GIS4035 Module 8: Thermal & Multispectral Analysis

Building on what was practiced in Module 7 Lab, additional experimentation was done with different band combinations and adjustments of breakpoints after composites were created from layers in both ERDAS Imagine and ArcMap. Thermal imagery allows the comparison of different features by the energy emitted. This is dependent not only on the material but also on season,  time of day, and moisture content. The following map was created after much experimentation with different band combinations, including some adjustments of breakpoints. The goal was to obtain an image and band combination in which a selected feature really stood out.

Thermal and Multispectral Analysis
Feature: Lake

GIS4930 Project 3 - Statistical Analysis with ArcGIS (Analyze)

The examination of various socio-economic factors in facilitating the ability to predict methamphetamine lab locations began with the Ordinary Least Squares (OLS) regression to help assure the creation of a good model. Initially all independent (explanatory) variables under consideration in this study were applied to the dependent variable (meth lab locations) as a group. The results were examined for redundancy, statistical significance, and a correlation between the independent and dependent variables. Following this critique of the results, the variable was evaluated for consideration of inclusion based on considered importance to the study. The goal of the OLS regression was a return of a high Adjusted R-Square value. Independent variables were removed individually in an effort to maximize the Adjusted R-Square value based on a critique of the results. This was followed by another run of the OLS tool. This process was reiterated numerous times until satisfactory results were achieved. A table of the final OLS results follows:

Final OLS Results Table

To inspect for bias, the OLS regression's Jarque-Bera Statistic Score was checked along with the histograms of a scatter plot matrix. Finally, the Standard Residual values for each census tract were added to the map for visual critique. Most of the two-county area was predicted accurately with this model as shown in the map below. Additional work would include resolving the high and low residuals.

Meth Lab Locations and Standard Residual Values
Charleston, West Virginia, with Kanawha and Putnam Counties

GIS4035 Module 7: Multispectral Analysis

Map 2: Snow identified with band

combination Red: 5, Green: 4, Blue: 3

Map 1: Water feature identified with band

combination Red: 4, Green: 3, Blue: 2

Map 3: Lake identified with band
combination Red: 7, Green: 5, Blue: 3

This lab explored different ways to identify features using ERDAS. After reviewing histograms to identify patterns and shapes in the data, the imagery was viewed in grayscale to look for dark and light shapes as well as patterns. EMR bands were manipulated to emphasize different features in the imagery. Trying to find the right band combination to help make a feature noticeable was a challenge, but the band combination references posted on the discussion board were helpful. Exact pixel values of specific areas were obtained by using the Inquire Cursor. Map 1 shows the use of TM False Color IR to highlight bodies of water. Map 2 utilizes a Short Wave Infrared Color Composite of 5-4-3 to illustrate snow. The final map, Map 3, uses a band combination of 7-5-3 to make certain water features, such as this lake, stand out.

GIS4930 Project 3 - Statistical Analysis with ArcGIS (Prep)

Statistical Analysis is the focus of the third project in Special Topics. This particular study will analyze a variety of socio-economic factors to determine whether there is a connection to the location of illicit methamphetamine production labs in Kanawha and Putnam Counties, West Virginia. With 2013 being a record-setting year for meth lab busts in West Virginia (Eyre), law enforcement officials will appreciate having additional information that may help locate existing meth labs or prevent additional ones from starting business.

Preparation for the statistical analysis included creating new attribute fields for the census data and then using the field calculator to determine percentages of some data. Utilizing percentages rather than raw numbers for the census tracts will provide more equalized comparison of data. Python scripting was also used to add additional attribute fields and perform related calculations in a more efficient manner. Drug Enforcement Administration (DEA) meth lab bust information with geocoded data was joined to the Census data as well. Tidying up the attribute table was done by turning off fields which wouldn't be used for the statistical analysis. The following basemap of the study area was created. It includes cities and towns which have had meth lab busts in the past.

