Mountain Top Removal: Analyze Week

This week is a continuation of Mountain Top Removal. The main objective for this week was to Reclassify an MTR raster. To accomplish this, we used a Landsat image and manipulated it using ERDAS Imagine. I also learned about SkyTruth, a non-profit organization that provides  remote sensing and digital mapping technology using satellite images and other visual technologies, to provide information and data to environmental advocates, the media, and the public.  

My task was to examine a digital multispectral image that represents a portion of our study area. Additionally, this week we signed up for small groups. I am in Group 3 and we have two Landsat images to work with:

LT50190332010243EDC00.tar

LT50190342010243EDC00.tar

Using ArcMap I was able to use the Training Sample Manager to sample various polygons from the image and classify them as MTR or NonMTR. This could be a tedious task but I found it mildly entertaining.  I created the following histograms and scatter plots to check the accuracy of my sampling:

 

Looks pretty good to me. After this, I used ERDAS Imagine to create an AOI layer and then use the "Grow tool" to select pixels that were in an MTR area. Conducting this analysis took time for many of the tools to process the data. Once again, this was a fun exercise even though I had to wait patiently between each step of processing data to arrive at my final product that I then exported back to ArcMap to complete.  Below is my final re-classified MTR image 

ERDAS Imagine - Remote Sensing & EMR

This week I learned about ERDAS Imagine.  This is a remote sensing application for GIS. The ERDAS Imagine program allows us to manipulate raster images to extract information. In this lab we were using a raster of Washington State to classify the types of land cover such as vegetation, water, bare earth, etc. To do this, initially we began with some calculations of electromagnetic radiation (EMR). The chart below shows the electromagnetic spectrum:

There were a few formulas involved such as Maxwell’s wave theory: C = λν and Planck Relation: Q = hν 

But the real point of this lab was to learn about ERDAS Imagine and how to process a raster image to understand the types of land cover depicted. A very useful feature was the ability to switch between TM False Natural Color and TM False Color IR. The False Natural Color gives the bare ground a pink color which adds a sharp contrast making it easier to discern from the green forested areas.

Overall this was a fun Lab but I am sure I will need more practice with ERDAS Imagine to feel as comfortable with it as I do with ArcMap. Below is the map I created for this week's Lab. It shows an area in Washington State that I have re-classified by using the Inquiry Box and manipulating of the ERDAS Attribute Table.

  

Mountain Top Removal and Map Stories

This week I worked on a map concerning Mountain Top Removal for coal mining in the Appalachian mountain region. The EPA defines Mountain Top Removal as:

“Mountaintop removal/valley fill is a mining practice where the tops of mountains are removed, exposing the seams of coal. Mountaintop removal can involve removing 500 feet or more of the summit to get at buried seams of coal. The earth from the mountaintop is then dumped in the neighboring valleys.” (ilovemountains).

I used several tools to make this map. The bulk of the work was in creating the Hydrology Dataset from the Mosaic Group Raster Data. There were plenty of steps involved including:

- The Fill tool — This step modifies the raster to prevent water flow from pooling.
- The Flow Direction tool — This process assigns each pixel a value representing the direction of water flow across each cell.
- The Flow Accumulation tool — For each cell in the raster, this tool calculates the total number of other cells that flow into it.
- Calculate 1% of the pixels in the image —  A generally accepted condition is that the Flow Accumulation value must exceed 1% of the total number of pixels in the raster. If more than 1% of the total pixels in the raster flow into an area, it is probably a stream.
- The Con tool — This tool defines the threshold of flow accumulation values that qualify something as a stream
- Stream to Feature — This tool creates polyline vector features from the Con tool output raster

This was a lot of work but I am glad I used all the above tools for the exposure and practice. Below is my basemap.

Another skill I worked on this week was to create a Story Map and a Story Map Journal. The subject for both stories concerned the 6 Stages of Mountain Top Removal:

- Stage 1: Clearing 

- Stage 2: Blasting

- Stage 3: Digging 

- Stage 4: Dumping waste 

- Stage 5: Processing

- Stage 6: Reclamation 

You can find My Story Map at: Mountain Top Removal

I also created a Story Map Journal, but this is still a work in progress. If you would like to take a look at it now, go here: Mountain Top Removal: Streams and Basins

Ground Truth: Error Matrix & Overall Accuracy

This week I modified my map from last week, Land Use Land Classification. The challenge last week was to not use any tool that would help you verify the type of land I was classifying. I could only use the aerial image (.tif) of Pascagoula, MS. and make my best guess at what type of land I was classifying. This week, the challenge was to take an unbiased sampling of the land I classified and compute the overall accuracy. Under normal circumstances, you might travel to the location and conduct fieldwork to determine the correct land use/classification by conducting a Random, Stratified Random, or Systematic Sampling. Since I could not actually travel to Pascagoula, the tool of choice was Google Maps - Street View. This is an excellent proxy for actually being there.

