Project 5 Special Topics in GIS

For my fifth and final project in Special Topics in GIS I created a Power Point presentation and an Abstract, concerning the values of the timber located on a woodlot in New Brunswick Canada.  Provided are links to my abstract and Power Point Presentation.

www.students.uwf.edu/prc7/Project_5.pptx

www.students.uwf.edu/prc7/Abstract.pdf

Project 4 Forestry Report Week

This week we were to create a poster that portrays our stand or viewpoint on the practice of Clear-cut forestry.  Although clear-cut forestry has in the past had a adverse impact on our environment, I believe that using the latest management practices, which are based on experience and science, that a balance can be struck between clear-cut forestry operations and the environment.

Project 4 Analyze Week

How the Public Perceives Forestry (and Why It Matters)

As the title indicates, this article discusses how the public perceives forestry operations and why these perceptions matter.  Although this article is written about the Northwest United States, I believe it is relevant worldwide.    The article states that, of the people interviewed, nearly 70% were opposed to the practice of clear cutting, based largely on aesthetics.  However, when provided with information that educated them on issues such as future land use and the science that is now applied to most clear cutting operation, they were much less likely to be opposed.  One caveat to providing this information was to ensure that the information provided was factual, based on scientific research, and understood by those who received it.  It was also important to tailor the information to the audience, in other words, address the issues pertinent to that particular region.  In the cases that this was achieved, the idea that clear cut forestry operations were a valuable tool to aid land managers in providing a sustainable resource and still have the best interest of the environment in mind was much better received.  

 

Citations:

Ecological

1^st Article: Greenberg, Cathryn H., Harris, Lawrence D., Neary Daniel G. 1995. “A Comparison of Bird Communities in Burned and Salvage-Logged, Clearcut, and Forested Florida Sand Pine Scrub.” The Wilson Bulletin 107, no 1:40-54. http://www.jstor.org/stable/4163511

 

Economic

1^st Article: Hahn, W.A. and Knoke, Thomas 2010. Sustainable development and sustainable forestry: analogies, differences, and the role of flexibility. European Journal of Forestry 129:787-801.

 

Aesthetic

1^st Article: Murray, Sarah, and Peter Nelson. How the Public Perceives Forestry (and Why It Matters). lecture., University of Washington, 2005. https://digital.lib.washington.edu/researchworks.

Module 10 Supervised Classifiction

Project 4 Prepare week

For prepare week of Project 4 I was required  to locate clear cut areas that were visiable from roads that run throughout the area.  The study area was a  1400 ha “woodlot” located in New Brunswick, Canada. The first step in the analysis was to identify recent clearcuts adjacent to the main roads.  After adding the “cover” feature class to my workspace, I selected the main roads by selecting by attribute the cover type “RD”.   Next I did a select by location query to find the cover types that were adjacent to, or intersect my main road selections. Once this was complete I isolated the clearcuts and treed bogs from the selection by doing a select from current selection “all treed bogs and stands with an age of greater than or equal to “0” years and less than or equal to 5 years”. This left me with 43 clearcuts visible from main roads.  I then opened the attribute table calculated the statistics for the “shape_area” field to determine that the total area of clearcuts adjacent to the main roads occupied approximately 121 ha. 

Next I needed to identify boundaries shared by main roads and clearcuts.  To start this process I opened the “feature class to feature class tool.  I named the output “cover_arcs” set the coordinate system to the appropriate projection, and made all other entries, and ran the tool. I then joined the coverage PAT to the cover_arcs AAT using “COVER# and LPOLY as identifiers. Once this was complete I did a select by attribute from cover_arcs to isolate arcs with recent clearcuts on the left using the expression provided in the lab sheet.  I added a new field in the attribute table “LeftPoly” and assigned it a value of “CC” with the field calculator.  I then repeated these steps for the clearcuts on the right. I then removed the join.  I then did a select by attribute to calculate the total length of boundaries shared by young clearcuts and main roads.  This turned out to be 44 arcs with a length of nearly 8 kilometers (7.74).  To see how many, if any of the clearcuts were adjacent to each other I used the Dissolve tool. Once this tool was ran, it indicated that one area was nearly a kilometer long.

