Lesson 9 and 10: Final Project

by Diana Jo Lau

Introduction

Acme Conservation Unlimited conducted a site selection analysis of conservation areas meeting specific

criteria. The results of our analysis will be displayed in a map depicting the candidate reserve areas

within the county.

Project Criteria

Priority conservation areas should fulfill the following criteria:

Greater than 70 bird and mammalian species combined Less than 10% of each study area occupied by buffered roads, highways, and interstates High habitat potential Publicly owned land Forested areas Slope less than 10%

Workflow

The following files were downloaded from Lesson 9 and 10:

Vector Raster Table Studyareas Elevation Speciesrich

Roads Landuse Habitat

Ownership Boundary

The raster cell size was set to 50 under Environments. All the files were in Lambert Conformal Conic

(Pennsylvania State Plane North) NAD 83, in meters (King, Walrath, & Zeiders, 1999-2013). The following

methodology was performed to obtain each criteria:

Greater than 70 bird and mammalian species combined

The vectors "studyareas" and "boundary" were intersected using the intersect tool. A new field was

added in the "speciesrich" table to calculate the total species; the new field was called "total_sp", then

using the field calculator the fields birds and mammal were added. The table "speciesrich" was joined

with the new study area layer. After exporting the new study area layer with the table joined, an

attribute query was performed to identify the total species greater than 70.

Less than 10% of each study area occupied by buffered roads, highways, and interstates

Intersect the layer "roads" with the new study area layer to identify the roads within the study area

with species greater than 70. A new field was added to the new study area layer, the field was called

"area_orig" and using calculate geometry the area was calculated. A new field was added to the new

roads layer. The new field described the buffer distances for roads, highways, and interstates, which

was 20 m, 50m, and 100m, respectively. Then the new roads layer and new study areas (of total species

greater than 70) layer were joined using the union tool. Having the data from both layers, an attribute

query was performed to identify areas outside of the buffered zones within the study area, the buffer

distance of zero (0) help identify these areas, then a new layer was created showing the areas not

buffered. Under the layer showing areas not buffered, three (3) fields were added: new area

(no_rd_area), road area (rd_area), and road percent (rd_per). Refer to Figure 1, the new area was

calculated using calculate geometry the field was called "no_rd_area"; the road area was found by

subtracting the original area ("area_orig") with the new area, the field was named "rd_area"; finally, the

road percent was found by dividing the road area over original area and multiply that by 100, the field

was called "rd_per". The layer was converted to raster and reclassified to identify the study areas less

than 10% of road.

Figure 1 Snapshot of depicting the results of road percentage.

Figure 2 Left: Study areas are depicted in light blue and roads are shown in dark blue. Right: Raster of study areas identifying areas less than 10% road in green and areas greater than 10% road in blue.

High habitat potential

The layer "habitat" was intersected with the new study area layer. Then the new habitat layer was

converted to raster. The raster was reclassified to identify high and low potential. The high potential was

named as 1 in the new value and low potential as 0 in the new value. Refer to Figure 3.

Figure 3 High (purple) and low (green) habitat potential areas within the study areas.

Publicly owned land The layer "ownership" was intersected with the new study area layer. Then the new habitat layer was

converted to raster. The raster was reclassified to identify public and private land. The public land was

named as 1 in the new value and private land as 0 in the new value. Refer to Figure 4.

Figure 4 Public and private land within the study areas. Public land is depicted in purple and private land is shown in green.

Forested areas The raster file "landuse" was reclassified to identify forested areas and other land use areas. Forested areas was given a value of 1 and to the other areas a value of 0 was given. Refer to Figure 5.

Figure 5 Forested areas (purple) and other land use areas (green) in the county.

Slope less than 10% Using the slope tool in the spatial analyst, the "elevation" raster was selected to create a raster showing the slope in percentage. The slope raster was reclassified, the equal interval method and two (2) classes were selected under classify, then the first value on the right was changed to 10 and the value below was left by default. Having two ranges in the old values column in the reclassify tool window, from the ranges of 0 to 10 a value of 1 was given in the new values column and 0 in the second row. Refer to Figure 6.

Figure 6 Left: Slope percent output raster. Right: Slope percent reclassified, slopes less than 10% are shown in green and slopes greater than 10% are shown in wine.

Results

Using Map Algebra tool, a multiplication of the following reclassified rasters was performed: slope, land

use, ownership, habitat, and road percentage (this raster includes the species greater than 70). The

result output a raster combining all the project criteria. The potential conservation areas (suitable sites)

shown in Figure 7 are depicted in green. The suitable area has a total of 29,403 acres.

Figure 7 Potential Conservation Areas. The suitable sites are shown in green.

Conclusion

Acme Conservation Unlimited performed a site analysis to find potential conservation areas. The results

yield a total of 29,403 acres of suitable land. A cell size 50m x 50m was used to create these maps, which

was appropriate for this project. Greater cell size will produce lower resolution and a generalized

analysis; unlike lower cell size will allow analysis of small areas such as small parcels. As studied in GEOG

482, satellites provide higher resolution data, for example, the highest resolution of global topography

and bathymetry data can be obtained from ETOPO1 (1 arc-minute) (DiBiase and others, 2012).

References

DiBiase, D. and others. (2012). Nature of Geographic Information. The Pennsylvania State University.

Retrieved April 3, 2013 from http://natureofgeoinfo.org.

King, E., Walrath, D & Zeiders, M (1999-2013). Problem-Solving with GIS, Lesson 9 and 10. The Pennsylvania State University World Campus Certificate/MGIS Programs in GIS. Retrieved May 1, 2013.

This document is published in fulfillment of an assignment by a student enrolled in an educational offering of The Pennsylvania State

University. The student, named above, retains all rights to the document and responsibility for its accuracy and originality.

https://www.e-education.psu.edu/natureofgeoinfo