Thermal & Multispectral Analysis - Barren Soil, Guayaquil, Ecuador

For this lab I conducted an analysis of a particular feature using Thermal & Multispectral tools. The primary goal was to use both ERDAS Imagine and ArcMap to adjust band combinations to highlight a feature of my choice. I created a composite image in ArcMap using a LANDSAT ETM image of Coastal Ecuador.  (Data Management Tools > Raster > Raster Processing > Composite Bands). At first I examined each band separately. For Band 6, the Thermal Band, the image remained mostly the same as in all the previous bands, only it appeared more “fuzzy.” The thermal return indicated that my feature was quite warm. This told me that it was a material (bare earth) that was emitting a good deal of heat. Knowing that I was viewing a feature very near the Equator, I surmised that this feature was emitting retained heat from the sun. I could also venture to say that this image was not taken at first daylight since the bare soil was already at least "warm".  I would guess the bare soil had absorbed at least some sun energy and therefore the time of day was at least past mid-morning. Additionally, I could not detect any significant shadows so this also told me the sun was probably at a high angle overhead. After viewing each band separately, I used various band combinations to further examine the image including:

False color – 4-3-2

Natural color – 3-2-1

Healthy vegetation – 4-5-1

Near IR, Mid IR, Red – 4-5-3

I finally decided to use the Band combination of 7-4-2 to display the final map analysis. This combination provides a "natural-like" appearance of many features. Healthy vegetation is bright green, grasslands appear green, pink areas represent barren soil, oranges and browns represent sparsely vegetated areas. Hence, the feature I selected in this map of Guayaquil, Ecuador is a patch of barren soil surrounded by healthy vegetation and water.

Statistical Analysis with ArcGIS - Meth Labs in Charleston, WV

This week was "Prepare" week for Statistical Analysis with ArcGIS. Below is my basemap of the two counties in West Virginia we will be examining using Statistical tools in ArcGIS. The map depicts Putnam and Kanawha counties and a simple distribution of Meth Lab Seizures, courtesy of the DEA and the National Clandestine Laboratory Register.

I took 2010 US Census data for this area and computed percentages for various categories, including, single female head of household  (pcnt_FCHLD), My hypothesis is that where there is a high (or elevated) percentage of single female head of household, there will be a corresponding high probability of a meth lab. I will test this hypothesis using ArcGIS Statistical Analysis tools. There are various other conditions that make it likely for a meth lab to exist, however, pcnt_FCHLD is the primary attribute I will use in my study. It is my goal that the results may be useful to DEA and local authorities whose objective is to seize Meth Labs and arrest anyone involved in the production of Methamphetamine.      

Multispectral Anaysis

This week in Photo Interpretation we continued our study of image enhancement with multispectral analysis. This was a great week where I learned how to use:

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. (This one was a blast!)
4) Use the Inquire Cursor to find the exact brightness value of a particular area (The Inquire tool was my favorite).

I worked through several tasks in ERDAS Imagine to accomplish the above.  The first Exercise was using Histograms in ERDAS. Manipulating Breakpoints was a challenge for me. Though I understand the principle, getting the correct image was difficult. Exercise 2, was Using Spectral Characteristics. My main take-away is:

a.      The band combination, Red: 4, Green: 3, Blue: 2 is called

      Near Infrared.

b.      The band combination Red: 5, Green: 4, Blue: 3 is called

      Short Wave Infrared.

c.       The band combination Red: 3, Green: 2, Blue: 1 is called

      True Color.

These band combinations help to highlight certain features and make it easier for us to find or identify important or otherwise, elusive features on a map. Exercise 3, Band Ratios - Creating Indices was very interesting to me. I created a Normalized Differential Vegetation Index (NDVI) that helps to distinguish clearcut areas. I could have used this tool for identification of MTR Mining sites in Special Projects!

The last Exercise, was to find 3 "Mystery" features matching pixel criteria as specified below.

The first feature was:  In Layer_4 there is a spike between pixel values of 12 and 18. Name the type of feature responsible for this and locate an example of it on the map. I used all four of the techniques above and with some trial and error I identified a large body of water as this feature:

I selected a True Color band combination to display this feature.