To calculate the viewshed for all roads I opened the “clines” feature class in Arcmap.  I then removed the sewer line and power line easements using the “definition query” and renamed the layer “Main Roads”.  Using the Merge tool I combined Main Roads with proads named the new feature class “RoadViewers”.  I then added the elevation raster as a layer.  I used the “feature to raster” tool to add stand heights from the cover layer to the elevation layer. I named the output “HeightClasses”.  To fix a band of “no data” I merged the publicrow with the cover layer and then computed a new “HeightClasses” raster.  Using the Raster Calculator and the elevation raster, and HeightClass layer I created a viewing surface, named oddly enough “ViewingSurface”.  Using the viewshed tool and the RoadViewers feature as the observer and ViewingSurface as the viewing surface I created a new layer named “Viewshed”. 

To determine the amount of visible clearcut I used the definition query to isolate young clearcuts in the cover layer, and renamed the layer “RecentClearcuts”. This operation selected 121 stands.  I then used the “feature to Raster” tool to convert “recentClearcuts” to a 10 m cell-sized raster and named the output “Clearcuts”.  To calculate where both “Clearcuts” and “Viewshed” exist I used the Raster calculator with “Clearcut” & “Viewshed” as input and named the output “VisibleClearcuts”.  The map above is a base map that depicts the study area I will be using for the entire project.

Module 9 Unsupervised Classification

For this week in Remote Sensing we explored unsupervised classification of features, using both ArcMap and ERDAS.  The map above depicts five separate feature classifications.  To create this map we first took a provided image and created another image with 50 classifications.  Once this was complete, we then reclassified the image into just five classifications; this was all done in ERDAS.  To make the final map I used ArcMap and added all the map essentials.

Web Applications - Report Week

For the third and final portion of "Web Applications" we were to refine our web maps from last week.  For this I changed my subtitle to "Where I spend My Time and Money in Okaloosa County", I also changed my first tour point from an introduction to an actual point on the tour (probably should have done this in the first place), I then changed the layout parameter from the default of "three-panel" to "integrated" just like the look of the integrated layout better, and finally I changed the zoom layer parameter from the default of "-1" to "19" this lets the viewer see a close up of each point of the tour by simply clicking on that point. Like I mentioned last week my tour is not real flashy but I am happy with the results, hope you enjoy!

Below is a link to my final map:

http://students.uwf.edu/prc7/GIS4930/Module3/index.html

Project 3: Web Applications - Analyze Week

During Analyze week of Module 3, Topics in GIS, we created story maps. My story map depicts ten locations in Okaloosa County, Florida where I spend my time and money!  For the project, we were provided with basically everything we would require; we just had to update it with our information.  I first found or took photos of my locations of interest, and then using a picture editor resized the photos for use in the map.  I then found the coordinates to the locations and converted them to decimal degrees, and wrote a brief description of each location. Once this was done I updated the .csv file that was provided.  Done with the .csv file I created a web map in ArcGIS online, and added my points to the map along with a supporting layer.  Then I “pointed” my map template, which was provided, to the map I had just created in ArcGIS online, and added the UWF logo.  After this was complete I tested my link to ensure my map worked, much to my surprise it did!  This week took me way out of my comfort zone, the lab handout provided helped, but the “Read Me” file provided was a life saver!  Below is a link to my map it is not real flashy but it has been a rough week. Hope you enjoy.

http://students.uwf.edu/prc7/GIS4930/Module3/index.html

Module 8 Thermal and Multispectral Analysis

In Module 8 we created composite images and continued exploring histograms, breakpoints, and band combinations to better understand and view features. In this module we were task with locating a feature of interest within a given image and creating a map, using the skills gained in this module to display the feature on a map. The map above depicts Test Range B-70 located on Eglin, AFB in Florida.

