GIS 1 Lab 3
Goals and Background: The goal of this lab was to use multiple tools for vector analysis to determine a suitable habitat for bears in Marquette County, Michigan. The DNR wanted to know which areas within their management area in the county would be suitable for bear habitat.
Methods:
Objective one: The first objective was to create a feature class of the locations of the bears in the county. The GPS locations of these bears were created by making a feature class in ArcCatalog, originating from the XY table of coordinates. This class was then imported onto ArcMap, and after importing the correct projection parameters, a correct feature class of bear locations was made.
Objective two: The second objective was to find the top three habitat types for bears. In order to do this, I had to find the number of bears in each type of environment in the county. The first thing to do was a spatial join between the points of bear locations and the landcover feature. Next, I summarized this table, which gave me the number of bears in each habitat. The top three habitats for bears I came up with are: Mixed Forest Land, Forested Wetlands, and Evergreen Forestland. I created a feature for these three habitats, called suitable land.
Objective three: The purpose for this objective was to find out if it was important for bears to be near streams or not. If this is an important feature to bear habitats, it must be involved in the data to find suitable habitats in the DNR management areas. This was done by looking at the percentage of bears within 500 meters of streams. To do this, I buffered the stream feature class to 500 meters. Next, I used the select by location tool to find the number of bears within 500 meters of streams. I found that 72% of bears were within the 500 meters, so the data would be extremely important to account for in finding suitable habitats for the DNR.
Objective four: This objective's purpose was to create physical polygons showing the suitable habitats for bears within the county, including the best forestry and within 500 meters of a stream. To do this, I first created a feature class from the suitable lands found in objective two. I did a query of the bears per landcover to find the three top habitats, and created a layer from the selected features. Next, I intersected the buffer feature of 500 m streams and the suitable lands feature. This gave me a feature class (Suitable Habitat) that contains the top 3 land cover types while still being within 500 meters from a stream. Since there was overlapping in these areas, I dissolved the feature and got the dissolved suitable habitat feature.
Objective five: Here I had to locate suitable habitats that were within the DNR management lands. The DNR area, however, gave me information for the whole state, not just the county. To get rid of this, I clipped the DNR management and the study area to get a feature class of the DNR management study area. I then dissolved the feature to get rid of unwanted internal lines. Next, I intersected the dissolved DNR study area with the dissolved suitable habitat. This gave me areas of suitable bear habitat within the DNR management areas (DNR habitat on flow chart).
Objective six: The DNR was satisfied with the data, but did not want bear habitats within 5 kilometers of urban or built up areas. First, I had to locate the urban land. I did this by doing a query within the landcover feature, and created a feature from the urban or built up land type. Next, I buffered this feature class to 5 kilometers, as requested. I then did an erase with the DNR habitat and Urban 5 km feature, creating the final area of suitable DNR habitats. These suitable DNR habitats are in the top 3 landcover types, within 500 meters of a stream, within the DNR management lands, and at least 5 kilometers away from urban or built up areas. I have created a basic flow model representing the methods I used and the order in which I used them below.
Objective seven: To make a cartographically pleasing map, I chose a simple layout with a complimentary color scheme. I added a north arrow, legend, scale bar, title, and source to make the map presentable. The data flow model was created on Word, inserting the appropriate tools and feature classes with the approximate direction of flow that I worked in.
Objective eight: The purpose of this objective was to provide a basic introduction to python using the ArcGIS Python window. To do this, I opened the Python window in ArcGIS and typed in the simple commands provided to me, performing geoprocessing operations.
The Results: Below is a map of my results and a data flow model showing the tools I used to create the map, as well as the results of the introduction to python.
Commands in Python window:
>>> import arcpy
>>> arcpy.Buffer_analysis("Streams", "streams_buf2", "1 kilometer","FULL", "ROUND", "ALL")
<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\streams_buf2'>
>>> arcpy.Intersect_analysis(["streams_buf2","suitable_land"], "land_stream")
<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\land_stream'>
Sources:
All of the data were downloaded from the State of Michigan Open GIS Data:
http://gis.michigan.opendata.arcgis.com/
Landcover is from USGS NLCD:
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html
DNR management units:
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm
Streams from:
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html
Methods:
Objective one: The first objective was to create a feature class of the locations of the bears in the county. The GPS locations of these bears were created by making a feature class in ArcCatalog, originating from the XY table of coordinates. This class was then imported onto ArcMap, and after importing the correct projection parameters, a correct feature class of bear locations was made.
