Lab 9 Unsupervised classification using ERDAS - UWF high resolution .sid image using only visible bands of light (RGB)

This lab provided an exercise in both ArcGIS and ERDAS Imagine to perform an unsupervised classification.  The deliverable component came from the ERDAS Exercise 2 portion of the lab.  We utilized the Unsupervised tool within the Raster tab Classification group and set the nuber of classes to 50, accepted the approximate True Color for the color scheme and assigned Red as 3, Green as 2 and Blue as 1; we also used a maximum iteration of 25 and a convergence threshold of 0.950 and set the skip factors to 2 for X and Y.  Our next task was to reclassify the 50 classifications we just created via the attribute table.  We selected known items in the image which were the highlighted in the table; we then set the Class_Name field to one of our 5 categories: trees, buildings/road, grass, shadow and mixed; we also changed the color of our new class to something appropriate.  We repeated this process until we were left with items which were difficult to discern and placed them in the mixed category.  We then turned on the original .sid image and used a combination of the Swipe, Flicker, Blend and Highlight tools by first selecting an unclassified item in the attribute table, changing it's color to something distinct and bright and using the tools to aid in identifying the item at which point we could properly reclass it.  This process was repeated until all 50 classes had been reclassified and assigned the required color.  At this point we used the Raster Tab, Thematic button Recode to merge our 50 classes into 5 for our final product.  This new recoded  image was then saved.  The image was imported into my ArcMap geodatabase and a final map created.  We also added a new column for Area to the attribute table.  These were then summed and used to develop permeable and impermeable acreages and subsequently percentages.  This information was included in the final map.

Lab 8 - Thermal and Multispectral Analysis using ERDAS and ArcMap

      After reviewing the use of histograms and learning to combine multiple images in this case obtained using LANDSAT ETM+, we practiced converting the multiple images into a composite using the Composite Bands tool in ArcMap of the Layer Stack tool in ERDAS.  We also reviewed items from past lectures and applied them during our analysis phase.  This included the use, in ERDAS, of multiple views, histogram editing, and using the Discrete DRA tool.  We used symbology in ArcMap and altering color band layer associations in ERDAS during our Multispectral analysis.

      I noticed an area in the Midwestern portion of the image which caught my eye due to both its shape and coloration.  By using the ETM Composite image I created from the ETM layers 1-8, I had the basic image ready to adjust.  Again, I prefer to work in a geodatabase so I imported each layer into the ETM.gdb and stored the ETMComposite there as well.  While running various comparisons made different areas of the image stand out, using the stretch symbology and reviewing each layer, visually I prefer the multispectral imagery.  I set Red to layer 4, Green to layer 2 and Blue layer 6 (the thermal layer).    As I panned around the image I noted a feature south of Guayaquil which was within an urban area, oval in shape with a bright green outline and dark red center.  This became my AOI.

I noted that by using the Stretched symbology the object was most visible in layers 1-3.  Band 6 did not illustrate central red blurs fading to yellows and then surround blues.  I knew the red blurs were potential hot spots, assumed the fading to yellow was a blend of greens and blues and that the outer more distant blues were urban areas.  By switching to the RGB composite symbology as described in the first paragraph, the shape and heat signature were much more clear.  I suspected that this could be some sort of civic area or sports arena.  Knowing the incredible following of Fútbol (soccer) I began to lean toward this area being a stadium.  The coordinate value obtained from the information icon in ArcMap  (Coordinate System WGS 1984 UTM Zone 17N, Projection Transverse Mercator, Linear Unit Meter, Angular Unit Degree, Datum WGS 1984)  is 79°55'29.684” W, 2°9.956"S.  I noted this on the map.  Wanting to confirm my theory, I entered those coordinates into Google earth and discovered that the location was in fact a sports arena known as Estadio Monumental Isidro Romero Carbo AKA Estadio Banco Pinchincha which is in the parish of Tarqui in Northern Guayaquil, Ecuador.

Project 3 Prepare Week using Statistical Analysis within ArcGIS to analyze existing meth labs and hopefully reveal potential locations for new methlabs

This week our focus was on importing the provided files to then perform various statistical analyses to produce a quality base map to be the basis of our future weeks.  We created and calculated attribute fields both manually and using a python script which required minor editing to function.  We used the spatial join function to join shape files. We obtained base map data such as roads, county boundaries, major cities and a state boundary to give our map a complete feel and a focus on our AOI.  This map also included meth lab locations.  We also performed readings of scholarly articles to use for the beginnings of what will be our final statistical report at the end of this project.  This is an interesting real-world project.  The intent is to use social and economic statistics paired with the location of existing meth lab seizures to develop potential methods to find a pattern in where meth labs arise.  This will also allow analysis of environmental impact by adding additional data for comparison.  Thus creating an environmental tool and a law enforcement tool.  Pretty cool stuff.

Module 7 - Performing Multispectral analysis and using the NDVI (index) to enhance specific image features

Both the exercises and the final lab were pretty interesting as they gave us insights in how to utilize the multispectral tab, NDVI creation and the use of the panchromatic tab as well as using histograms and adjusting the breakpoints and LUT (visible brightness on screen) Histogram; also worth mentioning is the Discrete Data button which will automatically adjust breakpoints to create a balanced histogram which produces good results for most but not all situations.  We also experimented using a few the tools found in ArcMap, but after a bit of fiddling with ERDAS, I find that I preferred it over ArcMap.  For our lab we were given three distinct features to locate by using the inquiry button and observing pixel values.  Once each feature was located we applied what we learned in the exercises and then created a subset & chip of the area after selecting the optimum band combination for each map.  Although some maps could have used the same band combination we were asked to use three distinct band combinations, one for each feature.  Below are the maps I produce in Arcmap based upon the multispectral analysis performed in ERDAS.

Module 6 - Spatial Enhancement

I found this lab to be quite difficult regarding the final portion.  The exercises were easy to follow and made sense.  Using them in both ERDAS and ArcMap for cleaning up stripting in a Landsat image proved much more difficult.  Despite the lecture and readings, I still felt far out of my league.  I did additional research online, but failed to grasp the high-level concepts.  I did my best and here is my result:

Mountain Top Removal - Report Week

This week concludes our study of mountain top removal in the Appalachian Coal Regions of Kentucky, Ohio, Tennessee, Virginia and West Virginia.  I was part of group three and our focus areas were portions of Kentucky and Ohio.  The map below was produced in ArcMap for the Analysis and reporting phase of this project.  That information was then used to create  a Group MTR Anlaysis Map and a final MTR Story Map Journal.

The link to the Journal is:  http://arcg.is/2dLxhfn