GCS) and Projected (PCS) We simply want to take data mapped in our world (3D) and lay it on a flat map. This is called projecting it into 2D. A GCS is where the data is located and a PCS is how to draw that 3D data on a flat map. I would think of a GCS a globe, while a PCS is a flat rectangular map you see in a textbook. However, when we project Geospatial data onto a flat surface, there will be distorted data such as the shape, size, area, direction or angle of the object being projected.  This is why it is crucial to understand the different kinds of coordinate systems to accurately portray your data on your map. The ones I will be showing you below are Conical Projected Coordinate Systems and are great for doing world maps. These would not be great to use for using local maps for states, counties, or cities.  https://www.esri.com/arcgis-blog/products/arcg...

  Today I am going to show some maps I put together using a variety of data sets and techniques.  But the main focus of todays article is to talk about Cartography! Cartography is the following - "The science of making maps" Cartography is how we make great maps. It is the process of using map essential elements, such as a title, legend, scale, and even orientation to help an end user understand what a map is about. It tells us map creators what we need to put on a map for it to make sense. If you put a bunch of points on a map with colors and data, we need to tell the map reader what that data symbolizes. This is why legends are crucial when making maps. Cartography talks about using symmetry to orient these map elements and texts on your map in a way that is appeasing to a viewer.  In a way, Cartography and map making is a lot like being an artist, or even graphics design. The processes used are very similar, and helps marketers target particular audiences with their ma...

  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 Cl...

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. Histogra...

 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 ...

  LULC Classification and Ground Truthing In this exercise, we are using imagery located at Pascagoula, MS, to digitize the land cover surrounding the area and classify it by land use and description.  In order to do this, I decided to use a land cover legend to digitize the different land types using a polygon feature class. The different land types were given different colors to differentiate the symbology of the land cover.  I then used 30 sample points called "truthing points" to test the accuracy of my land cover results. I simply dropped 30 points on my image and then exported them to google earth using a kmz tool. I then zoomed in on each sample point and viewed the google Earth Image in 3D to see fi my land cover was classified accurately or not. I then coded my results in my truthing feature class to identify which points were accurate and wh9icvh ones were not.  Close Up View Of one Sample Point The final map below shows the sample points in accuracy i...

 Aerial Photography and Identifying Features Texture, Tone In any remote sensing analysis, we need to identify features in imagery. In order to do just that, we use a few tools and tricks to help us along the way. The tone and texture of objects in an image help us to identify different areas based on light vs. dark, and even fine objects vs. coarse or choppy. This allows us to make out areas of dense vegetation, bodies of smooth or still water, and manmade structures such as runways of an airfield.  Size, Shape, Patterns, Association, Shadows The size and shape of objects help us to identify structures, such as a house or building. We can indeitfy manmade structures such as the pier in the image below due to its rectangular shape and length, but thin width running out into the water.  Take a look at the images below True Color                               ...