Web Applications: Prepare Week

 

For this phase of project three, in Special Topic in GIS, we were tasked with exploring the workings of a story map.  A story map is a map that uses text, multimedia, and other interactive functions, to inform, entertain, and inspire viewers about a topic.

I intend to create a story map that shows eleven points of interest within Okaloosa County, Florida.  Originally, it was to be “points of interest in my town”; however, living in Holt, Florida that would be Brown’s Gas Station, a church, and an abandoned elementary school.  So, I expanded the area to include Okaloosa County.  I plan on starting with a base map of the area and show the location of the points of interest with configured pop-up icons  Once the user clicks on the icon, it will show a brief description, with a prompt for a more detailed description, such as address, schedule of events, hours of operation, or menu depending on the location.  I do not plan to have a route or tour of the sites, but by individualizing the icons, the user should be able to click on the location of interest and find all related information with a few clicks of the mouse.

 

Module 7 Multispectial Analysis

For this module in Remote Sensing we were to identify certain features of an image based on their pixel count in certain layers.  This was accomplished using the image histogram and the inquire cursor tool.

In layer four there was a spike between pixel values 12 and 18. This was on the left side of the histogram, and from the module I knew this would mean a dark area of the image.  From the image it appeared this may be deep water. Using the search cursor I verified that there was a correlation between pixels in the histogram and the area I had selected on the image.  Once this was done I used the Inquire box and the Subset & Chip tool to select an area that best depicted this feature in the image and saved this as new file. I then opened the file in ArcMap and created the map above.

In layers 1-4 there was a small spike around pixel value 200 and a large spike between pixel values 9 and 11 in layers 5 and 6. This was on the right side of the histogram, and from the module I knew this would mean a very light area of the image.  From the image it appeared this may be the snowcapped mountains. Using the search cursor I verified that there was a correlation between pixels in the histogram and the area I had selected on the image.  Once this was done I used the Inquire box and the Subset & Chip tool to select an area that best depicted this feature in the image and saved this as  a new file.  I then opened the file in ArcMap and created the map above.

  

 

 

 

In certain areas of the water, layers 1-3 are much lighter than normal.  Layer 4 was a little brighter and layers 5 and 6 were unchanged. From the image it appeared this may be several areas of shallow water. Using the search cursor I verified that there was a correlation between pixels in the histogram and the area I had selected on the image.  Once this was done I used the Inquire box and the Subset & Chip tool to select an area that best depicted this feature in the image and saved this as a new file. I then opened the file in ArcMap and created the map above.

 

 

Module 6 Spatial Enhancement

 

 

 

Week 6 of Remote Sensing covered Spatial Enhancement.  In this module we covered various methods used to enhance imagery. The map above is the result of starting with a LandSat image and using the convolution tool to enhance the image, using the trial and error method; I finally decided that using the 3x3Sharp5 filter worked best to remove the striping and still keep the image somewhat identifiable.  I am sure that with more experience I could do a better job, but for now this is the best I could come up with.  

  

Network Analyst - Report Week

This scenario involved developing three separate crude maps for the distribution of emergency supplies by the U.S. Army National Guard to three designated storm shelters.  The map above depicts the supply route to one of those shelters. All data to complete this map had been created during the analysis week of this project.  This route is from National Guard Armory to Oak Park Elementary School.  Written directions were also produced, copied and reformatted.  This map was created in ArcMap, for ease of use, the route was shown in its entirety in one data frame, then other data frames were added showing the route in shorter segments; the reformatted driving directions were then added to the appropriate map segments then exported to Adobe Illustrator where some text was fine-tuned and it was put in a Grey-Scale format, as this would make the map easier to reproduce.  Once this was complete the map was exported as an image file.    

Network Analyst - Report Week

This map was created for a scenario which involved developing an informational pamphlet for patients and their families that required evacuation from Tampa Bay General Hospital to two other hospitals in the city. The map above depicts the evacuation route from one of those hospitals. In this case, the pamphlet was provided; however, maps and directions to the receiving hospitals needed to be created and added to the pamphlet.  All data to complete this map had been created during the analysis week of this project.  This route is from Tampa Bay General hospital to St. Joseph’s hospital.  Written directions were also produced, copied and reformatted.  Once the map was completed it was exported as an image file and placed in the pamphlet.  The reformatted driving directions were also added to the pamphlet.  Once the map was placed in the pamphlet, minor modifications were made to have the map and driving directions appear as uniform as possible while still proving useful.

