Lab 4: Proposed Expansion of Protected County Forest to Increase Corridor and Forest Transitional Habitat

Introduction

For my independent project, my question was:

• Which areas within Eau Claire County should be added to the protected county forest land? 

To find these areas, I asked to following two questions:

• Which areas in Eau Claire County would be best for increasing protected forest transitional habitat?
• Which areas in Eau Claire County would be best to add corridors between currently protected county forest areas?

These areas I've proposed would increase the boundaries of the protected county forest. Currently within Eau Claire County the protected county forest runs along the Eau Claire River, then continues to expand to the east. Some of the protected county forest areas along the river have significant space between them. Adding corridor habitat would increase the ability for wildlife to travel between these areas, therefore increasing the wildlife habitat areas by increasing the connectivity between the protected areas. This increased connectivity between protected areas can be critical to the survival of certain species. Additionally, when comparing the WDNR Ecological Landscapes data, it is apparent that forest transitional areas is the landscape type has the smallest expanse within Eau Claire County. Knowing this habitat is more scarce within the currently protected county forest in Eau Claire County and throughout the state, priority could be placed on forest transitional zones when expanding county forest protection.

This information of the proposed sites would be presented to officials in charge of controlling the protected county forests, with the hope that the officials would consider adding these parcels of land to their protected areas. These officials would most likely be a part of the WDNR.

 

 

Data Sources

For my projected, I used data from the following databases: Esri2013, WDNR, and EauClaire. For this project I was in need of river data, county forest data, the Eau Claire county boundary data, ecological landscape data, road data, and city location data. My concerns about the data include mostly about how complete the data sets are. For example, I feel as if there may be critical information know but withheld from the feature classes, such as information unique to individual county forest sites. This information could influence if it the areas is truly ecologically worthy or in need of being protected, and or connected by a corridor. Also, the river data was used to create buffers, in which areas within this buffer area would be preferred. This data does not include information of the quality of this river, which is important factor when deciding which areas should be protected. Metadata for the WDNR data can be found here: http://dnr.wi.gov/maps/gis/metadata.html.

 

 

Methods

 

For my methods to find the proposed corridor habit, I began with my Eau Claire county boundary feature class, and clipped all feature classes to this area. From there, I converted the data frame and all of my clipped feature classes to the NAD_1983_HARN_WISCRS_EauClaire_County_Meters projected coordinate system. From there I began using the WDNR county forest feature class, and ran the buffer tool to create a feature class that includes county forest area with a 400 meter buffer surrounding them. From here I erased the currently protected county forest areas from the county forest buffer layer to find areas that are currently unprotected but nearby protected areas. Following this, I added the clipped river feature class and selected the Eau Claire River using select by attribute. I created a new feature class from this, followed by running the buffer tool to make a 400 meter buffer surrounding it. I used the intersect tool to combine my river buffer feature class and my proposed corridor sites feature class. These output contained unprotected areas between currently protected county forest, that were within 400 meters of the Eau Claire River.

 

For my methods to find the proposed forest transitional habitat, I first used the clipped ecological landscapes feature class to select forest transitional habitat by using the select by attribute tool. From here I created a new Forest Transitional feature class, which I then used the erase tool on to erase currently protected county forest areas from it. This output gave me unprotected Forest Transitional habitat, however I continued to narrow the results by intersecting it to a river buffer feature class I had created. This river buffer feature class included areas within a 200 meter distance. Therefore the final output includes currently unprotected forest transitional habitat within 200 meters of a river.

Figure 1: Data flow model created using Model Builder
 

Results

 

My two sets of results are depicted in yellow and orange. The yellow areas signify the areas that are proposed areas for corridors habitat. This areas would increase connectivity between the currently protected county forest area along the Eau Claire River. The areas are within 200 meters of the Eau Claire River and within 400 meters of a currently protected county forest area. The orange area is the proposed county forest sites that would increase the amount of protected forest transitional landscape within the county forest area. This area consisted of unprotected forest transitional area that is with 200 meters of a river. Overall, if both proposed sites were excepted, the connectivity between county forest areas would increase and as well as the county forest's landscape diversity.

Figure 2: Map of proposed corridor and forest transitional sites to expand the county forest within Eau Claire, WI.

Evaluation

I found through completing this project that model builder is an effective tool that allows the user to make easy changes to their data flow model. Overall I am mostly happy with the my projects outputs. I hoped the corridors locations would be located in unprotected areas between currently protected county forest areas. There are a few areas that would expand the protected area however are located more on the outskirt of the protected forest than what is needed for new corridor space. This was challenging to find an output that would have a large enough expanse to connect the majority of the protected county forest areas, but not too large that it would include too much area on the outskirts of them. Getting an output that is solely between the protected forests is something I would analyze further if I were to repeat the project. Additionally, there are a few portions of the forest that would still not be connected to other portions of the forest if this model were actually implemented. This occurred because these portions of the forest are not within the 200 meter distance of Eau Claire River, which was criteria that I specified for my project. If the project were to be repeated, several aspects could be analyzed further. For example, currently the distance of the buffer zone are somewhat arbitrary. More research can be done to make this buffer distance values have more significant meaning. Additionally, more ecological data would need to be collected within the currently protected portions of the county forest, as well as the proposed sites. This information could be used to determine if the sites are ecologically valuable to protect. A ranking system based on this ecologic value could be created to prioritize which sites should be protected over others. Overall, more information about what is located in these proposed sites is needed to make the results more accurate.

