Friday, October 13, 2017

Lab 2 - Mountain Top Removal - Report Week


This week's portion of our mountain top removal lab was to finalize our data and compile all of the group's work into a layer package, and then use all the final data and outputs and finalize our storymap. We also created an online map to use as the image for the analysis section of the storymap. I was the group leader for the project, so I was also responsible for checking everyone's data to make sure that it followed the specifications, and then compiling the data into one dataset to redistribute to the group and upload to ArcGIS online to share publically. It was a much longer process than I expected, but without further ado, here is my final mountain top removal storymap.

Tuesday, October 3, 2017

Lab 5a - Intro to ERDAS Imagine


This week's lab was an in depth introduction into using ERDAS Imagine that covered many essential basic functions. We learned how to add data, and to make sure we set the raster properties in the raster tab. We also learned how to navigate through the different tabs and how to zoom and set the extent. The lab gave detailed useful information about how to navigate through the program. We also learned how to create and save a subset of the data to work with in ArcGIS or other programs. I appreciated this lab after having a brief intro into using ERDAS Imagine last week in our Special Topics in GIS course. This was a nice comprehensive introduction to the program which is completely new to me this semester. I am looking forward to unlocking all that this program has to offer.

Friday, September 29, 2017

Project 2 - Mountain Top Removal - Analyze Week



This week we analyzed the data for our mountain top removal (MTR) project. We started out by exploring MTR from 2005, where the areas had already been identified and each patch's boundaries were added as a polygon in a shapefile. We created training sample areas both in MTR areas and outside of known patches and looked at histograms to learn more about the data. Next, we had an introduction into using ERDAS IMAGINE to view signatures within the data. After we had analyzed the 2005 data, we began the process of locating MTR areas in the 2010 data. We began in ArcGIS, and used the composite bands tool to combine the 7 Landsat bands into a single raster file, and then used the extract by mask tool to clip the raster to our project area. Next, we used ERDAS IMAGINE to conduct unsupervised image classification. This included the detailed process of using the tool to create 50 different classes and editing the color scheme and then going through the map zoomed in to small areas where each pixel was visible and categorizing each class as MTR or NonMTR areas and assigning them each a color. This was a time consuming process that was definitely a bit tricky at first. The areas identified as MTR are larger than what is actually MTR, but we will be editing the data further to remove areas that are NonMTR, but have similarly colored pixels as MTR areas. Finally, we reclassified the data so MTR areas are assigned a value of 1 and all other areas are assigned a value of "no data".

Tuesday, September 26, 2017

Lab 4 Assignment - Ground Truthing


This week during our lecture and lab, we learned about the different methods of ground truthing methods to verify accuracy of aerial photography interpretation. The ideal method is by collecting actual in situ data at the study site, but that is not always possible due to the access to the area, property ownership, vegetation and/or water coverage, etc. We could not collect in situ data for our lab obviously, because we are not near the location of the imagery, so we turned to the higher quality imagery of Google street view to help decipher the actual land use/land cover (LULC) classes for our maps. We started by creating 30 points on the map, and then compared them to the actual street view of the area to determine if our class assignments from last week's lab were correct or incorrect. Some of the Google imagery was very clear and easy to decipher, while other areas did not help as much with identifying the correct class code. My randomly selected points were 63% accurate. I misidentified a few areas as being residential or urban, when they were in fact services or commercial. Overall, this was an interesting first step into ground truthing data.

Friday, September 22, 2017

Project 2 - Mountain Top Removal - Prepare Week


This week's lab assignment was the prepare portion of our mountain top removal study that we are working on with SkyTruth. Mountain top removal is a detrimental practice that is used to extract coal from deep within the Earth's surface. It scatters coal and silica dust, which harms people and animal's lungs and drastically changes the landscape. This week we began by editing a complex script to create a DEM mosaic raster, then run a series of hydrology tools on the mosaic to create a shapefile of the area's basins as well as a shapefile of the areas streams. We used our final outputs to create a basemap of our study area (which is pictured above). Finally, we created a file geodatabase to organize our created project data.

The next portion of the lab was to create a story map describing the 6 stages of mountain top removal. We used detailed pictures and descriptions for each slide depicting each of the 6 steps in a condensed story tour format. We then edited the map layout and basemap, as well as the story map title and added our name as the author. We saved the maps and made sure they were able to be shared publically. Here is my finished masterpiece.

Finally, we created the template for our story map that will be used to share our project results in. We created a more detailed story journal, which provides more room for text and explanation. We set place marks for each slide, set a picture for the introduction slide, shared the link for our "6 Stages of Mountain Top Removal" story tour, and added our basemap to their respective places on the slides. Here is a sneak peak of my work in progress.

Overall this week had its tough moments, but the lab is definitely broadening our experiences. I am excited to be working on a project associated with SkyTruth, and I am interested to see what the analysis holds.

Tuesday, September 19, 2017

Lab 3 Assignment - Land Use/Land Cover Classification Mapping


This week's lab assignment aimed at making us more familiar with aerial photography interpretation by learning about land use and land cover (LULC) and how to identify different LULC code areas. Land use refers to visible changes that humans have created, whereas land cover refers to the biological characteristics of different areas. I enjoyed this lab, and found it to be similar to a hidden object game, although I did have some difficulties with some of the urban areas that were not residential. For this lab, we created polygons around visibly different LULC code areas and added the code and code description to the attribute table for each field. We finished the map by labeling the areas by their LULC code and creating symbology that assigned each LULC code a unique color. I edited the transparency of the created LULC layer so that the aerial photograph was still visible underneath. I am definitely looking forward to next week's lesson to see how correct my selections were.

Friday, September 15, 2017

Project 1 - Hurricane Preparedness Planning For Tampa, Florida - Report Week



This week's assignment was an in-depth exercise in disaster preparedness map creation. We worked with the data and routes edited and created last week to create seven different maps and a report providing an overview of the project and deliverables. We first had to create a pamphlet depicting routes and directions for patients evacuating the low-lying Tampa General Hospital to either Memorial Hospital of Tampa or St. Joseph's Hospital. Next, we had to create rough maps to direct supply route drivers from the National Guard armory, where the emergency supplies are stored, to each of the three hurricane shelters (Middleton High School, Tampa Bay Blvd. Elementary School, and Oak Park Elementary School). The next deliverable was a map depicting evacuation routes from downtown Tampa to the nearest shelter for residents of the downtown area (top picture). Finally, we created a map showing which shelter was closest to each area of Tampa to direct residents to the closest shelter and minimize chaos and confusion in the face of the coming storm. We used the network analyst extension to create all routes and service areas, as well as provide detailed step-by-step directions for the hospital evacuation pamphlet, as well as the supply delivery route maps. It was a lot of work for a short period of time, but I feel happy with my final products, and look forward to our next lab adventure.