Another tool I created and used was an Error Matrix. I conducted a virtual Random Sampling of my map starting at the North West border and working my way South to cover the entire study area and I marked as "True" or "False" the areas I had previously classified. I used Google Maps to confirm what I classified was correct - or not. I found that I miss-identified a few land types such as:

- A Commercial / Industrial area (Category 15, USGS Andersonian framework for national land use and land cover) - What looked like an Industrial area to me turned out to be a shopping center.
- A Residential area (category 11, USGS Andersonian framwork) - I correctly identified most of the residential areas, but one area I classified as residential turned out to be a cemetery!

My Overall Accuracy was 76%. Here's the map I created.

   

Network Analysis: Report Week for Tampa Evacuation

It has been a very long week. Lots of excitement and drawing of Storm Drains with Brevard County GIS Dept. I am learning a great deal and keeping busy. For this Special Topics assignment, I completed my work on the Tampa Bay Evacuation project by compiling a Pamphlet that could be used to inform relatives and hospital patients of the Tampa General Hospital of critical information in the event of an evacuation. Below is one page of the pamphlet I created.

Another Network Analysis I performed was to compute multiple evacuation routes from downtown Tampa to Middleton High School Shelter. This involved setting-up the Network Analysis to load or account for fifteen different locations in the downtown Tampa area. Here;s my results:

Another Network Analysis I ran was to generate multiple polygons to find the closest shelter, there were three to consider, for residents in the Tampa area. The analysis was interesting  because I cannot remember using this tool before though I do recall using the Multiple Ring Buffer. This was better. Here's my map for the Closest Shelter:

Land Use Land Cover: Pascagoula, Mississippi

Another fun week! This week I worked with the USGS Andersonian system for classification of  Land Use Land Cover. I continue to hone my skills for using, Shape/Size, Shadows, Patterns, and Association, for identifying various land features. In this exercise, we used the USGS Anderson framework for naming Land features. The area we examined was Pascagoula, MS. This location has loads of water features. Digitizing the water or the river, was my biggest challenge. I took a large cut at trying to draw my initial polygon around as much water as I could only to have the polygon distort or completely disappear. I finally got the Create Features to work by digitizing smaller areas at a time. This work can be tedious, but I enjoyed it very much. As it happens, I am working on a similar project at my internship with Brevard County, where I am digitizing and updating storm drain pipes throughout the county.    

Network Analysis Week 2 - Tampa Evacuation Routes

This week it was time to analyze our data from the Prepare Week for the Tampa Evacuation exercise. The objective was to find the closest shelter for residents in specific areas in Tampa. Additional objectives included finding the best routes to supply the Shelters from the Tampa Armory and where and how to best evacuate patients from Tampa General Hospital located on Davis Island.

I learned a lot using the Network Analyst Toolbar and the Network Analyst Window. I created a new Network Dataset and set-up the network using Time (seconds) as the Scaled Cost to be evaluated relative to the route selection. I also used the Flood Zones layer (grid code) to group the Scaled Cost to be evaluated in the route selection. I created additional polygons to form the three sections around the Shelter facilities where residents would evacuate to in the event of flooding. I believe this was the most important message to convey, not that a hospital evacuation nor delivery of needed supplies from the Armory aren't important, it's just that I felt informing thousands of potentially panicking people (the triple "P" threat) where to go for safe shelter seems more important to me. Below is the map I created this week.

GIS 4035 - Visual Interpretation

This has been another busy week. The Lab for Photo Interpretation and Remote Sensors was actually a three for one.

The first Exercise for Visual Interpretation was to use Tone and Texture to discern various features using an aerial photo. I used an aerial photo that showed features such as a runway, housing subdivisions, water and vegetation. Tone is defined as the  brightness or darkness of an area and the first part of the lab had us classify areas from very light, to very dark. Using the "Convert Graphics to Features" went smoothly for the first Exercise, but I had some technical challenges for the second Exercise. Another objective of this lab was to look critically at the features you are given in an aerial photo so that you can distinguish the subtle differences between organic and man-made features. Below is the map I created for Exercise 1:

  Exercise 2 consisted of Identifying Features by using Shape & Size, Shadows, Patterns and Association. The objective here was to use all available information to ensure you correctly identified the object or feature you were looking at. As I said above, I had some technical challenges with part 2 with the "Convert Graphics to Features" option. It  seemed as though each time I needed to convert my selection (Shape & Size, Shadows, Patterns and Association) the option was greyed-out and not available. I tried Starting an Editing session and Stopping Editing; I tried opening and closing AcrMap. I walked away for a while and when I came back, the Convert option was still not available. Somehow, I did get the Convert Graphics to Features to work....mostly. But I do not know why the option was greyed-out.  Below is my map for Exercise 2:

The last portion of the Lab, Interpreting Color, had us use two .tif's to compare the indicated color of features using True Color and False Color. False Color is also called Color Infrared. I found various features including land, water, healthy vegetation and distressed vegetation. The colors I found matched closely, but were not exactly the same as what the "legend" for "False Color IR vs True Color" stated in the Lab instructions.