Network Analyst-Analyze Week

This was the second week of my Network Analyst project.  After preparing the data last week, I calculated two evacuation routes from a hospital in the flood zone to hospitals that were close by and at a higher elevation.  Then I calculated supply routes from the National Guard Armory to the three designated Storm Shelters.  Both the evacuation routes and supply routes were then assigned a name and separate color.  This was all completed the “Routes” tool in the Network analyst toolbox in ArcGIS.  Next, using the “New Service Area” function also located in the Network Analyst toolbox, I calculated the nearest Designated Storm Shelter for areas within Tampa Bay.  Once the polygons were created they were each also given a different color.  Once the routes and storm shelter areas were calculated I created the map above and like last week shared it as a map package. Then I wrote an addendum to my E-mail explaining what I have done to date.   The map package, and e-mail were then zipped into a single file and put into the dropbox.

Intro to ERDAS Imagine and Digital Data

Week 5 of Remote Sensing covered the basics of ERDAS Imagine.  For this module we were given an opportunity to explore where some of the tools were located and how to use them. We also used the viewer to view data in ERDAS Imagine.  We then preprocessed an image for making a map in ArcGIS. I am however a little leery because the lab instruction mentioned “crashes” and using “work arounds” to make ERDAS Imagine work properly.    But with that said, I was able to create the map above without much problem.  The map depicts a sub-section of an image of forested area in Washington state which was classified into different types of ground cover.  

Network Analyst - Prepare Week

For this week in Special Topics in GIS, Project 2, I started to prepare data for a Network Analyst project. Project 2 is a hurricane evacuation scenario for the Tampa Bay, Florida area. First I clipped and re-projected all data sets into the same area and projection.  This was completed using a provided mass clipping and re-projection tool.  Then a DEM was reclassified and converted into a vector file format. From that I created a “Flood Zone” layer.   New fields were added to the streets layer in anticipation for upcoming network analysis.  After that I edited and exported the metadata.  I created a base map of the area (above), and shared it as a map package. Then I wrote an E-mail to my “employer” and a “Coworker” explaining what I have done to date, and what I think the next steps should be.  The map package, metadata, and e-mail were then zipped into a single file and put into the dropbox.

Ground Truthing and Accuracy Assessment

Module 4 of Remote Sensing, covered Ground Truthing and Accuracy Assessment.  For this project we used the LULC map from last weeks module, and since actually going to the site and verifying the accuracy of our LULC assessment was not feasible, Google maps street view was used.  First I selected 30 random points within the study area. Then using Google maps I located these exact points, and zooming to the street view I was able to verify if, in fact, my assessment of that particular point was correct.  Once this was complete I calculated the overall accuracy of LULC Assessment.  In this case, of the 30 points that were checked, three were found to be incorrectly identified resulting in an overall accuracy of 90%.  

LULC Classification

Module three of my Remote Sensing course covered Land Use and Land Cover Classification.  Given a aerial photograph of the Pascagoula, Mississippi area I created a shape file of polygons that depict different land uses and land covers. This was done only to the second level as shown in the legend.  Techniques and criteria used to identify these areas were covered in Module two. 

Project1 Analysis

During week two of project one, Statistical Analysis with ArcGIS,  I ran an Ordinary Least Squares (OLS) regression model using known meth lab locations as a dependent variable, and twenty-nine  various social statistics as independent variables. Using the OLS model and a set of criteria I was able to pare the list down to nine independent variables,(shown in the table above) that are the best indicators of future meth lab locations. The results of my final model are displayed in the map of standard residuals (also shown above).  This model was created to give law enforcement the ability to target areas with a high propensity for meth labs in the future.