GIS 4930: Special Topics; Project 2: MTR Analyze

This week we were tasked with classifying areas of Mountaintop Removal (MTR) and NonMTR on four landsat images for our group's study site.  There are four groups to sign up for in which I decided to become a part of Group 1. The fact that there are four landsat images and four members in my group, we all took responsibility for classifying one image.  

The first step was to create a single raster dataset of seven landsat bands that pertained to my image with the Composite Bands tool in ArcMap.  Next, the Extract by Mask tool was used to create a raster of portion of the composite landsat that fell within the study area.  At this point, the image was ready to be classified into two categories: MTR and NonMTR.  This step was accomplished by using a new software known as ERDAS.  Within ERDAS, the Unsupervised Classification tool was selected, creating 50 classes from the masked image.  Following this, the MTR and NonMTR pixels were selected and titled as such in the attribute table.  I then saved the layer and added it into ArcMap.  The raster was then reclassified with all areas other than the MTR areas as No Data. This was then converted into a polygon.  My results from this assignment are shown below. 

River banks, roads, and flat mountainous regions have similar spectral reference characteristics as MTR sites, so a portion of the red area is not accurate. This will be corrected next week during the Report Week assignment. 

GIS 4035: Remote Sensing; Module 5a: Intro to ERDAS

This week we were given the opportunity to explore a new software known as, ERDAS Imagine.  In doing so, we learned some of the basic tools within the software.  Those tools include how to use and navigate around the Viewer with two different types of satellite images. We also learned how to add a new column in the image attribute table.  On top of that, we learned how to create a subset from the image provided, which is shown below.  We were then able to create a map in ArcGIS using our subset image, which was created in ERDAS Imagine.  In the map shown below, you can see the various classes and their area (as shown in meters).  

I really enjoyed this assignment because I was able to explore the capabilities of a new software.  I succeed at learning new softwares, and all of their tools and functions.  Being a quick learner, I have the ability to learn new softwares within 1-2 days.  I enjoy exploring new softwares not only because it's interesting, but it also makes me more valuable. 

GIS 4930: Special Topics; Project 2 - MTR Prep

The next few weeks will be spent looking at Special Applications in GIS that can be used to analyze mountain top removal (MTR). MTR and valley filling are a common practice most particularly dealing with coal mining. The Appalachian Mountain chain in the mis-eastern United States is an area that is particularly affected with this form of mining.  The mining essentially involves peeling away the surface of the earth including trees, brush, soil to get at the rocky layer beneath to harvest away the precious coal. This is the premise of the project throughout the next few weeks. The first part of week's assignment was to create a basemap for the study area that I will be exploring during these next few weeks. The project as both an individual and group component.  Deliverables, like the basemap shown below, still have to be done independently. However, much of the upcoming analysis will be broken down into manageable chunks to be completed in groups resulting in a final group presentation. 
The basemap below provides an overview of the study area, and displays the DEM, streams, and basin for Group 1, which is the group I have chosen to work with.  Many things have been done to the original DEM layer to show the elevation, streams, and basins as shown below.  Essentially, a mosaic raster was made out of 4 DEM sections, which was then clipped to the study area.  From there, multiple tools were applied to the mosaic to generate the streams and basins. Using the Fill tool, I was able to fill the holes in the pixel database.  This makes it so when running a subsequent flow analysis, there aren't holes for the "flowing water" to go into.  Flow direction is applied to see how and where water would or should move given the overall contours of the elevation slopes.  From there, a calculation is ran to determine what actually correlates to a running stream.  This calculation funnels into a conditional statement tool identifying areas that should be streams.  Finally, a feature class is created from that entire process and then displayed appropriately.  
We were also asked to create a Map Story, which displays the six stages of mountaintop removal. We were also asked to create a Story Map Journal, which is the building blocks in progress towards a final compilation for the project. 

GIS 4035: Remote Sensing; Week 4 - Ground Truthing

This week we continued with the Land Use and Land Cover map that was created last week.  This assignment was focused on ground truthing, which entails a personal visit to sites in the field to verify the land use or land cover located at a given location.  There are three different types of ground truthing and those as followed: 1.) In-situ data collection used to identify and guide range of variability of classification/interpretation; 2.) In-situ data collection used to conduct an accuracy assessment to verify that classification/interpretation is correct; and 3.) In-situ collection using a hand held field spectrometer.  For this exercise, we used the second method of ground truthing.  Thirty sample points were randomly selected to show ground truth.  With the help of Google Maps, I was able to identify whether those sample points were true or false.  The green points shown below are marked as "True", which means the street view from Google Maps indicates the same LULC as my map's classification.  The red points shown below are marked as "False", which means that location is found to have a different use, or cover. 

