GIS 4035 - Module 8 - Thermal and Multispectral Imagery

For this week's Module 8 we were asked to analyze two composite satellite images at different RGB and landsat band setups in order to highlight specific features. The composite image that I chose was the EMTcomposite.img created in module 8 and the feature I chose to identify was the two fires located in the NE corner of the original EMTcomposite.img. I chose 4 data frames to show 4 different image configurations. These 4 data frames represent, in my opinion, the most effective ways to spot a forest fire via satellite imagery. The configuration of the 4 data frames is as following: RGB 123 - True color image for comparison, RGB 137 – fire appears brighter and more vivid, Landsat Band 1 – Grey Scale – shows brighter areas, Landsat Band 1 – Color Gradient – shows brighter areas as red. Landsat band 1 was chosen as it is the only band that effects the fire. This may be in part due to the fires intense brightness and its physical appearance being higher in elevation when compared to surrounding ground elevation.

GIS 4035 - Module 7 - Multispectral Analysis

This week, for module 7, we were tasked with analyzing satellite imagery for a mountain range in Washington state, tm_00.img. By analyzing histogram data one can extrapolate important information about the .img file.
The following map deliverable focuses on three features located in the tm_00.img.
Feature 1 criteria: 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.
Feature 2 criteria: Identify the feature that represents both A) 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.
Feature 3 criteria:  In certain areas of water, layers 1-3 to become much brighter than normal, 
layer 4 becomes somewhat brighter, and layers 5-6 to remain unchanged. Locate an example area that clearly shows these variations in water, using a band combination that makes them stand out.

The following map deliverable was created in arcMap highlighting all 3 features and showing their location in the original tm_00.img with colored extent indicators. The following RGB band combinations are listed as legends for each corresponding feature and an explanation for how I located the feature is found to the right of each corresponding feature.

GIS 4930 - Project 3: Prepare - Statistical Analysis of Methamphetamine Laboratory Busts

"Most illicit drugs present two serious problems for society. First are the many consequences of drug use for the user, the community, and society as a whole. Second is the violence that accompanies the business of drugs that are transported across national borders through elaborate networks. Methamphetamine contributes to both problems, but in many parts of the country it presents another serious problem, namely the social and environmental damage that comes from domestic methamphetamine production." Methamphetamine Laboratories: The Geography of Drug Production - Ralph A. Weisheit and L. Edward Wells

For this week’s portion of Project 3: Prepare, we were tasked with preparing for a Statistical Analysis of Methamphetamine Laboratory Busts in West Virginia, USA. The final report for this module will consist of a fully constructed multi paged scientific analysis of the West Virginia study area comparing specific census tract demographics to the location of reported meth lab clusters. This analysis will serve to solidify any doubt in regards to medical, property and economical damage, that this drug and the drugs creation processes has wrought on West Virginia over the last few decades. I look forward to performing this analysis and to next week’s ordinary least square regression model creation.

Below is a constructed basemap of the study area with all essential map elements for the module 3 project.

GIS 4035 - Module 6 - Spatial Enhancement

"The Landsat 7 sensor suffered a malfunction in 2003 called a Scan Line Corrector failure. Since then, Landsat 7 images have frequent lines of no data, giving the images a striped appearance." -Module 6 Background

For this weeks module we were tasked with enhancing a .img file of satellite imagery using Erdas Imagine. Utilizing Erdas one can edit an image in an assortment of ways and this module goes into depth into a variety of filters and tools that can be utilized in order to enhance an area to highlight certain features or in the case of l7_striping.img fix large gaps running diagonally through the image caused by satellite processing malfunction. The main tools used in order to correct for this malfunction were the Fourier Transform, Fourier Transform Editor, Spatial Convolution, and Focal Statistics. A preview of the Fourier Editor settings is included in the attached map and shows the technique I used in order to hide the gaps by applying wedge voids to the "star line" gaps. A Spatial Convolution of 3x3 kernel was applied in order to sharpen features in the imagery. The output of these tools was my deliverable final.img. I imported this .img into Arcmap, added essential map elements, and created the layout you see in the attached map.

GIS 4930 - Project 2 - Mountaintop Removal: Report

“There is always going to be ambiguity in image classification based on data quality, tools being used and judgments made by the interpreter. Perfection is impossible.” -Skytruth President, John Amos

Finally, after three weeks of data compiling, classifying, and assessing project 2: MTR is complete. For this weeks portion of project 2 I classified mountain top removal sites for path 17 row 34 in group 4's study area for the WV, Appalachian Coal Region Mountaintop Removal Project.
My results were as follows:
Accuracy: 90%
Total Acreage: 21243.2 square acres
Acreage difference between 2005 and 2015 MTR analysis : + 2908.8 square acres
Group Results:
Total Group Acreage: 137668.86 square acres
Total Group Accuracy: 85%

Attached is a screenshot of a map showing my completed layer package.

Link to MTR: Arcgis Online Analysis Map

Link to MTR: Story Map Journal

Link to MTR: 6 stage guide

GIS 4035 - Module 5b - Intro to Erdas and Digital Data 2

This week we continue module 5 with part b, and continue working with Erdas. For this weeks project we looked at a total of ten .img files and one .shp file in Erdas to analyze properties and characteristics that distinguish areas of the same imagery. The first step in any properties analyzation is to always first review the metadata, and that is precisely what I did. In reviewing metadata, that is unique in Erdas, one can view valuable data about an image that can let one determine the following but not limited to: spatial resolution, radiometric resolution, bit type, pixel size min/max, image dimensions, and projection info. For the final exercise of module 5b we were supplied an .img file soils_95 and a shapefile hydro_00. We were asked to create an area column and a percent column in the soils_95 attribute table in order to select for characteristics. The attached screenshot is a preview of a map in Erdas showing soil layers that contain humus and fine grain particles. This weeks lab concluded the introduction to Erdas and I'm sure that I will find it to be a valuable tool in future GIS projects.

GIS 4035 - Module 5a - Intro to Erdas Imagine and Digital Data

Module 5a Intro to Erdas Imagine and Digital Data had us start off by giving a tutorial into the program Erdas Imagine. This program is designed to process imagery and in this weeks lab I used Erdas to perform another ground truthing / accuracy assessment for a new area.

The region is located in the pacific northwest of the United States and is ~24 square miles in area.
Utilizing Erdas I was able to manipulate the color bands into a RGB 5,4,3 combination to present the following color scheme. I then imported the selected subset .img into ArcGIS pro to assemble the attached map. This ground truthing / accuracy assessment map highlights various features represented in the legend across the diverse landscape of the satellite imagery.