Lab 3: Vector Analysis with ArcGIS

Background

The purpose of this lab was to determine the most suitable land to be used as a bear habitat within the study area in Marquette County, Michigan. While keeping in mind previous locations of bears, presence of streams, proximity to urban areas, and most suitable land type I was able to use data provided by the state of Michigan to select and remove land based on the given criteria.

·         Within at least 500 meters of a stream

·         Favorable land cover type

·         Areas of DNR managed land

·         At least 5 kilometers from an Urban or Built up land cover type

Goal

The goal for this lab is to become familiar with the different types of geoprocessing tools and be able to determine which ones to use to figure out what land is best suitable for a new bear habitat.

Methodology

Throughout this lab’s objectives, I used several different methods to narrow down the suitable land of a new bear habitat. You can see a simplified model of my methods in Figure 1.

Objective one:

During objective one, I was able to explore the data and the file types that were to be used in this lab.

                marquette_bear_study

                landcover

                bear_management

Within these data files, was an excel file specifying the XY coordinates of previous bear locations. Because the excel file is a non-spatial database, I first had to add the coordinates as an “event theme”. An “event theme” allows you to plot these XY coordinates spatially within ArcMap. Once added, I exported the locations to my lab 3 geodatabase.

Objective two:

After adding all of the data from the bear_management_area dataset, I created a unique value map of the land cover by “Minor Type”. I then wanted to determine which land cover type had the most bear locations. By intersecting the land cover and bear_location feature classes, I was able to generate a table that had both the id of the bears and the various types of land cover. For this objective, I focused on the “minor type” field and summarized the field to determine the count of bears in each type. I concluded that the top 3 habitat types were…

                  1. Mixed Forest Land (964)

                  2. Forest Wetland (644)

                  3. Evergreen Forest Land (576)

I then created a separate layer for these 3 land types and named it suitable_land_cover

Objective three:

After receiving information from biologists, I wanted to determine how man of the bears were found in close proximity of streams. To do so, I conducted a query by location and determined that nearly 72% of the bears were found within 500 meters of a stream. Because of this large percentage, I believed it to be important criteria to keep in mind when determining suitable bear habitat locations.  This importance led me to create a 500 meter buffer around all streams within the study area (stream_buff). I then dissolved the buffer to generalize the feature class and clean it up.

Objective four:

Based on the findings up to this point, I decided to intersect the stream buffer and the suitable land cover because of the important role they play in the location of bears. I dissolved the result of the intersection to combine the internal boundaries of the layer to simplify the data. 

Objective five:

Because the bear habitat must be on DNR managed land, I then chose to intersect the suitable land near streams (objective 4 outcome) and the DNR managed land. I also dissolved this output to create a more appealing layer. 

Objective six:

For this task, I was asked to manipulate the data further and select areas that are not near any urban or built up land. To do this, I used a query on the land cover layer and created a new layer of the urban and built up land. I then put a 5 kilometer buffer on the layer (then dissolved it again). With this 5 kilometer buffer, I was able to erase the areas that were within the designated area from the suitable land managed by the DNR leaving us with only areas away from urban land. 

Figure 1: Data Flow Model to find suitable land for bear habitat

Practice with Python:

To gain some practice using python coding, I did some of the previous geoprocessing tools by typing commands within the python window (Figure 2). I proceeded to do a buffer analysis, an intersect analysis, and an erase.

Figure 2: Python Coding Practice

Results

Figure 3: Final Results

 The final results from this lab are shown above (figure 3).This map shows the study area within the Marquette County boundary. It also includes a location map on the right showing where Marquette county is located in Michigan. The map also shows the bear locations, the locations of streams, the suitable land types near streams (objective 4), and the suitable land near streams on DNR managed land (objective 6). Most of the urban and built up areas we wanted to avoid in this selection are found in the southern region of the study area. Overall, all areas shown in the pink/salmon color would be perfect habitat for bears following the criteria. In my opinion, I would select the area in the north east of the study area because of the large area allowing the bears to roam and its numerous streams for the bears. 

Sources

"Michigan 1992 NLCD Shapefile by County." Michigan 1992 NLCD Shapefile by County. Accessed December 6, 2015. http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html.

"Michigan Geographic Framework: Marquette County." Michigan Geographic Framework: Marquette County. Accessed December 6, 2015. http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html.

"Wildlife_mgmt_units." Wildlife_mgmt_units. Accessed December 6, 2015. http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm.