Lab 3: Vector Analysis with ArcGIS

Goals

The purpose of this lab was to apply the knowledge and skills learned in previous weeks about spatial relationships and manipulating data using spatial and non-spatial tools to locate suitable habitats for bears in central Marquette County, Michigan. Learning how to create data flow models and use python code were also important parts of this lab.

Methods

Objective 1: Map a GPS MS Excel file of black bear locations in the study area. To complete this objective, I imported the bear locations data using the "Add XY Data" feature in ArcMap (Figure 1) and selected the same coordinate system as the other spatial data. I then exported the data into my geodatabase so I could bring it into the map as a feature class.

Figure 1. Add XY Data in ArcMap.

Objective 2: Determine the forest types where black bears are found in the study area based on GPS locations of black bears. I spatially joined the bearlocations and landcover feature classes to produce the bear_cover feature class containing information on the type of land the bears were located on. I then used this new feature class to summarize the minor_type field and created the bear_coversum table containing the number of bears located on each type of land cover (Figure 2).

Figure 2. Table showing the top three types of land cover bears were located on.

Objective 3: Determine if bears are found near streams. First, I created a 500 meter buffer around the streams feature class to produce streambuff. I then used select by location to determine which bears were found within the buffer (Figure 3).

Figure 3. Map of the study area showing a 500 m buffer around each stream and the bears within the buffer (highlighted points).

Objective 4: Find suitable bear habitat based on suitable land cover and distance from a stream. First, I queried landcover for suitable habitats (from objective 2), which were mixed forest land, forested wetlands, and evergreen forest land and made a new layer called suitable_cover from that selection. I intersected streambuff and suitable_cover and dissolved the interior boundaries to form bearhabitat_diss, which displayed the suitable habitat locations for the bears.

Objective 5: Find all areas of suitable bear habitat within areas managed by the Michigan DNR. First, I needed to clip the dnr_mgmt feature class so I only had the data within the study area. I then intersected the new feature class dnrstudyarea with bearhabitat_diss and dissolved the interior boundaries to produce the dnrbearhab_diss showing the bear habitats within the DNR's management areas.

Objective 6: Eliminate areas near urban or built up lands. I queried the landcover's major_type field for urban or built up lands and created a new layer from that selection and created a 5 kilometer buffer, producing urbanbuff (Figure 4). I erased the dnrbearhab_diss areas located within the buffer and produced bear_mgmtareas, which showcased bear habitats that were within the DNR's management areas and at least 5 km away from urban or built up lands.

Figure 4. Map of the study area showing a 5 km buffer around the built up and urban land covers.

Results

Objective 7: Generate a digital data flow model of the workflow and cartographic output. Figure 5 below presents the final map results showcasing the bear locations and suitable bear habitats both within and outside of the DNR's areas of management. Included is a map of the state of Michigan to indicate where Marquette County and the study area are located. My data flow model is also included within this figure.