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Jim Kompanek
Introduction:
Habit loss from development is a leading factor in pushing many species to the brink of extinction. According to King (2006), there are over 1,400 species considered either threatened or endangered in the United States. The Centre County (Figure 1), Pennsylvania government has proposed the designation of land as a biological reserve system within the county. Six environmental, geographical, and cultural factors were analyzed to identify the proposed priority conservation areas in Centre County. A summary of files provided for this project is directly taken from (King 2006) and is provided in the Appendix section of this report.
Prerequisites for Priority Conservation Areas:
- Greater than 75 bird and mammalian species combined;
- Less than 10 percent of each study site occupied by buffered roads, highways and interstates;
- High habitat potential;
- Publicly owned land;
- Forested areas;
- Slope less than 15 percent.
Figure 1. Topographic map of Centre County, Pennsylvania indicating relative elevation.
Identification of Species Rich study blocks:
The first step in the identification of species rich study blocks involved performing a join of the speciesrich attribute table and studysites polygon based on the Block_ID field. The studysites polygon divides the county into a series of square study cells of roughly equal area (Figure 2) and the speciesrich attribute table contained species information based on these study blocks. After the join was conducted, a new field (Total_Spec) was created in the resulting table. This field was populated with the Calculate Values command and "Birds + Mammals" code. As defined by King (2006), a species rich study block contains more than 75 total bird and mammal species. These were identified in the Total_Spec field with the Select by Attribute command ("studysites.Total_Spec > 75"). The final step was to Export Data to speciesrich.shp shapefile. Species rich study cells are presented in Figure 3.
Figure 2. Topographic map of Centre County, Pennsylvania indicating relative elevation, roadways, and study cells.
Figure 3. Topographic map of Centre County, Pennsylvania indicating relative elevation, roadways, and study cells with more than 75 total bird and mammalian species.
Less than 10 percent roads and buffers:
The first step to identify study blocks with minimal roads and associated buffers was to look only at the cells identified in the previous step. This was performed with the intersect tool in ArcToolbox (ArcToolbox à Analysis Tools à Overlay à Intersect) and exported to the Pot_Roads file. To determine buffer size based on road type, a new field (Buff_siz) was created in the attribute table. The Select by Attribute tool (“rd_type” = ‘roads’) was used to select all roads in the attribute table. The Calculate command was then used (Buff_siz = 20) to populate all roads with a buffer size of 20. The same basic process was used to determine buffers for interstates and highways.
The buffer wizard was then used to create buffers for the Pot_Roads file. Because the initial step involved the intersect tool to identify just those roads in the species rich cells, this greatly reduced the amount of time needed in the buffer step. The results of this buffer were then exported to Buffer_Roads.shp (Figure 4). To determine the amount of area taken up by roads and their buffers, it was necessary to identify areas outside of buffers. To do this, ArcToolbox was used to conduct a union of the Buffer_Roads.shp file. The attribute table was then opened and the Select by Attribute command was used to identify cells with BufferDist = 0. This was a simple work around, since the BufferDist attribute for anywhere within roads or buffers would have been populated earlier on. This was then exported into a new shape file (Data Export Data à Inverse_buffer.shp) (Figure 5).
To determine the percentage of each cell taken up by roads, the first step was to open the Inverse_buffer attribute table. A new field named New_Area was created and the following code was used to calculate area outside of the road buffer:
Dim dblArea as double
Dim pArea as IArea
Set pArea = [shape]
dblArea = pArea.areaTo determine the area comprised within the road buffers, the Calculate command was used (Area – New_Area) to populate a new field named Dif_Area. To determine the percentage of each block comprised of roads or buffers, a new field was created in the attribute table (Percent_Rd). This field was populated with the Calculate command (Dif_Area/Area). To identify those cells with less than 10 percent roads, Select by Attribute was used in the attribute table in the Percent_Rd field ("Percent_Rd" <0.1) and exported to RC_Road_Percent.shp. A new field was added to the attribute table named Suitable and all suitable locations were populated with a "1" for easier analysis (Figure 6).
Figure 4. Topographic map of Centre County, Pennsylvania indicating road buffers within "species rich" study cells.
Figure 5. Map of Centre County, Pennsylvania indicating areas outside of road buffers within "species rich" study cells.
Figure 6. Topographic map of Centre County, Pennsylvania indicating relative elevation, roadways, and study cells with more than 75 total bird and mammalian species and less than 10 percent of total area comprised of roadways.
Spatial Analyst to determine habitat potential, ownership, forestation, and slope:
Spatial analyst was used to determine habitat potential, ownership, forestation, and slope. A cell size of 50 was used throughout these steps because it was the largest grid size used with the data provided. Although a 30 m grid was used in the elevation file, using a 30 m grid with spatial analyst would imply a degree of precision not possible.
The first step to identify areas with high habitat potential was to use the Features to Raster tool in Spatial Analyst (Spatial Analyst à Convert à Features to Raster). SP_habitat was entered as the output. Spatial Analyst was then used to reclassify SP_habitat, with High reclassified as "1" and Low reclassified as "0". The output was exported to RC_Habitat (Figure 7).
The same basic procedure was used to determine areas of publicly owned land. Spatial Analyst à Convert à Features to Raster was used to convert the ownership polygon to a raster file (SP_Ownership). Spatial Analyst was then used to Reclassify Public as "1" and Private as "0" (Figure 8).
To determine areas identified as forested, the procedure was slightly different. The landuse file was already a grid file, therefore the Features to Raster tool was unnecessary. The Reclassify tool was used to reclassify the S_Value field and "1" was entered for forests and "0" for all other fields, and output to RC_landuse (Figure 9).
Spatial Analyst was again used to determine areas with slope of less than 15 percent (Spatial Analyst à Surface Analyst à Slope). The following information was entered to generate a raster map of elevation in Centre County:
Input: elevation
Output: percent
Z factor: 1
Output: 50
Output raster: RC_SlopeSpatial Analyst was used to Reclassify the RC_Slope file. Classify was selected and "2" was entered for Classes and break values were entered at 15. Reclassify was again used and the Old Value of 0 - 15 was replaced with "1" and the 15 + was replaced with "0". This was then output to RC_Slope_1 (Figure 10).
Figure 7. Topographic map of Centre County, Pennsylvania indicating areas of low and high habitat potential.
Figure 8. Topographic map of Centre County, Pennsylvania indicating areas of public and private ownership.
Figure 9. Topographic map of Centre County, Pennsylvania indicating forested areas
Figure 10. Topographic map of Centre County, Pennsylvania indicating areas of less than and greater than 15 percent slope.
Conclusion:
The final step to identify priority conservation areas in Centre County was to "combine" all of the data previously created. This was accomplished with Raster Calculator (Spatial Analyst à Raster Caluclator). With Raster Calculator, [RC_Habitat] * [RC_landuse] * [RC_ownership] * [RC_slope_1] were multiplied and output to RC_Potential. Because "1" was considered suitable and "0" unsuitable, any combination of cells with a "0" would be considered unsuitable and only a combination comprised entirely of "1"s would result in suitable areas. The RC_Road_Percent file created in an earlier step was then converted to a raster file and multiplied by RC_Potential. The final step was to make the output permanent and named RC_FINAL and the value of "0" was given a null value. Figure 11 displays the priority conservation areas identified within Centre County, Pennsylvania.
Figure 11. Topographic map of Centre County, Pennsylvania indicating priority conservation areas.
References Cited:
King, Beth
