GIS and Remote Sensing Portfolio by Kristen Hestir
Cartographic StudySpatial correlation of autism to superfund sites and arsenic in ground water.
Cartogram Banana exports from South America to the USA. Cartograms use distorted map geometry in order toconvey thematic information in a visually stimulating and interesting way.
Viewshed Analysis AM/FM Radio Coverage, Dona Ana County.Viewshed illustrates an area of land that is “visible” from a fixed vantage point.
Geographic AnalysisComparison of gross domestic product to quality of life indicators for top 100 countries in the world.
Geographic AnalysisCompares acreage of organic crops to pesticide usage in California.
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
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, NAD83Data source: USDA 1936 Black and White Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
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, NAD83Data source: USDA 1936 Black and White Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
Salt Cedar Dynamics 1936 - 2009Image processing techniques demonstrated: change detection quantitative assessment
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
Land Cover Assessment Using Remote SensingImage Processing Techniques demonstrated: digitized satellite imagery classification
Land Cover Classification using Remote Sensing Comparison of classification algorithms for an arid environment, Yuma Valley, Arizona.
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 84Comparison of supervised classification techniques for Yuma Valley Arizona
Reporting of Land Cover Classification Accuracy
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 WetlandExample of an error matrix used to evaluate classification accuracy.Overall Agriculture Barren Rangeland Urban Water Wetland71.8%Agriculture 68.67 0.00 0.00 1.47 0.00 10.17Barren 1.33 45.00 1.53 1.47 0.00 0.00Rangeland 16.00 45.00 87.76 27.94 29.73 11.86Urban 5.33 10.00 10.71 66.18 8.11 6.78Water 0.67 0.00 0.00 0.00 45.95 3.39Wetland 8.00 0.00 0.00 2.94 16.22 67.80
Visualizing Land Cover Change Write Memory Function Insertion technique combines feature stacks from different time periods thendifferent layers are inserted into the red, green, and blue color guns to illustrate change.
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
Data Visualization using a Feature Space Plot A feature space plot is derived from the image sceneby plotting a histogram of one band on an x-axis and a histogram of another band on a y-axis.
Landsat TM derived data b Near Infra-red Band 4 c a Red Band 3The 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.
Endangered Species Investigation Poster format Will the Jaguar (Panthera onca)persist in the New Mexico and Arizona?