 The next "Mystery" feature was: A small spike in layers 1-4 around pixel value 200, and B) a large spike between pixel values 9 and 11 in Layer_5 and Layer_6. I selected a snow-covered mountain range as this "Mystery" feature.

I selected a Short Wave Infrared band combination to highlight the snow on these mountains.

The "Mystery" feature here is Mount Olympus in Washington State. The mix of dark, small, bare rocks on the mountain tops made this identification more challenging.

The third and final "Mystery" feature was to identify a certain type of water feature: Layers 1-3 become much brighter than normal; layer 4 becomes somewhat brighter, and layers 5-6  remain unchanged. 

Using the four techniques from above, finding this water feature was a difficult hunt. I thought the type of water might be near shore, so it would be a mix of rocks, shallow water and perhaps some turbidity. But the more I searched using the Inquire tool I became convinced the feature could be a stream or river. The Queets River, along its banks seemed to exhibit the above traits. So, this is the feature I selected.

  

I displayed this feature using a Near Infrared band combination, 4-3-2, The Queets River in Washington State, is located on the Olympic Peninsula, mostly within the Olympic National Park. This river empties into the Pacific Ocean. 

Mountaintop Removal:Report Week & MTR Story Map Journal

This week in Special Projects, we concluded our module on Mountaintop Removal (MTR) mining sites. We took a deep look at the data by processing Raster images of the Appalachian Coal Mining Region. To do this, we were split into several groups so that we could analyze Landsat images that would reveal MTR sites in this region. I was a member of Group 3 and I analyzed Landsat image  LT50190342010243EDC00. Specifically, I processed this image using ERDAS Imagine to re-classify areas that were MTR or Non-MTR and I used the Multipart to Singlepart tool to create several more polygons for analysis. After classifying the MTR sites using ERDAS Imagine, I conducted an accuracy check by generating 30 random points and comparing those points to a 2010 aerial image of the same region. My accuracy check yielded 28 out of 30 points that were True or correct which gave me a 93% accuracy. My results were a slight decrease in the total acreage of MTR mining sites. Though this is possible due to restoration, the decrease in amount of MTR acres does not provide remediation for the source of drinking water for the families who live in this region. There is a correlation between MTR mining and poor health, fish kill, lower bird hatchings, and poor water quality.

There are six stages to MTR mining: Clearing, Blasting, Digging, Dumping Waste, Processing, and Reclamation. During the blasting phase it is not unusual to cause violent shaking to families homes located miles away from the MTR site, nor is it unusual to find coal dust coated on the their homes. In my opinion, it is time to end MTR mining. As a nation, we have many other forms of energy available to us and we should make use of these alternate energy sources. The Coal Mining region located in the Appalachian Mountains would benefit immensely if we converted from MTR mining to wind, solar or other methods for producing electricity. Changing from MTR mining to solar energy, would be another great way to create jobs and provide America a lasting and renewable energy source.

Please see my Map Journal - MTR Mining: Streams and Basins

Lab 6: Spatial Enhancement

This week in Photo Interpretation we took a look at downloading data from USGS to obtain a Landsat Archive of Landsat 4-5 TM. The objective of this lab was to use various image enhancement tools and techniques to improve the quality of your image.

I used ERDAS Imagine to begin my spatial enhancement. The first technique I used was the Fourier Transformation. A Fourier Transformation is a mathematical technique for separating an image into its various spatial frequency components. I had a Landsat image that contained striping and the Fourier Transformation afforded me a method to remove that striping. The next tool I used was the Wedge button. This was a challenge to get the correct amount of coverage and not delete too much detail of the image. I also used the LowPass button that seemed to smooth the image a bit more. After completing these tasks in ERDAS Imagine, I switched to ArcMap to try a few more tools to enhance my image. I tried other filters and changing the brightness and contrast. In the end, I built pyramids for my images and this seemed to add a slight bit more contrast to the image. Below is my final map.