Lab 7 - Multispectral Analysis

GIS 4035

In this weeks lab we looked at image histograms, how to operate the inquire cursor, interpreting images, utilizing the help function, and interpreting digital data.  When then utilized the skills that we learned in the exercises to properly locate and accentuate a specific location within an image that was provided.  In this case, an image of forest surrounding the Olympic Mountains in Washington State.

The first Figure located was a dark waterbody.  Utilizing near infrared RGB settings to make the water darker, the reader can easily identify the dark waterbodies.  As described in the map the pixel values ranged from 12 and 18 in band 4.  This was observed utilizing the inquire cursor in ERDAS.

The next Figure that I located was the snowcapped mountains.  These features produced pixel values of 200 in the 1-4 band layers.  These features also had a large spike between pixel values 9 and 11 in Bands 5 and 6.  These features can be observed in the image above as yellow.

My final figure was an area of water that layers 1-3 seemed much brighter than normal, with layer 4 become slightly more brighter. Layers 5 and 6 stayed unchanged.  I utilized the band selection to set up 1-3 and then changed the blue band between 5 and 6 to identify the location that the description was illustrating.  In this case the description identified shallow waterbodies that could be seen as a greenish color.

Lab06- Image Enhancement

GIS 4035

In this weeks lab we worked more with the ERDAS program as well as ArcMap.  With the government shutdown we were unable to get into the USGS website to download the Landsat 7 images, but our instructors were kind enough to provide the data to us.  With the data downloaded and extracted we began to perform spatial enhancements.  We utilized Fourier Transformation function as well as Basic Filters found in both ERDAS and ArcMap. 

I felt most of this lab dealt with Convolution Filtering found in the ERDAS program, and we were able to utilize different Kernel settings to see a multitude of outputs based off these settings.  

Finally I was tasked with modifying an image to generate an image that reduced the Scan line corrector failure lines so that they were less  noticeable, but the image still retained enough detail to let the viewer see the obscured image.  This image is shown in the map above.  As is the description of the steps taken to clear up the image as much as possible.

Lab 5a - Intro to ERDAS Imagine

 

GIS 4035

 

 

 

This week I worked with a new program called ERDAS Imagine 2011, as well as learned how to calculate wavelength, frequency, and energy of EMR (Electromagnetic radiation).  With the ERDAS Imagine program I learned how to navigate the basic tools, viewer, and how to add and remove data.  Ultimately we combined the data we had obtained and modified in ERDAS Imagine into ArcMap and the above map is an example of this capability.  In the above map, I show the user multiple classifications, as well as the total acres that the classification covers.

 

The lesson this week was a great opportunity to work with a new program that I have not used yet.  It allowed me to see how the program performed basic functions as well as allowed me to manipulate the attribute table associated with it.  



Lab 04 - Ground Truthing and Accuracy Assessment

 

GIS 4035

 

 

In this weeks lab, I continued with the Pascagoula, Mississippi Land Use/Land Cover map that I created.  The class was to focus in on Truthing, and verifying that the data we surmised last week was true and accurate.  Utilizing Google Street Views I was able to look at sample site locations and determine if the classifications that I determined last week were in fact accurate.  Overall for this sampling I was 70% accurate when comparing last weeks assessments to this weeks samplings of the areas. 

 

In the above map, you can see my sample locations.  Those locations that I found to be of an accurate assessment were colored green.  However, if I looked at a sample location and found that the area represented in the classification was false, I symbolized the dot in a red. 



Lab 3 - LU/LC Classification

GIS 4035

 

 

 

This weeks lab, we looked at Land Use and Land Cover.  The above map of the Pascagoula, Mississippi area was created utilizing an aerial photograph.  Based on that aerial photograph and utilizing the skills we learned in the past two lessons I was tasked with identifying different locations within the photograph and classifying them based on their looks.  I utilized size, color, shape, surrounding features to identify each location.  The Minimum Mapping Units (MMU) that I used on this map was approximately 2.5 hectares (roughly 6 acres).  If something was smaller than this standard it was typically lumped in with the other surrounding classification.

 

This was a time challenging map.  I utilized a lot of time trying to get as much detail as I could into the islands in the marshland area.  I could have cut corners, but in the end, I think it looks better and presents the data in a more professional manner.



Lab 2 - Visual Interpretation

GIS 4035

 

 

 

In this weeks lab and lecture we learned about identifying tone and texture in an aerial photograph.  Above you will see areas that are defined by their tone.  Tones are defined by the brightness or darkness an area displays on an aerial image.  This varies from a very light tone, all the way up to the very dark tones.  Also in the above aerial, I show different textures.  Texture is how rough or smooth a surface looks when viewing the aerial photo.  I have shown a range from very fine texture (water) all the way up to a very rough course texture (housing development).

 

 

 

 

The map above identifies features based on shape/size, shadows, patterns, or associations.  Shape and Size of features is a common way that analysts look at aerial photos and quickly determine what they are looking at.  Roadways, Buildings, Water bodies can all be identified in the photo by the way they look and the size they are relative to their surroundings.  Shadows help show the viewer what may not be clear with an initial viewing of the photograph.  Take for instance the water tower, sign and Utility Pole, all of which can easily be identified on the ground but from the air, may be more difficult.  Shadows help the analyst make a better determination of what is on the ground.  Pattern is also used by analysts to determine what they may be looking at.  Housing developments, Vegetation, and Parking Lots can be looked at and by a pattern show the user what they are looking at.  Finally, through association we can make a determination of what we are looking at.  The end of a major water body and the pier makes it easy to associate a beach to the coastline.  While a pool and building shape, with cars and a parking lot helps the analyst determine a hotel is present.

 





Module 11: Sharing Tools

GIS 4102

 

 

This weeks lab assignment worked with sharing custom tools to others.  The above screenshot is an example of a custom tool that was created to generate random points within a feature class no closer than a specified distance.  The points that were generated were then given buffers as defined by the tool.  All the results were then exported out to a specified destination folder. 

 

Overall, the custom tools section of this class has just been very rewarding to me.  I find that the ability to create custom tools and then share them to others is a huge benefit to me in my line of work.  I also like that passwords can be assigned to these tools that they can only be edited and exported by those that know the passkey.