GIS I Lab 4: Using Overlay Functions to Find Suitable Bear Habitat in Marquette County Michigan

Goal:

The purpose of this Lab is to apply the spatial vector tools learned over the recent weeks. These tools will be used in finding a suitable habitat model for a Michigan county bear population.

Background:

Like any animal, bear have a niche. As we discovered throughout the lab, this niche was in tree cover and within a close proximity to a water source. The DNR wanted to create a habitat zone for bear in this niche, but they could only create habitat zones in land they already managed. So a habitat model for bear within the DNR's land management zone must be created.

Methods:

This lab was once again centered around the ArcGIS program, specifically ArcMap. Inside of ArcMap a variety of tools were used to create a suitable habitat range and narrow down the criteria to the specified overlap zones. The new tools applied to this lab were intersect, clip, erase, buffer, and dissolve.
These tools allow for more complex queries and specified areas of interest. The 1st tool intersect, takes all the overlapping points, lines, or polygon regions from the input features and creates a new feature class. The next tool, clip, cuts an input feature in the shape of clipping polygon. The output feature class has all the same data of the original input feature, but shapes and values inside of the shape of the clipping polygon used. The tool erase works similar to the clipping tool, but instead only keeps the data on the outside of the erase polygon. One of the more useful tools, is the buffer tool. This tool takes an input feature and creates a polygon around the original point, line, or polygon with a user designated distance between them, or a "buffer." Finally, the dissolve tool is used to erase or "dissolve" the internal boundaries. This would be used if nearby points had overlapping regions and you only wanted the union of the two buffer shapes, not the specific circles for each buffer region.

The data flow model was created using Microsoft Visio.

Results:

The bear population lived in a relatively specific niche. There were 68 bears in Marquette County, Michigan. Of the 68 bears, 62 of them lived in three varieties of forest cover: mixed forest land (31), forested wetlands (17), and evergreen forest land (14). Also noteworthy, 72% of all the bears were found within 500 meters of a stream. This data, in combination with the DNR management zones, was used to create a bear management model as seen in Figure 1.





Figure 1. Marquette County Michigan DNR Management Model.

 

Figure 2. Data Flow Model for the DNR management model.

 

Data Sources From Michigan Geographic Data Library:

Landcover:
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

DNR Management Areas:
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

Streams:
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

GIS I Lab 3: Introduction to GPS

Introduction:

 

The purpose of this lab was to familiarize ourselves with the general process of collecting data with a GPS (create a personal geodatabase, collect data in that geodatabase, then making a map with the data).

 

Methods:

 

For data collection, a Juno 3B GPS unit was used. Prior to collection, a geodatabase was created in ArcCatalogue. In ArcMap: points, lines, and polygon feature classes were created. A raster image and campus buildings shapefile (shown as brown polygons in Figure 1) were also brought into ArcMap. Next the data was exported as an apo. file to the Juno unit and ready for data collection.

 

Results:

 

As Figure 1. shows the data collected wasn't very accurate. Although most of the time my PDOP value was under 2.0, my data still wasn't very accurate. The Juno unit should have given me 2-5m accuracy. However, there were many multi-path errors in the form of buildings and trees. The main source of error probably came from the atmosphere in the form of overcast skies. There was a mixture of rain and snow, so I imagine the error from the Troposphere was higher than usual. In conclusion I wouldn't recommend mapping out sidewalks with a GPS, digitizing would probably work better.

 



 

Figure 1. Collecting points, lines, and polygons on the lower campus mall at UW- Eau Claire.

Problems/Solutions:
I had two hiccups during this exercise. The first came when my project file wasn't loading on the Juno unit. I think the issue revolved around my data dictionary. I ended up checking out a new unit and going through the creation of my dictionary again and re-deployed the data successfully to my new Juno unit.

The second problem came when trying to "check-in" the data to the mxd. would show the data collected. Apparently when trying to remove some of the clutter in my folder I deleted the old data for the first Juno unit. In the process I deleted the geodatabase for my points, lines, and polygons. To fix this I created a new geodatabase and brought the data back into that geodatabase.