Special Topics, Week 10: Web Applications - Report Week - Final Story Map

This week we had to add one more customization to our story map and fine tune the map as a whole.  For my customization, I decided to add a zoom level to each point feature.  Initially, you're zoomed out, able to see from Chicago all the way to Hawaii.  As you go through the slide show, you're zoomed into that city.  Most of the time you're able to see all points located in the city at once.  I felt like this zoom level was optimal because it gives viewers some choice about the zoom level.  I also decided to change my map summary and edit a few of the site descriptions.  Here is the final link to my story map featuring my move to Hawaii!

Remote Sensing & Photo Interpretation, Mod9: Unsupervised Classification

This week we used both ArcMap and ERDAS to perform an unsupervised reclassification of a provided image.  The image we used for our final map, was of the UWF campus.  The ultimate outcome of this map was to determine what percent of surfaces is permeable and what percent is impermeable.  We originally reclassified to 50 different classes and then worked with a specific area in the image to break it down to 5 categories: Trees, Grass, Buildings/Roads, Shadows and Mixed.  After the classification was complete, the final map product was produced in ArcMap.

Unsupervised Classification of the UWF campus.  Area shown in acres with the estimate that about 58% is
permeable while the remaining 42% is impermeable.

Special Topics, Week 9: Web Applications - Analyze Week - Online Story Map

This week we put our story board plan into action.  Initially, we needed to find images for each of our locations (a full size image and thumbnail for each).  We then needed to edit the CSV template to contain our information, lat and long coordinates, URL links to images and thumbnails, a title and description.

From here, we uploaded the CSV into arcgis.com into a web map.  This automatically placed pin points at each of our locations.  We chose a base map that was appropriate and added an additional layer that pertained to the theme and feel of our story board.

Finally, we needed to edit scripts in the index html and the map configuration.  We needed to make sure the code knew which map we wanted to work with and where it could be found.  The last item was to add the UWF logo to the top right of the map.

I ran into a few issues that were pretty frustrating, but the major roadblock turned out to be an extra "s" in the hyperlink.  With endless troubleshooting and help from the professor, TA and classmates I finally have a rough draft to present.  Here it is.

Remote Sensing & Photo Interpretation, Mod8: Thermal & Multispectral Analysis

For this lab, I utilized both ERDAS and ArcMap to combine several layers of imagery into one single multispectral layer.  In order to get my specific feature, the city of Guayaquil, to stand apart from the rest of the image I needed to find the right band combination.  By working in ERDAS and ArcMap, I was able to fool around a bit with the bands and histograms until I found a combination that I liked.  This combination then needed to bring out the urban areas of the city and provide an obvious distinction between urban and vegetation, agricultural and even the river. 

I think my final combination of R: 3, G: 1, B: 6 and adjusted breakpoints really creates an image that allows you to see the urbanized areas of Guayaquil, Ecuador.  These areas appear as a bright yellow-ish green color which contrasts nicely with the dark greens, blues and pinks of the other parts of the land.  This also allows the river to be a lime color with a smooth and constant texture.  

Urban areas of Guayaquil, Ecuador using the band combination of R: 3, G: 1, B: 6.

Special Topics, Week 8: GIS Web Applications - Prepare Week - Storyboard Development

This week we started a new module and we're focusing on GIS web applications.  We're working on story maps.  Story maps are interactive maps that are generally less technical and more descriptive.  They are designed to tell a story but they also include pictures or images, descriptive texts, attached documents and even helpful hyperlinks.  For prepare week, I put together a storyboard for what I want my story map to show and how I want my story told.  The basis of my map is going to be my move from Wisconsin to Hawaii.  I'll be flying out of Chicago, have an extended layover in San Diego to see some sites and friends and I'll land in Honolulu.  I'll see some classic Honolulu sites before my final flight to the island of Maui.  I want my map to show my trip and the sites that I am going to visit along my way.

I plan on using various images and configured pop-ups to convey information about the individual sites.  Below is an example of one of the images I might use for my stop in Honolulu.

Diamond Head Crater State Park, Honolulu, HI

Remote Sensing & Photo Interpretation, Mod7: Multispectral Analysis

This week we worked with multispectral analysis.  We used various techniques to locate and identify three different features based on their histograms of each layer.  Once the features were identified, we changed the bands in order to make that one feature more prominent.  As you can see in the first map, the feature was deep water.  I decided that bands - R: Layer_5, G: Layer_2 and B: Layer_3 worked best to emphasize the deep water.  The dark blue/black lakes really stand out against the red land.

Deep water is the prominent feature in this map - clearly standing out as the black/dark areas.

The second feature we needed to identify based on histograms and an Inquire Cursor was snow-capped mountains.  To be sure that the mountains that are snow-capped really stood out and became the prominent feature in the map, I worked with the bands to create the following image.  The snow, keeping white, really pops against the green/brown landscape.  I went with: R: Layer_3, G: Layer_2 and B: Layer_1.

The snow-capped mountains are the prominent feature in this map.  Keeping the snow white and mountains green really gives a good perspective. 

The third and final feature that was identified was shallow water.  As you can see, the show water is displayed as a light and bright blue.  It distinctly stands out against the dark, almost black deep water and the red land.

The shallow water in this map stands out as it is a much brighter blue compared the the dark, almost black deeper water and red land.

This lab seemed to be pretty unclear.  There was a lot of trial and error and communication between classmates in order to figure things out.  I felt that maybe more detail or insight needed to be included in this lab.

Remote Sensing & Photo Interpretation, Mod6: Spatial Enhancement

This week we mainly worked with ERDAS and learned about spatial image enhancement.  We had a few exercises (one that had to be cancelled due to the wonderful government shutdown) that taught us about the various tools in ERDAS to create image enhancement.

We were provided with an image that had significant striping and image distortion.  Working with the Convolution filter, we used different kernel settings to manipulate the image.  Another tool that was utilized as the Fourier Transformation function.  This function greatly reduced the distracting striping in the image.  Playing around with the different kernel settings and the Fourier transformation editor, a final image was created that has limited striping, yet still maintains a certain amount of image clarity and detail.  

Spatial image enhancement after various kernel setting adjustments in the Convolution filter and a run through the Wedge tool.