 

Lab 3: Bear Habitat

Goal

 

The goal of this lab was generate a final map indicating suitable habitat for bears within Marquette County, MI using geoprocessing tools within ArcMap.

 

Background

 

There were eight objectives to complete within this lab, which were created to provide exposure to using tools within the ArcMap toolbox. Through this lab I was required to use tools such as Intersect, Buffer, Erase, and Dissolve to determine which habitat was most suitable for bears based on provided criteria. I documented the processes I used to generate my final output map within a data flow table. During this lab I also practiced using the Python Script tool.

 

Methods

 

1) Data Flow Model Process

My first task was to create a new feature class of suitable habitat for bears, therefore I choose to intersect the bear locations and landtype feature class. Using this output, I summarized the field indicating habitat type, to find which habitat type had the most amount of bear sightings. The top three results were Mixed Forest Land, Forested Wetlands, Evergreen Forest Lands.

 

My second task was to create a feature class that would help me determine how many of bears were observed within 500 meters of a stream. For this, I was able to use select by location to determine how many bears were within 500 meters of a stream. The total was about 72%, which suggests that rivers are a critical part of their habitat. From here I used the buffer tool to create a feature class that had an area of 500 meters around all streams. 

 

 

My next task was to create a feature class for suitable bear habitat, which I now knew should include the top three habitat types, as well has stream habitat. To create this, I selected the top three habitat types through Select by Attribute. Next, I intersected the new steams with buffers feature with the selected habitat types. The dissolve to was necessary to use on the output because internal boundaries needed to be erased. 

The following objective was to find which DNR management lands lie within the suitable habitat areas. The intersect tool was used to generate the output, followed by the dissolve tool to remove the internal boundaries. 

 

Lastly, I was required to create a feature class of the DNR management areas within the suitable habit that are at least 5 kilometers away from Urban Areas. For this, I chose to create a feature class of the Urban Areas. From there I ran the buffer tool to create an area of 5 kilometers around the data points. I finished this task by running the erase tool, within the DNR management areas as my input, while erasing the Urban Areas feature class. This created an output of DNR areas away from Urban space. 

 

2) Using Python

 

In this portion of the lab I practiced using the Python script tool within ArcMaps. Three scripts were ran, which generated three feature classes. The first generated a stream feature with a buffer of 1 kilometers. The second generated an intersect between the steam buffer feature and the suitable habitat feature. The final script generated feature class of the suitable habitat which now does not include urban areas.


Results

  

My results suggest that streams are a critical feature with bear habitat. Additionally, it appears that the majority of bears were observes is in the northwestern and central portion of Marquette County, and therefore mostly away from the urban areas within the southern portion of the state. Additionally, I was able to determine that Mixed Forest Land, Forested Wetlands, Evergreen Forest Lands are the habitats were bears primarily are observed. These factors should all be taken into account when developing new protected bear habitat areas. The map indicates there are currently no DNR management sites in the northeaster portion of the county where bear prevalence increases. If the DNR were to construct additional sites, the area of suitable habitat in the northwestern portion of the map should be high considered.

 

Figures



Figure 1: Lab 3 data flow map. This indicates which tool were required to create the map, as well as the order they were used it.




 



Figure 2: Map of Suitable Bear Habitat in Marquette County, WI generated using data flow model from figure 1.




Figure 3: Three scripts ran in Python.






Figure 4: Map output of the three python scripts within figure 3.



Sources

 

The data used in the lab were all downloaded from the Michigan Center for Geographic Information.  

• Landcover is from USGS NLCD and retrieved from:(http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html)
• DNR management units retrieved from:(http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.html)
• Streams data was retrieved from:(http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marque)tte.html)

 

 

　



Lab 2: Downloading Data

Introduction 

During this lab, I was introduced to downloading data sets from the interest, as well as how to import them into ArcMap. In this lab, I downloaded data sets and shapefile census boundaries off of the U.S. Census Bureau. With the downloaded data I: practiced bringing the data into excel and converting it into the appropriate format, joined the attribute table to the Census shapefile, and symbolized the census data to create two final maps.

