Geographic Information Systems : GIS 1: Lab Three

Background:

The goal of this lab was to develop a suitable habitat for bears present in the research area of Marquette County, Michigan using geoprocessing tools aimed at vector analysis in ArcGIS to then create a digital data flow model and a cartographically pleasing map showcasing the new suitable habitat. The purpose of this lab was to use GPS points (X,Y coordinates) of bear location from an excel file and determine the area most suitable for the bear habitat based on certain criteria.

Methods:

The first objective was to map the GPS points of X and Y coordinates found in an excel file. These points represented black bear locations in central Marquette County, Michigan. In order to map X and Y coordinates that are in a non-spatial database (ex: excel) you need to add the coordinates as an “event theme”. An “event theme” is a temporary display of X, Y data in ArcMap, but they have certain limitations because an “objectID” field is not officially generated in an “event theme”. Without an “objectID” field you cannot select features in the map layer, edit layer attributes, perform any interactive edits such as selecting points and moving them, or define a relate. X, Y coordinates describes points on the earth’s surface, the fields must be numeric; once you add data to your map it becomes an X,Y event layer and acts like other point feature layers.

To add the X,Y event theme, I simply added X,Y data under add data from the file drop down bar, using a projected coordinate system called NAD_1983_HARN_Michigan_GeoRef (Meters), indicating my X-Field as my X-points collected, and my Y-Field as my Y-points collected. Then, I exported my points into my geodatabase as a feature class to use for further mapping.

Following this process, I needed to determine the habitat that the bears were inhabiting by creating a new feature class with the bear location and the land cover type where it was found. I joined the feature class of bear location points and the type of land cover, based on the ObjectID field. It was a simple spatial join. Then, to determine the top three habitat types that the bears inhabited, I summarized the bear location by the land cover type in that location. The top three habitats the bears were found in were mixed forest land, evergreen forest land, and forested wetlands.

After determining the top bear habitat lands, I needed to find out how many bears were found near, within 500 meters of a stream, when the GPS point was collected. In order to do this, I created a buffer of 500 meters around all of the streams, then under data management and generalization, I used the dissolve tool to blend the layers. After, I used the select by location to determine the percentage of bears found within 500 meters of a stream; it was 92.7%. Sixty-three out of sixty-eight bears were found within 500 meters of a stream. Biologists consider a percentage above thirty to be important criteria.

Now to find suitable areas for bear habitats, I used the two criteria assessed earlier. I needed to find all of the locations with the top three land covers and within 500 meters of a stream. To do this, I needed to separate the land cover types by using the select by attributes to select the top three land covers and create a new feature class with only those types. Then, I used the intersect tool to combine the top three land cover areas and the stream buffer of 500 meters to create a new layer that would be considered suitable habitat areas. Because I combined two layers, I needed to remove the internal boundaries to create one layer by using the dissolve tool.

Once I established all of the suitable areas for a bear habitat based on land cover and location to a stream, I needed to find the suitable areas under DNR land management. To do this, I performed an overlay analysis intersecting the DNR managed lands and the suitable locations, removing the internal boundaries to create one new layer containing areas of suitable habitat within the DNR management areas.

Lastly, I wanted to create suitable bear habitats away from urban or built-up areas, so I created a 5 kilometer buffer around urban and built-up areas and used it to eliminate all of the areas that were DNR managed lands and suitable habitats within that area.

During the lab, I recorded my steps to create a digital data flow model.

Digital data flow model of methods performed during this lab.

Python code to adjust streams buffer and the urban areas buffer.

Results:

During objective two, it was determined that 92.7% of black bears were found within 500 meters of stream. Sixty-three out of sixty-eight bears were found within 500 meters of a stream. Biologists consider a percentage above thirty to be important criteria. The pink colors on the map show areas that are suitable for bear habitats under two criteria: land cover type and accessibility to streams (within 500 meters).The dark pink areas of the map are the final products for suitable habitat area because they are more than five kilometers from urban or built-up areas, are DNR managed lands, and contain both criteria. The light pink areas represent suitable habitat that is within five kilometers of urban or built-up areas, are DNR managed lands, and contain the two criteria.

Figures:

The map represents suitable habitat areas for black bears in Marquette County, MI. The pink colors show areas that are suitable for bear habitats under two criteria: land cover type and accessibility to streams. The dark pink areas are the final products of suitable habitat area because they are more than five kilometers from urban or built-up areas. The light pink areas represent suitable habitat that is within five kilometers of urban or built-up areas.