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| The map to the left shows a comparison of suitable habitat for bear and the ideal suitable habitat for bear in Marquette County, Michigan. The ideal habitat falls within pre-existing Michigan Department of Natural Resources land management zones. A small locator map in above the map on the left indicates the location of the study area, within Marquette County, in the state of Michigan. The map on the right shows the land use for areas within the study area. |
Goal
Background: The goal of this assignment was to create a map based on suitable habitat for bear in Marquette County, Michigan.
Purpose: The purpose of the assignment was to gain a better understanding of how to utilize and analyze data in order to properly show how the data relates.
Methods
Step One: In order to begin gathering the necessary data for this lab, a file geodatabase was created for storing feature class data. A Microsoft Excel table, containing XY coordinates for bears being monitored through radio tracking, was converted to be used in ArcGIS. This was done by adding the coordinates as an "event theme" in ArcMap, selecting "File" -> "Add Data" -> "Add XY Data" inputting the correct data, and exporting the final product to the file geodatabase. These points were then able to be saved as a feature class that provided the XY coordinates of all bears at the time of data collection.
Step Two: The next task was to prepare the data for analysis by running various tools to narrow down the search area for ideal bear habitat. The XY coordinates were spatially joined with a feature class showing landcover, in order to create a new feature class showing what landcover types bears were most likely to be found in. The results were then summarized to get a better, quantitative understanding of where bears were found.
Information was provided that stated that most bears seemed to be located near streams in order to have easy access to food and water. The stream feature class was selected and the boundaries surrounding the various stream segments were dissolved in order to create a single stream line. A buffer was created to encompass an area of 500 meters around the stream. Out of the sixty-eight bear locations recorded, forty-nine of the recorded locations were within the buffer created by the stream.
Both the landcover-bear spatial join and the stream buffer were intersected, showing the bear locations and landcover types within the boundaries of the stream buffer.
Step Three: With the newly acquired stream buffer showing bear locations and landcover, the next task was to use the acquired parameters to assess the best suitable habitat for bear management zones. This was achieved by first performing an attribute query to select the three most prominent landcover types that were inhabited by bears, determined by referencing the summarized table from the prior step. The newly clipped feature class was then dissolved so there were now unnecessary boundaries within the feature class. This selection was made into a feature class and the data was clipped from the stream buffer, to show the top three suitable habitats within 500 meters of a stream, where bears were present.
Step Four: After acquiring the data for the best suitable bear habitat within 500 meters of a stream, the next task was to compare this to current Michigan Department of Natural Resources management land, to determine the easiest place to expand upon a bear management program. The suitable habitat-stream buffer was intersected with the Michigan DNR lands in order to show all DNR land that fell within suitable lands within 500 meters of a stream. The resulting feature class was dissolved so there were no unnecessary boundaries.
Step Five: The final task was buffer urban areas so that potential bear management areas would not be found too close to urbanized areas. An attribute query was performed to isolate areas noted as "Urban" or "Built Up Land" and this was made into a feature class. Following this, the feature class was dissolved and a 5 kilometer buffer was applied to the feature to exclude areas within 5 kilometers of any urban areas. The buffer was then compared to the suitable lands within DNR lands within 500 meters of a stream. An erase tool was used in order to erase any data that fell within the urban buffer. This excluded all unwanted information and all that was left were the ideal areas for bear habitats, as noted in the left map, above.
Step Six: A proper map was created and a data flow model was also created to show workflow for the project.
Step Two: The next task was to prepare the data for analysis by running various tools to narrow down the search area for ideal bear habitat. The XY coordinates were spatially joined with a feature class showing landcover, in order to create a new feature class showing what landcover types bears were most likely to be found in. The results were then summarized to get a better, quantitative understanding of where bears were found.
Information was provided that stated that most bears seemed to be located near streams in order to have easy access to food and water. The stream feature class was selected and the boundaries surrounding the various stream segments were dissolved in order to create a single stream line. A buffer was created to encompass an area of 500 meters around the stream. Out of the sixty-eight bear locations recorded, forty-nine of the recorded locations were within the buffer created by the stream.
Both the landcover-bear spatial join and the stream buffer were intersected, showing the bear locations and landcover types within the boundaries of the stream buffer.
Step Three: With the newly acquired stream buffer showing bear locations and landcover, the next task was to use the acquired parameters to assess the best suitable habitat for bear management zones. This was achieved by first performing an attribute query to select the three most prominent landcover types that were inhabited by bears, determined by referencing the summarized table from the prior step. The newly clipped feature class was then dissolved so there were now unnecessary boundaries within the feature class. This selection was made into a feature class and the data was clipped from the stream buffer, to show the top three suitable habitats within 500 meters of a stream, where bears were present.
Step Four: After acquiring the data for the best suitable bear habitat within 500 meters of a stream, the next task was to compare this to current Michigan Department of Natural Resources management land, to determine the easiest place to expand upon a bear management program. The suitable habitat-stream buffer was intersected with the Michigan DNR lands in order to show all DNR land that fell within suitable lands within 500 meters of a stream. The resulting feature class was dissolved so there were no unnecessary boundaries.
Step Five: The final task was buffer urban areas so that potential bear management areas would not be found too close to urbanized areas. An attribute query was performed to isolate areas noted as "Urban" or "Built Up Land" and this was made into a feature class. Following this, the feature class was dissolved and a 5 kilometer buffer was applied to the feature to exclude areas within 5 kilometers of any urban areas. The buffer was then compared to the suitable lands within DNR lands within 500 meters of a stream. An erase tool was used in order to erase any data that fell within the urban buffer. This excluded all unwanted information and all that was left were the ideal areas for bear habitats, as noted in the left map, above.
Step Six: A proper map was created and a data flow model was also created to show workflow for the project.
Results
This assignment provided an opportunity to apply GIS to real world situations and queries. This was a particularly interesting assignment, as biogeography is a field that I am very interested in. Querying suitable bear habitats is a very applicable issue to work with. Many of the methods learned from this lab will be used to discern suitable hunting areas in the final project for this course.
Michigan Geographic Data Library
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