UW-Eau Claire GIS I: Introduction to Geographic Information Systems: May 2015

Introduction: Pigeon Lake—a biology field station—used to be an integral part of the University of Wisconsin-Eau Claire’s foundations of biology course as well as an educational retreat for other schools within the Eau Claire and Chippewa County. However, it recently closed. The purpose of this project is to find suitable land for a new biological field station in which 5 criteria are ideally met: 1) within forested area, 2) an appropriate distance from major roads, 3) nearby inland still waters (lakes, swamps, ponds, etc…), and 4) nearby moving waters (rivers and streams). These criteria are used in hopes they will provide the potential area for a new biology field station with diverse ecosystems to study and a greatly diverse resident organism population.

Data Sources: I originally gathered all necessary data from the UWEC database connection to Wisconsin DNR 2014 data and ESRI US Census data. This proved to have problems fitting together once projected. However, once I found feature classes within the Wisconsin DNR 2014 database to replace the ESRI data, the feature classes were able to line up properly. The earliest feature class I obtained from here was updated in 2011, so all feature classes are fairly recent. All metadata describes feature class attributes as the proper criteria needed for this project. I have no more remaining concerns surrounding the data.

Methods: Using model builder, I first projected all data layers to the NAD 1983 (2011) Zone 15N projected coordinate system (Figure 1). I then clipped all data layers by the study area layer (Eau Claire and Chippewa counties). I created a 1km buffer surrounding major roads and erased county forests within 1km of major roads. I then made a 3km buffer surrounding streams and a 5km buffer surrounding water bodies. All buffers were set to dissolve all internal boundaries. Finally, I intersected the county forest areas away from major roads, the streams buffer, and the water bodies buffer to create the data layer that contains suitable lands for a new biological field station.

Figure 1. The final model builder displaying the overall process of the project. This model builder was used to create the final map.

Results: The results yielded appropriate locations for a new biological field station if interest and budget allows for action (Figure 2).The locations fit all 4 criteria that helps to support biodiversity and diversity of habitats for educational purposes.

Figure 2. Final map displaying possible locations for a new biological field station in Chippewa and Eau Claire County.

Evaluation: This project contained very easy concepts and tools while also giving valuable information in the output. If I needed to do another project like this, I would not hesitate to agree to it. However, I did face some challenges during the process. Like I said previously, there was a problem with the original data from two separate sources that would not fit together once projected. There was also a technical issue of the model builder freezing up on the last intersect tool. However, that was mitigated by dissolving boundaries between feature classes created in previous steps. Eventually, the process ran correctly and the result was obtained.

Sources: Wisconsin DNR 2014 Database

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.

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