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.

Mountain Top Removal: Analyze Week

This week is a continuation of Mountain Top Removal. The main objective for this week was to Reclassify an MTR raster. To accomplish this, we used a Landsat image and manipulated it using ERDAS Imagine. I also learned about SkyTruth, a non-profit organization that provides  remote sensing and digital mapping technology using satellite images and other visual technologies, to provide information and data to environmental advocates, the media, and the public.  

My task was to examine a digital multispectral image that represents a portion of our study area. Additionally, this week we signed up for small groups. I am in Group 3 and we have two Landsat images to work with:

LT50190332010243EDC00.tar

LT50190342010243EDC00.tar

Using ArcMap I was able to use the Training Sample Manager to sample various polygons from the image and classify them as MTR or NonMTR. This could be a tedious task but I found it mildly entertaining.  I created the following histograms and scatter plots to check the accuracy of my sampling:

 

Looks pretty good to me. After this, I used ERDAS Imagine to create an AOI layer and then use the "Grow tool" to select pixels that were in an MTR area. Conducting this analysis took time for many of the tools to process the data. Once again, this was a fun exercise even though I had to wait patiently between each step of processing data to arrive at my final product that I then exported back to ArcMap to complete.  Below is my final re-classified MTR image 

ERDAS Imagine - Remote Sensing & EMR

This week I learned about ERDAS Imagine.  This is a remote sensing application for GIS. The ERDAS Imagine program allows us to manipulate raster images to extract information. In this lab we were using a raster of Washington State to classify the types of land cover such as vegetation, water, bare earth, etc. To do this, initially we began with some calculations of electromagnetic radiation (EMR). The chart below shows the electromagnetic spectrum:

There were a few formulas involved such as Maxwell’s wave theory: C = λν and Planck Relation: Q = hν 

But the real point of this lab was to learn about ERDAS Imagine and how to process a raster image to understand the types of land cover depicted. A very useful feature was the ability to switch between TM False Natural Color and TM False Color IR. The False Natural Color gives the bare ground a pink color which adds a sharp contrast making it easier to discern from the green forested areas.

Overall this was a fun Lab but I am sure I will need more practice with ERDAS Imagine to feel as comfortable with it as I do with ArcMap. Below is the map I created for this week's Lab. It shows an area in Washington State that I have re-classified by using the Inquiry Box and manipulating of the ERDAS Attribute Table.

  

Mountain Top Removal and Map Stories

This week I worked on a map concerning Mountain Top Removal for coal mining in the Appalachian mountain region. The EPA defines Mountain Top Removal as:

“Mountaintop removal/valley fill is a mining practice where the tops of mountains are removed, exposing the seams of coal. Mountaintop removal can involve removing 500 feet or more of the summit to get at buried seams of coal. The earth from the mountaintop is then dumped in the neighboring valleys.” (ilovemountains).

I used several tools to make this map. The bulk of the work was in creating the Hydrology Dataset from the Mosaic Group Raster Data. There were plenty of steps involved including:

- The Fill tool — This step modifies the raster to prevent water flow from pooling.
- The Flow Direction tool — This process assigns each pixel a value representing the direction of water flow across each cell.
- The Flow Accumulation tool — For each cell in the raster, this tool calculates the total number of other cells that flow into it.
- Calculate 1% of the pixels in the image —  A generally accepted condition is that the Flow Accumulation value must exceed 1% of the total number of pixels in the raster. If more than 1% of the total pixels in the raster flow into an area, it is probably a stream.
- The Con tool — This tool defines the threshold of flow accumulation values that qualify something as a stream
- Stream to Feature — This tool creates polyline vector features from the Con tool output raster

This was a lot of work but I am glad I used all the above tools for the exposure and practice. Below is my basemap.

Another skill I worked on this week was to create a Story Map and a Story Map Journal. The subject for both stories concerned the 6 Stages of Mountain Top Removal:

- Stage 1: Clearing 

- Stage 2: Blasting

- Stage 3: Digging 

- Stage 4: Dumping waste 

- Stage 5: Processing

- Stage 6: Reclamation 

You can find My Story Map at: Mountain Top Removal

I also created a Story Map Journal, but this is still a work in progress. If you would like to take a look at it now, go here: Mountain Top Removal: Streams and Basins

Ground Truth: Error Matrix & Overall Accuracy

This week I modified my map from last week, Land Use Land Classification. The challenge last week was to not use any tool that would help you verify the type of land I was classifying. I could only use the aerial image (.tif) of Pascagoula, MS. and make my best guess at what type of land I was classifying. This week, the challenge was to take an unbiased sampling of the land I classified and compute the overall accuracy. Under normal circumstances, you might travel to the location and conduct fieldwork to determine the correct land use/classification by conducting a Random, Stratified Random, or Systematic Sampling. Since I could not actually travel to Pascagoula, the tool of choice was Google Maps - Street View. This is an excellent proxy for actually being there.

Another tool I created and used was an Error Matrix. I conducted a virtual Random Sampling of my map starting at the North West border and working my way South to cover the entire study area and I marked as "True" or "False" the areas I had previously classified. I used Google Maps to confirm what I classified was correct - or not. I found that I miss-identified a few land types such as:

- A Commercial / Industrial area (Category 15, USGS Andersonian framework for national land use and land cover) - What looked like an Industrial area to me turned out to be a shopping center.
- A Residential area (category 11, USGS Andersonian framwork) - I correctly identified most of the residential areas, but one area I classified as residential turned out to be a cemetery!

My Overall Accuracy was 76%. Here's the map I created.