GIS 4930: Special Topics - Project 1: Network Analyst Results

In preparation for Hurricane Oscar, four products were created to help the community.  The target audience does not have a GIS background.  The first product communicates evacuation routes from Tampa General Hospital to two local hospitals, Memorial Hospital and St. Joseph’s Hospital.  An informative pamphlet was developed for distribution to patients and their families.  The pamphlet includes a plan showing two evacuation routes, evacuation timing, emergency contacts, as well as location details of each hospital.  The pamphlet is designed to inform patients and their families on the evacuation process.

Evacuation routes were created utilizing Network Analyst within ArcMap.  Data was retrieved from the University of West Florida, Florida Division of Emergency Management, and the Florida Geographic Data Library.  Directions for both destinations were extracted using the Network Analyst function within ArcMap. 

The pamphlet clearly depicts evacuation routes and other important information, however, it does not include local shelters near the destination hospitals.  It also doesn’t provide alternate routes for family members coming from major highways.  Regardless, this pamphlet would give me confidence that the hospital was taking good care of my loved one.

The second product communicates emergency supply routes to the delivery crew and emergency workers.  Grayscale maps were developed showing the distribution of emergency supplies by the U.S. Army National Guard to the three local storm shelters: Tampa Bay Blvd Elementary, Middleton High School, and Oak Park Elementary.  These three maps serve as an emergency supply route plan, with detailed directions for each location.  

Using Network Analysis, three separate maps were created to provide detailed information showing the routes from the U.S. National Guard Armory to the three local storm shelters.  The routes have been divided into several linear sections to provide drivers with a clear understanding.  Directions were extracted using the Network Analyst function within ArcMap.  An inset map provides an overview of the detailed routes. 

While the maps are affective in providing navigation details, they do not include contact data or timing on when supplies should be delivered.  The maps also don’t include addresses of the starting or destination points.  Regardless, they are adequate for their intended purpose. 

The third product displays multiple evacuation routes from downtown Tampa to the nearest local shelter, and is intended for distribution by television and newspapers.  Close up images of the routes are provided, as well as an inset map displaying the full route.  Text advises drivers on general precautions. 

Using Network Analysis and Adobe Illustrator, routes were created from 15 zones to the shelter. Color codes help the public determine the recommended route.  Streets and major roads along the routes are labeled accordingly and arrows provide directional information. 

While the map clearly depicts the evacuation routes for the downtown Tampa Bay area, it does not provide detailed driving directions, contact information, or the address of the shelter destination.  Nevertheless, local residents should be able to find the evacuation routes.  The map shown below displays the emergency supply route from the National Guard Armory to the Oak Park Elementary Shelter.

The fourth product, as shown below, depicts shelter locations and will be distributed to the general public by television and newspapers.  The area is divided into three zones, each with a designated shelter.  Informational text lists the shelter names, addresses, predictions on hurricane landfall, and safety precautions in the event of flooding. 

ArcMap was used to create the map showing the zones and shelter locations.  Major state roads and highways were labeled accordingly.  Illustrator was used to make it aesthetically pleasing and add textual information. 

The map serves its purpose in communicating the nearest shelter locations to the general public.  It would be helpful to provide contact information for the three shelter locations.  

Overall, I really enjoyed this first project and am pleased with how all of my maps turned out. However, the creation of the maps depicting the four different scenarios was extremely time consuming.  Nevertheless, I'm looking forward to creating more maps throughout the upcoming projects. 

GIS 4035: Remote Sensing - Week 3: LULC

This week we learned all about the classifications and codes of land use and land cover.  Land use refers to how land is being used by human beings.  Land cover refers to the biophysical materials found on the land.  In the map shown below, you'll notice that I used mostly level II classifications, with the exception of a few codes taken from the level III classification scheme.  The level II codes that were used are as followed: 11 for Residential, 12 for Commercial and Services, 13 for Industrial, 41 for Deciduous Forest, 43 for Mixed Forest, 52 for Lakes and 54 for Bays and Estuaries.  The code descriptions are all two digits to signify second level classification.  Whereas, with third level classification, the code descriptions are all three digits.  The level III codes I used are as followed: 111 for Trailer Parks, 121 for Retail, 122 for Schools, 144 for Highway, and 171 for Cemetery. 

After reviewing my final product, and looking over all of the codes to ensure I hadn’t forgotten anything, I realized I should have used code 61 (Forested Wetland) instead of 43 (Mixed Forest) for the swampy areas. 

This project was a lot more time consuming than I ever thought it would be. It took me all weekend just to draw all of the polygons.  Besides that, I really enjoyed this assignment and I love the outcome. 

GIS 4930: Special Topics - Week 2: Analyze

After creating a basemap of the study area and determining the potential flood zones, an evacuation route map was created based on four different scenarios.  Using the Network Analyst toolbar in ArcMap, I was able to define optimal evacuation routes to allow the transfer of patients from one hospital to another, deliver emergency supplies to shelters, transfer citizens to the nearest shelter location, and finding the nearest shelter for local residents.