Methods

I began the lab by visiting the U.S Census Bureau website to download the data. After I selected my perimeters of interest, I was able to select and download the Wisconsin Total Population Census Data from 2010. This provided me both the Census data and a the Census shapefile boundaries. From here I opened the Census data within excel to converted it to an Excel file to be able to use within ArcGIS. I opened up this file along with the Census shapefile in ArcGIS to begin the mapping process. After determining what attribute data column they shared, known as the common attribute, I was able to join their tables together. This allowed me to map both layers together, and symbolize the Census shapefile. These methods were completed again by downloading a new variable of my choice from the Wisconsin County level. I chose to map the percent of individuals between ages 20-24. I did this by repeating all of the previous steps. Lastly, these data frames were arranged into a map to be displayed.


Results

Figure 1: Maps of the U.S Census or Wisconsin. Left image displays the population per county in WI. Right image displays the percent population of people from ages 20-24 per county in WI.

The highest population is seen within the southwestern counties of the state, which include the large cities of Milwaukee, Madison, and Green Bay. The highest population of people between age 20-24 is within the Counties of Eau Claire and the western counties that following highway 94, as well as Madison County.

Sources:

The data used in this lab was downloaded from the U.S. Census Bureau.

• http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

Lab 1: Base Data

Goals and Background:

The Confluence Project is a private and publically funded project with goals to develop a 150,000 square foot community arts center that will be used for both fine arts performances and university students in Eau Claire, Wisconsin. Additionally, a separate building will include a large commercial retail center and an apartment complex that is available for university students to rent. Outdoors, a public plaza will be built that will include public bike paths. The project in total is expected to cost about $80 million and continues to receive support by state government officials and community voters. The community arts center is estimated to cost $50 million, and the mixed use privately owned center will cost about $28 million.

To make informed development decisions and to inform the public of the development plans, it is important to make the information about the proposed develop sites organized and readily available for any interested party. Here, all known information about the area surrounding the proposed site is organized into six maps.

Methods

Using ArcMaps, I created six maps to organize known the geographic and city census data regarding the proposed site of the Confluence Project and its surrounding area. Legends and scale bars were added to all maps. The legend bars allow the reader to identify the map objects, as well as interpret data values. The scale bar is measured in mile and gives the reader a sense of the space they are observing. All feature classes were laid over a satellite image of the proposed site with Way Claire, Wisconsin. The transparency of the added feature classes was increased in order to make the satellite base image visible to the reader. Also, the world imagery was used to locate the proposed plots of land for the Confluence Project to we able to digitize the plots of land. Digitizing allowed me to create polygon features to add into each of the six maps to draw attention to the proposed site. The descriptions and methods unique to each individual map are listed below.

1) Civil Divisions Map

To make this map, the World Imagery was brought into the data frame. To focus on our area of interest, the Civil Divisions feature class of Eau Claire County was added, followed by the digitized area of the proposed site. This map of civil divisions was created to show how the civil division types are divided within the area surrounding the proposed site. The different types of civil divisions are City, Town, and Village. In my map, we are able to see the proposed site is within the City municipality.


2) Census Boundaries Map

To make this map, the World Imagery was brought into the data frame, followed by the feature classes of BlockGroups and Tracts, and the digitized proposed site. The tracts background was removed to allow the reader to see how they are broken down into smaller block groups. These block groups represent the total population of the area normalized by the square mileage. The reader is able to gather an estimate of the normalized population of the area surrounding the proposed site because a gradient of color was used to symbolize the population within each block group.

3) PLSS Features Map

Here, the World Imagery was brought into the data frame, followed by the digitized proposed site and the PLSS quarter quarter feature class. The PLSS quarter quarter data frames shows how to township is divided for public land surveying, and most importantly which survey plot the proposed area lies within.

4) EC City Parcel Data

This map was created by first adding the World Imagery to the data frame followed by the cities parcel area data, the centerline data, and the digitized proposed site. The Eau Claire City parcel data shows the division of land parcels within this area in relation to the parcels of the proposed area. 

5) Zoning

For the zoning map, the World Imagery was added to the data frame along with the digitized proposed site, and zoning class data. Originally, there were 40 areas marked with their specific zone class. To organize the data in an effective way, the areas that shared a broader common zone type were grouped together. These broader zone classes are listed Commercial, Central Business District, Industry, Public Properties District, Residential, and Transportation. The reader can see that the proposed site is within a Central Business District.

6) Voting Districts

This map was created by adding the World Imagery, followed by the voting district class feature and the digitized proposed site. Each voting districts was labeled with its unique voting district number.

Results

Figure 1 below displays the Civil Divisions Map, Census Boundaries Map, PLSS Features Map, EC City Parcel Data Map, Zoning Map, and Voting Districts Map that I created during this map.

Figure 1: Six maps of the proposed Confluence Project site in Eau Claire, WI. Further description for each map is provided within the blog's Methods section above, under the headings that correspond to each map title.

Sources

 

I collected information from the following sites for this lab:

• UWEC. (2015) Frequently asked questions: The Confluence Project. Retrieved from https://www2.uwec.edu/News/more/confluenceprojectFAQs.htm.
• UWEC. (2015) Confluence Project Flowing Forward. Retrieve from http://www.uwec.edu/news/stories/confluence-project-flowing-forward_24942