Objective two: The second objective was to find the top three habitat types for bears. In order to do this, I had to find the number of bears in each type of environment in the county. The first thing to do was a spatial join between the points of bear locations and the landcover feature. Next, I summarized this table, which gave me the number of bears in each habitat. The top three habitats for bears I came up with are: Mixed Forest Land, Forested Wetlands, and Evergreen Forestland. I created a feature for these three habitats, called suitable land.
Objective three: The purpose for this objective was to find out if it was important for bears to be near streams or not. If this is an important feature to bear habitats, it must be involved in the data to find suitable habitats in the DNR management areas. This was done by looking at the percentage of bears within 500 meters of streams. To do this, I buffered the stream feature class to 500 meters. Next, I used the select by location tool to find the number of bears within 500 meters of streams. I found that 72% of bears were within the 500 meters, so the data would be extremely important to account for in finding suitable habitats for the DNR.
Objective four: This objective's purpose was to create physical polygons showing the suitable habitats for bears within the county, including the best forestry and within 500 meters of a stream. To do this, I first created a feature class from the suitable lands found in objective two. I did a query of the bears per landcover to find the three top habitats, and created a layer from the selected features. Next, I intersected the buffer feature of 500 m streams and the suitable lands feature. This gave me a feature class (Suitable Habitat) that contains the top 3 land cover types while still being within 500 meters from a stream. Since there was overlapping in these areas, I dissolved the feature and got the dissolved suitable habitat feature.
Objective five: Here I had to locate suitable habitats that were within the DNR management lands. The DNR area, however, gave me information for the whole state, not just the county. To get rid of this, I clipped the DNR management and the study area to get a feature class of the DNR management study area. I then dissolved the feature to get rid of unwanted internal lines. Next, I intersected the dissolved DNR study area with the dissolved suitable habitat. This gave me areas of suitable bear habitat within the DNR management areas (DNR habitat on flow chart).
Objective six: The DNR was satisfied with the data, but did not want bear habitats within 5 kilometers of urban or built up areas. First, I had to locate the urban land. I did this by doing a query within the landcover feature, and created a feature from the urban or built up land type. Next, I buffered this feature class to 5 kilometers, as requested. I then did an erase with the DNR habitat and Urban 5 km feature, creating the final area of suitable DNR habitats. These suitable DNR habitats are in the top 3 landcover types, within 500 meters of a stream, within the DNR management lands, and at least 5 kilometers away from urban or built up areas. I have created a basic flow model representing the methods I used and the order in which I used them below.
Objective seven: To make a cartographically pleasing map, I chose a simple layout with a complimentary color scheme. I added a north arrow, legend, scale bar, title, and source to make the map presentable. The data flow model was created on Word, inserting the appropriate tools and feature classes with the approximate direction of flow that I worked in.
Objective eight: The purpose of this objective was to provide a basic introduction to python using the ArcGIS Python window. To do this, I opened the Python window in ArcGIS and typed in the simple commands provided to me, performing geoprocessing operations.
The Results: Below is a map of my results and a data flow model showing the tools I used to create the map, as well as the results of the introduction to python.
Commands in Python window:
>>> import arcpy
>>> arcpy.Buffer_analysis("Streams", "streams_buf2", "1 kilometer","FULL", "ROUND", "ALL")
<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\streams_buf2'>
>>> arcpy.Intersect_analysis(["streams_buf2","suitable_land"], "land_stream")
<Result 'H:\\Documents\\ArcGIS\\Default.gdb\\land_stream'>
Sources:
All of the data were downloaded from the State of Michigan Open GIS Data:
http://gis.michigan.opendata.arcgis.com/
Landcover is from USGS NLCD:
http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html
DNR management units:
http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm
Streams from:
http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html
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