Thursday, October 19, 2017

Project 3: Statistical Analysis - Prepare Week

For Prepare Week of our Statistical Analysis lab, we prepared data for geostatistical analysis of methamphetamine labs in Charleston, West Virginia. We will use this data to create a basemap that depicts the density of meth labs per square mile in Charleston. 

After first examining the fields in the CensusTracts attributes table, we needed to add more data to this layer. The first few fields (Percent population growth, Percent white and Roommates) were added and calculated manually. The rest were added through a Python script that required some edits. Once these edits were made and script run the new fields were created. The next step was to create a field that represents the clandestine meth labs in the area. To accomplish this, we joined the meth lab point data with our census tracts using the Spatial Join tool. In the newly joined layer we then created a new field to calculate the meth lab density per square mile in each census tract.

Below is the basemap created from this data.



Lastly, for Prepare Week, we read a Methamphetamine Journal Article and completed the first two sections (Introduction and Background) or our scientific report.


Wednesday, October 18, 2017

Module 7: Multispectral Analysis

In this week's lab, we examined an image of the forest surrounding the Olympic Mountains in Washington State. We performed a few exercises in which we learned to use different bands of satellite imagery and learned how to calculate differences in spectral bands to create an index to further enhance certain features. 

In each map provided below, the four steps to identify feature in ERDAS were used:
  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.
  4. Use the Inquire Cursor to find the exact brightness value of a particular area.
The first map depicts Feature 1 in which we were to locate the feature responsible for creating a spike between pixel values 12 and 18 in Layer 4. The feature has been identified as the deep waters of the rivers in the image. I chose to display this feature in TM False Color IR.


The second map depicts Feature 2 in which we were to locate and identify the feature that represents both a small spike in layers 1-4 around pixel value 200 and a large spike between pixel values 9 and 11 in Layers 5 and 6. This image was displayed in TM True Color.


The third map depicts Feature 3 - variations in water. In these areas of water, layers 1-3 become much brighter than normal, layer 4 becomes somewhat brighter, and layers 5-6 remain unchanged. This image is displayed in TM False Natural Color.


Friday, October 13, 2017

MTR - Report Week

My analysis on Mountain Top Removal has come to an end in Report Week. For the final part of this project, I had to edit and package my reclassified raster I created in Analyze Week. To edit the raster, I first converting it to a polygon then removed noise and interference by creating a final polygon layer with areas no smaller than 40 acres, removing areas within 50 meters of roads and rivers, removing areas within 400 meters of major rivers and highways, and only including areas that intersect with mountain ridges. I then checked this polygon for accuracy by generating random points and checking each for correct classification. The polygon was then packaged and sent to my group leader for analysis and merging with other group layers.

I then created an MTR Analysis map with our final group polygon. I added a basemap, my stream shapefile I created in Prepare Week, and a US Coal Fields dataset provided in ArcGIS Online (credit to the National Atlas of the United States of America) which shows coal fields of Alaska and the conterminous United States and are symbolized by Max Rank of Coal. 

Screenshot of MTR Analysis map


For a more comprehensive look at my analysis into Mountain Top Removal, check out my MTR Map Journal.

Module 6 - Spatial Enhancement

This week we learned about how to modify remote sensing data using image enhancements. First we did some exercises in acquiring and formatting satellite data. We then used spatial enhancement techniques to modify images. Some of these techniques were using low pass and high pass filters and Fourier Transform in ERDAS and Focal Statistics in ArcMap. Our output for this lab was to take the provided Landsat 7 image and attempt to remove the "striping" effect that occurred from a malfunction in the sensor using the techniques we learned.

To achieve this, I took the original Landsat image and, in ERDAS, used the Fourier Transform tool to transform the .img file to a .fft file. I then opened the .fft file in the Fourier Transform Editor and used the Wedge and LowPass tools to reduce striping. I then used the 3x3 Sharpen filter four times in the Convolution Window to sharpen edges. Finally, I used the 3x3 Mean filter in the Focal Statistics tool in ArcMap. 




Monday, October 2, 2017

Module 5a - Intro to ERDAS Imagine

For this week's lab we learned about calculating wavelength, frequency, and energy of electromagnetic radiation (EMR) and were introduced to using ERDAS Imagine.

First we did used two equations, Maxwell's wave theory and Planck Relation, to calculate wavelength, frequency and energy. This gave us a better understanding of how the properties of EMR and how they relate to one another.

Next, we learned some basic tools of ERDAS Imagine including how to navigate around the viewer with two different types of satellite images.

Lastly, we created a map using the image we processed in ERDAS. (see map below)


Monday, September 25, 2017

Project 2: MTR - Analyze Week

For Analyze Week of our MTR project, we learned more about SkyTruth and how they use satellite imagery to document the impact of MTR on the Appalachian Region. We were each assigned a set of LandSat images, according to our group, that we were to analyze by conducting an unsupervised image classification in ERDAS. For this image classification we created 50 classes that we were to identify as either MTR or nonMTR and color code to each. The end product of this would be an image of our area with 2 class names and color types. We then reclassifed this image in ArcMap showing only the MTR areas. The screenshot below shows the final reclassification image.


Wednesday, September 20, 2017

Project 2: MTR - Prepare Week


For Prepare Week of our MTR Project, we created a basemap of the study area that covers part of the Appalachian Mountains. As part of Group 2, I used the DEMs provided for this group to create the study basin and streams. I then created a Story Map displaying the 6 stages of Mountain Top Removal: clearing, blasting, digging, dumping, processing, and reclamation. A placeholder for my Story Map Journal to be completed for Report Week, was also created.




Story Map
Story Journal Placeholder