The first scenario mentioned is the evacuation of patients from Tampa General Hospital on Davis Islands.  The hospital is located at the northern tip of Davis Islands, a small, residential area that was created with sediments dredged during the creation of the nearby canals.  Due to the very low elevation of the islands, the hospital will almost certainly be subjected to heavy flooding during the coming storm.  A route was created to evacuate all patients to other local hospitals before the hurricane hits.  The hospitals that were chosen to accept patients is the Memorial Hospital of Tampa and the St. Joseph’s Hospital.  These two routes were created by using the Network Analyst toolbar and ensuring that the Impedance was set to Seconds for calculating the routes.

The second scenario displays the distribution of emergency supplies by the U.S. Army National Guard to three storm shelters.  Emergency supplies will be delivered to the U.S. Army National Guard armory, located at Howard Ave. and Gray St. on the west side of the river.  Once the supplies reach the armory, National Guard troops will be tasked with delivering the supplies to the local storm shelters.  However, the supplies may not reach the armory before the storm hits, so drivers will have to travel to the shelters while avoiding flooded roadways.  Three new routes were created to assist the drivers and the National Guard troops to safely deliver emergency supplies to the shelters (Tampa Bay Blvd, Middleton High School, and Oak Park Elementary).  These routes were created using the same tools used in scenario one. 

The third scenario displays the creation of multiple evacuation routes for downtown Tampa.  Since the downtown area of Tampa is heavily populated, routes were created to assist the general public in evacuating to the nearest shelter location as quickly as possible.  This was accomplished by using the Scaled Cost function, which works by multiplying the Impedance attribute by the Scaled Cost attribute.  A New Closest Facility was created using the Network Analyst toolbar.  Using this, I was able to classify a point layer as Incidents, which shows all of the locations in downtown Tampa that need to be evacuated.  As shown in the map attached, the final destination for all evacuation routes is the Middleton High School shelter.

The fourth, and final, scenario shows residents which of the three shelters is closest to them by drive time.  The objective in finding the nearest shelter is to aid residents in getting to their designated shelter as quickly as possible and help alleviate confusion.  This was achieved by creating a New Service Area within the Network Analyst toolbar.  The area surrounding Tampa Bay Blvd Elementary is shown in light green, the area surrounding Middleton High School is shown in light red, and the area surrounding Oak Park Elementary is shown in light yellow. 

GIS 4035: Remote Sensing; Week 2 - Visual Intepretation

This first week's assignment provides an introduction to visual interpretation elements.  In doing so, it required looking at a couple different aerial photographs and then selecting specific elements that correspond to particular reference criteria.  The first map shown below focuses on two specific criteria: texture and tone.  Tone can be described as the brightness or darkness of an area, wile texture is how smooth or rough the surface appears. Five uniform areas were chosen to cover the range of both tone and texture of the aerial photograph.  The tonal areas range from very light, light, medium, dark, and very dark. Whereas the textural areas that were chosen range from very fine (still water), fine, mottled, coarse, and very coarse (rough surface).  The tonal areas are shown with the blue outline; the textural areas are shown with the green outline. 

The second map below displays features that have been identified based on four criteria: shape and size, shadow, pattern, and association.  Shape and size are criteria that first come to mind when visually identifying objects and features.  This criteria is show in green in the map provided below.  The features chosen for the shape and size criteria are: vehicles, road, and beach house.  Shadows provide an additional resource for identifying features in aerial photographs.  This criteria is displayed in blue with shrubs, water tower, and store front as the features chosen.  Pattern is useful for identifying groups of objects that individually may be insignificant, but together can make up one larger feature. The features chosen for the pattern criteria are: beach, parking lot, and water.  The chosen features are shown in pink.  Finally, association is a combination of an item with local elements to distinguish a common purpose. The two features I chose for this criteria are: pier and neighborhood; these are shown in orange.   

GIS 4930: Special Topics - Project 1: Network Prep

Welcome to week one of my Special Topics in GIS class.  This class is made up of four real life style projects, with topics that cover multiple weeks.  The order of the projects go as followed: project preparation during the first week, analysis throughout the second, and presentation during the third.
Throughout this first project, we looked at Network Analysis and how it can be used to prepare the citizens of Tampa Bay from a Hurricane that's about to make landfall.  Since this first assignment was the prepare week, the main objective was to acquire the data necessary for a base map and further analysis.  The data was all provided by UWF, which includes: a Digital Elevation Model (DEM), streets, point data files for fire departments, police departments, hospitals, a National Guard Armory drop location, and schools designated as shelters.  All of this data was used in compiling the base map shown below. The biggest aspect to this weeks project was preparing the potential flood zones.  This was done by reclassifying the original DEM into appropriately usable elevation increments.  The reclassified DEM was then converted into a polygon feature class for easier processing. It was discovered that all areas that have less than 6 feet of elevation will most likely have flooding.  To show this even further, a "Flood Zone" feature class was created to display the areas that are at risk for flooding.  The Flood Zone layer is shown with low opacity to better display the variations of elevation.  
The map below shows the Tampa Bay area as it relates to most likely flood zones, with transportation arteries that would be affected.