Programming: Module 2

The lab for module 2 introduced object types, performing math functions, use condition statements, and use methods.  Through the exercise, it was clear to see different types of objects are able to perform certain actions.  It was easy to see how math functions and condition statements in scripts.

Module 2 exercise was to use my full name and assign variables to get certain results. I named my name and split my first and last name.  I assigned the split name a new variable.  To print just my last name, I used listName[1].  Using the function len, with the split on my last name counted how many letters are in my last name.  After assigning the count of letters a variable, I used a math function to calculate 3 multiplied by the number of letters in my last name.  I then printed the last variable.

This screenshot shows the results of the script.  Each result is built on top of the other.  My full name is split, my last name is printed, then the number of letters in my last name, and finally number of letters times 3.

Script Results

Lab 1: Applications in GIS

Lab 1 introduced suitability modeling with raster and vector analysis tools.  After using the tools, I compared the different approaches used.  Another key part of the lab was to adjust parameters to see how it can affect the output.

To create this map, I reclassified the land cover shapefile.  I also reclassified the soil shapefile.  Next, I created a slope raster with the elevation layer with a suitability rating of 1-5.  I used Euclidean distance to create a 1000 distance around the streams and also used the Euclidean distance tool to create a distance to roads raster.  This was broken into 5 classes using the 1-5 suitability rating.  After the suitability rating was applied to all the layers, I used the weighted overlay tool twice.  The first time, I used equal intervals.  The second time, I used different variables for soil, slope, streams, and roads.

It is clear to see how added more weight to a criteria can influence suitable area.  To see how much area was the most suitable, I change the layer to polygons and calculated the geometry.

Suitability Modeling

Programming: Module 1

This week's lab introduced how to use Python IDLE and PythonWin.  It focused on looking at the syntax, and how to write pseudocode code.  Another part of the lab was to practice running scripts.

The script I was created 12 module folders.  Within each folder, 3 sub folders were created: data, results, and scripts.  After running the script, it is clear how much more efficient.

Used Script to Create Folders

To run the script to create all the folders, I found the saved script.  I had to right click and select edit in PythonWin.  PyhtonWin is an IDE that allows for me to run a script in a window and also save thet script in another.  The comments before each section helped explain what the script would do.  I select the "Run" button to run the script and all the folders were created under GIS Programming.  I am interested to learn to build other queries like this to help me in school and work.

Week 10: Supervised Classification

Week 10 objectives are to create spectral signatures and AOI features in ERDAS.  From there, create a newly classified image from a satellite image.  Once the image is recoded, identify and resolve confusion between the signatures.

Create the map below, I started with an image of Germantown, Maryland.  I opened the signature editor and added a new AOI layer.  I used the polygon tool to select features such as water and roads.  I used the inquire tool and coordinates to find urban areas, agriculture, and more.  If the feature seemed to contain a lot of differing pixels, I used the growing properties to select the feature I used the inquire tool for and adjusted the spectral Euclidean Distance. 

Once all my signatures were created, I looked at the different layers to see where confusion existed and to identify the best bands to display.  I used bands R-4, G-5, and B-6.  The final step was to recode all of the signatures.  I was able to calculate the areas of each class within ERDAS.  I was not 100% satisfied I was not able to eliminate all pixel confusion.  I attempted to adjust my signatures and change bands, but did not seem to find success.  I look forward to applying these techniques in through a potential job or practice to improve my skills.

Supervised Classification of Germantown, Maryland using bands R-4, G-5, and B-6

Module 9: Unsupervised Classification

Unsupervised Classification

The goal of the lab was to perform an unsupervised classification using ArcMap and ERDAS.  Another aspect of the lab was to classify images with different spatial and spectral resolution.  The final goal was to learn how to reclassify and recode images in ERDAS.

The lab started off with using the Iso Cluster tool and Maximum Likelihood Classification tool.  Doing this, it created a classified image which I assigned classes colors.

In ERDAS, I used the Unsupervised Classification tool.  This allowed for me to give the image 50 classes.  Once the image had the classes, I reclassified by opening the attribute tables to change the colors of the pixels that belong to each feature.  I did this throughout using the Swipe tool to help identify from the true image.  It was also helpful to change the pixel group to a red or yellow to see how much it is used in the image.  From there, I continued to classify the pixels.

Once the pixels were classified, I used the Merge tool to group the 5 classifications into 5 classes.  I looked at the classes and assigned them groups to bring the classes from 50 to 5. I added the area of the features to help determine how much is impermeable and permeable classes.

Lab 8: Thermal Infrared Images

The objectives of lab 8 is to interpret thermal infrared images using the Stefan Boltzmann constant and create thermal images by adjusting symbology and band combinations. The images that were used were from Ecuador and Pensacola.   

Once I went through the exercises, the last assignment was to identify features applying what I learned and enhance the image.  From the exercises in lab, I noticed there was a visible difference between farm land and natural vegetation when I adjusted the bands to R-6, G-4. B-7.  However, that difference was not as noticeable when I looked at the image with layer 6.  The two different feature types look very similar.  Band 6 shows how the vegetation emits less energy than the urban areas.

Thermal and Multispectral Image of Ecuador

Lab 7: Multispectral Image

The objectives of lab 7 were to look at the histograms of an image and use tools to look at data.  By looking at the histogram, an objective was to interpret the data. Within ERDAS, also use the Inquire Cursor tool to look at pixel data and find tools from the Help Tab.  After going through all the exercises, applied skills to identify features.

By looking at the metadata histograms, I was able to see if the features I was looking for were dark or light.  The size of the spike also told me if the feature was large or small.  Once I had an idea of the brightness and size, I used the inquire cursor to see pixel count.  I was then able to see the pixel count for each layer and see if it matched the clues.  I then adjusted the color to spectrum to help highlight the features I needed to find.

Deep Water - TM False Natural Color

Snow - True Color

Shallow Water - Near Infrared Color