UW-Eau Claire GIS I: Introduction to Geographic Information Systems: Lab 3: Determining Bear Habitat with Geoprocessing Tools and Python

Goal: The goal of this lab is to find suitable black bear habitat within a study area of Marquette County, Michigan using geoprocessing tools.

Background: The habitat is based on 4 criteria and geared towards DNR management purposes and thus takes several steps and tools to determine.

I first used a typical Data Flow Model Method to which there were several objectives:

1: Mapping XY Coordinates from an Excel File

2: Determining Forest Type in Which Bears were Found

3: Determining Legitimacy of Stream Proximity as a Suitable Habitat Criteria and Creating Feature Classes for the Criteria

4: Finding Suitable Bear Habitat Based on Two Criteria: Proximity to Streams and Forest Type

5: Finding Suitable Habitat Within the DNR Management Districts

6: Finding Suitable Bear Habitat Away From Urban Areas or Built Up Lands

Python was then used to find suitable bear habitat in a similar fashion to gain exposure to coding.

Objective 1: Mapping XY Coordinates from an Excel File

After previewing the bear locations data excel file, I noted that the data consisted of x, y coordinates and must be added to the map as an event theme. However, since the data does not have an ObjectID field, after the XY data was added to ArcMap, I needed to export the data and save it as a feature class on my personal lab 3 geodatabase.

Objective 2: Determining Forest Type in Which Bears were Found

To find the bear habitat, I generated a new feature class that has the ID number of the bear and the forest type in which it was found. To do this, I selected by location land cover types by bear locations. This layer contained only land cover areas that bears were recorded being within. I then joined the data from the land cover selection with the bear locations based on spatial location to get the bear and land cover feature classes on the same attribute table.

I found the top 3 habitat types (3 habitat types with the most bears found within the land cover type) by selecting by attributes for each of the 6 habitat types in the minor type field.

The amount of selected attributes equals the amount of individual bears in each habitat type.

Objective 3: Determining Legitimacy of Stream Proximity as a Suitable Habitat Criteria

Since it was indicated that bears may most likely be found close to streams, I tested this statement by determining if bears were, in fact, found close to the streams.

First, I selected by location bear locations within 500 meters of a stream with bear_locations (XY coordinates of individuals) as the target layer and streams as the source layer.

I then created a feature class that represents distance from streams. I used the clip tool to clip streams at the boundary of bear_cover. I then used the Buffer tool to create a feature class that contained a 500 meter radius surrounding all streams within the bear_cover feature class. I named this feature class within500m_stream_area.

Objective 4: Finding Suitable Bear Habitat Based on Two Criteria: Proximity to Streams and Forest Type

To find the suitable areas of habitat, I used both land cover types in which bears have been located (bear_cover) as well as areas 500 meters from streams (within500m_stream_area). Since land cover types in bear_cover had multiple polygons within their boundaries, I used the dissolve tool to remove the internal boundaries. I then intersected the two criterial feature classes to find areas in common. The resulting feature class is possible suitable habitat for bears.

Objective 5: Finding Suitable Habitat Within the DNR Management Districts

If the DNR is interested in taking steps to maintain this habitat, they must be able to know the areas which areas of suitable habitat lies within their management land. To find this, I first used the dissolve tool to eliminate the internal boundaries within DNR management lands. I then used the clip tool to find only the areas that DNR land management regions and suitable habitat have in common.

Objective 6: Finding Suitable Bear Habitat Away From Urban Areas or Built Up Lands

It may be of interest to the DNR to only include habitat at an appropriate distance from urban areas or built-up lands in their criteria for suitable bear habitat which they can manage. To do this, I first created a new layer by selecting by attributes “urban or built up land” from the land cover layer and used dissolve to remove the internal boundaries. I then used buffer to extend the boundary 5km from the actual urban or built up land cover. Finally, I used the erase tool to find the final bear management lands from the suitable bear habitat within DNR lands that were not included in the boundary of 5km from urban or built-up lands.

Using Python

I ran 3 functions in python to find suitable bear habitat. The first function created a buffer of 1 kilometer from streams. The second intersected the stream buffer and suitable habitat found previously in the lab. The final function performed an erase analysis on the suitable habitat and the buffer of 5 kilometers radius around urban areas found previously.

Results:

The results suggest that most bears prefer forest wetlands, mixed forests, and evergreen forests over all other forest types as these forest types had the highest amount of individuals within their boundaries.

Forested Wetlands: 9 bears

Mixed Forest Land: 9 bears

Evergreen Forest Land: 5 bears

It was also found that streams play an important role in suitable black bear habitat as 72% of all bears in the sample were found within close proximity of streams. Distributions of bears was observed to be mostly within the northern regions of Marquette County, away from urban areas which indicates this may be another important criteria of bear habitat and should be considered when determining suitable habitat within the county. Therefore, the final map of suitable black bear habitat in Marquette County, MI includes 4 criteria: 1) land cover (forest) types in which bears have been located, 2) areas within 500 meters of streams, 3) regions at least 5 kilometers away from urban areas or built up lands, and 4) areas within DNR management lands.

Figures: