1. GIS and Remote Sensing Portfolio
by Kristen Hestir

2. Cartographic Study
Spatial correlation of autism to superfund sites and
arsenic in ground water.

4. Cartogram
Banana exports from South America to the USA.
Cartograms use distorted map geometry in order to
convey thematic information in a visually stimulating
and interesting way.

6. Viewshed Analysis
AM/FM Radio Coverage, Dona Ana County.
Viewshed illustrates an area of land that is “visible”
from a fixed vantage point.

8. Geographic Analysis
Comparison of gross domestic product to quality of
life indicators for top 100 countries in the world.

10. Geographic Analysis
Compares acreage of organic crops
to pesticide usage in California.

12. Assessment of an Invasive Species Using Remote Sensing and GIS
Spatio-temporal dynamics of Salt Cedar (Tamarix spp.)
in Northern Doña Ana County, 1936-2009.
Image processing techniques demonstrated:
use of aerial photography
georectification
mosaic
subset
digitizing
classification

13. Photographs of Four Sites along the Rio Grande near Las Cruces, NM in 2009
Site 1 Site 2
Site 3
Site 4
0 125 250
¯500 750
Meters
1,000
Projection: UTM, Zone 13N, NAD83
Data source: USDA 1936 Black and White Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)

14. Land Cover in Four Sites along the Rio Grande near Las Cruces, NM in 1936
Site 1 Site 2
Site 3
Site 4
¯
Land Cover Type (1936)
Built-up Salt cedar high Row crops
Barren Salt cedar medium Pecans Meters
0 125 250 500 750 1,000
Water Salt cedar low Other vegetation Projection: UTM, Zone 13N, NAD83
Data source: USDA 1936 Black and White Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)

15. Salt Cedar Dynamics 1936 - 2009
Image processing techniques demonstrated:
change detection
quantitative assessment

16. Salt Cedar Dynamics in Four Sites along the Rio Grande near Las Cruces, NM, 1936-2009
Site 1 Site 2
Site 3
Site 4
¯
Salt Cedar Dynamics (1936-2009)
Salt cedar increase Water persistent
Salt cedar persistent Other land covers persistent Meters
0 125 250 500 750 1,000
Salt cedar decrease Other land cover changes Projection: UTM, Zone 13N, NAD83
Data source: Land cover maps above Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
SALT CEDAR DYNAMICS IN THE FOUR STUDY SITES BETWEEN 1936 AND 2009.
3.61
PERCENT CHANGE IN
SALT CEDAR AND 27.40
31.36
OTHER LAND COVERS
AVERAGED ACROSS Water persistent
SITES, 1936-2009.
Other land covers persistent
37.54 Other land cover changes
Salt cedar increase

17. Land Cover Assessment Using Remote Sensing
Image Processing Techniques demonstrated:
digitized satellite imagery
classification

19. Land Cover Classification using Remote Sensing
Comparison of classification algorithms for
an arid environment, Yuma Valley, Arizona.

20. Maximum Likelihood Neural Networks
Overall Accuracy 72.9% Overall Accuracy 75.7%
Land Covers
Unclassified
Agriculture
Rangeland
Barren Land
Unsupervised
Parallelepiped Urban
Overall Accuracy 38.5%
Overall Accuracy 54.7%
Water
Wetland
0 2.5 5 10
¯15 20
Kilometers
Projection: UTM Zne 11N. Datum: WGS 84
Comparison of supervised classification techniques for Yuma Valley Arizona

21. Reporting of Land Cover
Classification Accuracy

22. Example of accuracy results illustrated with bar graphs.
Black = Correctly classified.
Wetland
Water
Urban
Rangeland
Barren
Agriculture
0% 20% 40% 60% 80% 100%
Agriculture Barren Rangeland Urban Water Wetland
Example of an error matrix used to evaluate classification accuracy.
Overall Agriculture Barren Rangeland Urban Water Wetland
71.8%
Agriculture 68.67 0.00 0.00 1.47 0.00 10.17
Barren 1.33 45.00 1.53 1.47 0.00 0.00
Rangeland 16.00 45.00 87.76 27.94 29.73 11.86
Urban 5.33 10.00 10.71 66.18 8.11 6.78
Water 0.67 0.00 0.00 0.00 45.95 3.39
Wetland 8.00 0.00 0.00 2.94 16.22 67.80

23. Visualizing Land Cover Change
Write Memory Function Insertion technique combines
feature stacks from different time periods then
different layers are inserted into the red, green, and
blue color guns to illustrate change.

24. Change detection using write memory function insertion, 1987-2007, Yuma Arizona
RGB = 2007 band 4, 1987 band 4, 2007 band 1.
agriculture change to urban
agriculture both years
0 2.5 5 10
Kilometers
/
Projection: UTM Zne 11N. Datum: WGS 84

25. Data Visualization using a Feature Space Plot
A feature space plot is derived from the image scene
by plotting a histogram of one band on an x-axis and a
histogram of another band on a y-axis.

26. Landsat TM derived data
b
Near Infra-red Band 4
c
a
Red Band 3
The feature space plots are derived from the a scene of Yuma, Arizona.
Magenta indicates high frequency of co-occurrence of brightness values in the two bands.
Blue indicates low frequency of co-occurrence of brightness values.
In this scene that there is:
- a high occurrence of wet soils (a)
- a high occurrence of vegetation at peak growth and moving toward senescence (b)
- a few occurrences of vegetation in early growth stages of growth or completely harvested (c)
and blue areas.

27. Endangered Species Investigation
Poster format
Will the Jaguar (Panthera onca)
persist in the New Mexico and Arizona?