Socio-Economic Factors and Meth Lab Locations

Study Area: Kanawha and Putnam Counties, WV

Resource:
Eyre, E. (2014, February 19). W.Va. Senate OKs anti-meth prescription bill. Charleston Gazette. Retrieved from http://wvpress.org/news/w-va-senate-oks-anti-meth-prescription-bill/

GIS4035 Module 6 - Spatial Enhancement

Spatial enhancement of satellite imagery was the focus of Module 6 Lab. The enhancement of remotely sensed imagery enables viewers to extract more information from the imagery than originally expected. Enhancements can be used to emphasize particular aspects or features in the imagery or to correct errors resulting from sensor issues. ArcMap and ERDAS complement each other in the application of spatial enhancements.

After practicing the application of various enhancements to other imagery, the final exercise in this lab involved applying image enhancements to a Landsat 7 image. The purpose of the enhancements was to improve the image quality by reducing the visual impact of the "striping" effect of a sensor malfunction. The specific goal was to minimize the striping while maintaining the highest degree of detail as possible. Enhancements that were applied to the imagery to complete this map included Fourier Transform, 3x3 Sharpen Filter, Haze Reduction, Noise Reduction, Contrast Adjustment, and Brightness Adjustment.

Landsat 7 Imagery after the application of several spatial enhancements.

This exercise also served as a reminder of how finicky software can be. For undetermined reasons, both ArcMap and ERDAS required multiple attempts at completing some processes either because they resulted in completely unanticipated results or because they would not run at all. In one case, closing completely out of eDesktop and attempting the process again after re-starting resolved the issue. Other processes were unsuccessful primarily because of lack of experience in how to use them optimally. This was especially true with histogram adjustment. Additional practice will be beneficial in learning the ins and outs of spatial enhancement.

GIS4930 Module 2 - Mountain Top Removal, Appalachia Coal Region (Report)

The  Mountain Top Removal (MTR) method of coal mining in the Appalachian Mountains has been shown to impact the surrounding hydrology (Petrequin). As part of the study of applying GIS to MTR impacts, this project included creating stream and basin features. This was done by mosaicking four DEMs into one layer and then applying several Spatial Analyst Hydrology tools.

Analyzing the impact of the MTR method of coal mining on an area involved comparing imagery from two different time periods. ArcMap and ERDAS Imagine were both used with 2005 imagery to develop a signature file which was then applied to 2010 imagery to develop a map of MTR areas.

Only a portion of the 2010 data was analyzed by Group 3 for SkyTruth. The 7 bands of the 2010 Landsat imagery were consolidated using the Composite Bands tool, and the imagery was clipped to the group's study area in ArcMap. A layer with 50 classes was created in ERDAS Imagine using the Unsupervised Classification tool. Areas which appeared to be part of MTR were classed and symbolized by color accordingly with the remaining areas being classed as NonMTR. Many objects such as stream or river banks, buildings, and roads have identical spectral signatures as MTR and were included initially. They were removed from the MTR features later. In ArcMap, the classified image was reclassified with MTR being assigned a value of 1 and all other Class Names assigned blank values.

From this information, the MTR raster was converted to polygons. MTR features within 400 meters of major rivers or highways were removed from the MTR polygon layer, as were those within 50 meters of streets and other rivers. Features smaller than 40 acres were removed as well. An accuracy assessment was done with a result of 96.7%, and a comparison against the 2005 dataset was made. There was an overall decrease in acreage attributed to MTR from 2005 to 2010, but the data needs to be critiqued further. The 2005 dataset included features of less than 40 acres, while the 2010 data was restricted to features containing more than 40 acres. The 2005 dataset had more than 8,000 features, while the 2010 dataset had fewer than 500 due to the acreage restriction.

A layer package was created with this data and submitted to the group leader for compilation into one dataset for the group's study area, and a map service to present the group findings online was created. This map service was used to create an online map. A soils runoff classification layer was also added to the map. This additional data was selected because runoff from MTR sites impacts the surrounding areas. The online map can be viewed here, although the soils layer is not available to all viewers.

Resource:

Petrequin, M. (2012). Hydrological Impacts of Mountaintop Removal in Appalachia: History and Solutions (Colorado School of Mines, Department of Environmental Science and Engineering).  Retrieved from uwf.edu.