Remote Sensing: ERDAS Imagine

Lab 5a objectives were to calculate wavelength, energy of EMR, and frequency.  The other part of the lab was to learn how to use ERDAS Imagine and view the data with the Viewer.  The final part of the lab was to take subset data and prepare a map.

The first part of the lab compared the relationship between wavelength and frequency.  The calculation compared wavelength to energy.

Once in ERDAS Image, I played around with various tools such as zoom and pan.  It was also important to go through the different options for the raster after adding it to the table of contents.  After adjusting options. added a Viewer #2.  After having the image of Washington, I adjusted the color bands to show the forest land.

Another tool I learned was adding a column to an attribute table.  This helped show the power of ERDAS Image and exporting to ArcMap.  Using the Inquire Box tool from the Home tab allowed me to select an area of the Washington State image and then subset created the smaller image.  Exporting this into ArcMap allowed me to change the symbology to show the different classes and then prepare a map.

ERDAS Image and Classification

Module 4: Ground Truthing

Week four was another building week.  The objectives of this lab was to collect sample points, and check the accuracy of the classifications using Google Street View.

I decided to select 30 points at random.  I was concerned it would be difficult to be consistent with the systematic approach.  Randomly selecting points, I felt it would be very unbiased.  Once the points were selected, I selected one point and matched it up to Google Maps.  Once I located the point on Google, I zoomed in as much as possible.  Street view allowed me to see the front of the buildings if the point was on a building.  From seeing where the point actually fell, I could determine if my original classification was correct.

Most of my inaccuracy occurred when dots fell in the polygons that were classified as residential.  A lot of the dots ended up falling on trees or buildings that look like houses.  After using street view, it was clear they were commercial properties or industrial.  My accuracy rating was 67%.

Ground Truthing of LULC Classification

Lab 3: Land Use/Land Cover

This week's lab applied skills I learned from Lab 2, but now identifying features of land use and land cover.  The main objective was to find features and classify them using LULC classification,

Categorizing by land use and land cover

While identifying features in the aerial image, I wanted to look for patterns, association, and shapes to identify land use and land cover.  I started with the largest areas which were mainly residential features. This was easy to identify because of the small features, in a pattern.  I also was able to see the drive ways and yards.

The next thing was identify the different bodies of water.  This was easy to identify because of the color/shade and shapes of these features.  It was clear to see a long, narrow feature was a river.  It was also easy to identify the deciduous forest and shrubs by looking at texture and grouping with each other.

I struggled a little with identifying the different between commercial and industrial features.  Both require large square/rectangle buildings.  Parking lots can also be found at both.  However, commercial seemed to be along major road ways.  Industrial also seemed to have other smaller buildings besides the main building.  Industrial also seemed to have open land used for storage of possible trucks where as that is not necessary for commercial use.

I can see how digitizing aerial images can take months and a lot of patience.  I hope to improve my skills to make identifying features easier.

Remote Sensing Lab 2

Lab 2's objectives were to interpret textures and tones of aerial photographs.  Besides looking at textures and tones, also learn to identify land features and compare the features in true color and false infrared color.

The first map I created by looking at the aerial photograph to find very light to very dark tones.  Next, looked for very coarse to very fine textures.  The extremes of the tones and textures are easy to identify, but the tones and textures in between take more time to identify.

The second map I created to show different features in the aerial photograph.  Some features were easy to see because of the shape such as a road and vehicles.  It was interesting to see how much seeing a shadow really helps identify features like the trees and light poles.  Using patterns to identify features really helps identify features when originally they were hard to tell what they are.  Seeing a lot of smaller buildings along the roads helps tell me its a residential neighborhood.  The pier was easier to identify by looking at the surroundings.  

The last part of the lab was applying the skills of identifying land features and comparing those with different colors.  The first photo was in true color which made identifying the features easier.  Then the next photo was the same area but false infrared color.  It was interesting to see how objects that were green ended up being red in the false infrared colors.