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GIS and Remote Sensing Projects Portfolio                  by            Kristen Hestir              Maps              Ima...
California Organic Crops (2003)                                                   versus Pesticide Use (2007)Geographic An...
Cartography                                             Cartogram of Banana Exports                                       ...
Spatial Analysis  Viewshed AM/FM Radio  Coverage     of  Dona Ana   County     Viewshedillustrates an area   of land that ...
Top Ranked 100 Countries by                                                 Gross Domestic Product and Quality of Life, 20...
Autism Prevalence (2006), Superfund Sites (2007) and Arsenic Groundwater Contamination (2001)                             ...
Wyoming      Nebraska                          Mesilla Valley,                          New Mexico                        ...
Leaf-On                                 (Min: 300°K; Max: 329°K)  Image Derivative                                     Tem...
Leaf-On Image                           (Min: 0.11; Max: 0.97)Derivative                                      NDISINormali...
Leaf-On                                   Band 1 Min: 46 Max: 16811                                   Band 2 Min: -729 Max...
Landcover Assessment from Landsat TM5 Image,Mesilla Valley 2009Digitized Land CoverChange Maps       Land Cover Classes   ...
Land                                    Agricultural Land Cover                                   Barren Land             ...
Land                                       Agricultural Land Cover                                      Barren Land       ...
Process Flow ChartStage 1:     Leaf-on              Tasseled Cap         Principal                  Land Surface          ...
Matrices - Error Assessment and Statistical Test of                     Significance                                      ...
Comparative Analysis – Bar Chart                                                                Confusion Matrices Results...
Comparative Analysis - Scattergram                                                                     McNemar Tests Resul...
Comparative Analysis – Line Chart                                      Confusion Matrices Results                       74...
Comparative Analysis – Line Chart                                                Confusion Matrices Results               ...
AnAssessment   Using  RemoteSensing and   GIS Salt CedarDynamics in Northern Doña Ana County, N     M
New Mexico                                                                                         Site 1                 ...
Site 1                                                      Site 21936           Site 3                                   ...
Site 1                                                      Site 21955           Site 3                                   ...
Site 1                                                      Site 21983           Site 3                                   ...
Site 1                                                      Site 22009           Site 3                                   ...
Site 1                                                 Site 2 Land           Site 3                                       ...
Site 1                                                 Site 2 Land           Site 3                                       ...
Site 1                                                 Site 2 Land           Site 3                                       ...
Site 1                                                 Site 2 Land           Site 3                                       ...
WILL THE JAGUAR (Panthera onca) PERSIST IN NEW MEXICO AND                                                                 ...
LAND COVER CLASSIFICATION IN AN ARID REGION: AN                                               EVALUATION OF REMOTE SENSING...
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GIS RS Portfolio

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  • Cover 41% of Earth’s land surface, home to 35% of world population, experiencing rapid population growth, a driver of land cover change
  • Add image M12 = # of pixels misclassified in Map1 and not in Map 2M21 = # of pixels misclassified in Map 2 and not in Map 1If M12 + M21 > 19 then: Χ2 = (|M12 – M21| - 1)2 / (M12 + M21)At 1 degree of freedom, 0.05% confidence interval, If Χ2 > 3.84 then differences are statistically significant
  • Transcript of "GIS RS Portfolio"

    1. 1. GIS and Remote Sensing Projects Portfolio by Kristen Hestir Maps Image Processing Charts Tables Graphs Geospatial analysis of invasive species Posters
    2. 2. California Organic Crops (2003) versus Pesticide Use (2007)Geographic Analysis Pounds of pesticide per crop acre 0.00 - 1.00 1.01 - 2.50 2.51 - 5.00 5.01 - 10.00 Compares 10.01 - 12.60 Pesticides types include: insecticides acreage of hebicides microbiocides fungicides rodenticides organic crops ®to pesticide usage in One dot represents 50 acres of organic crops 50California. 250 1000 Representative densities: number of acres per 100 square kilometers Crops include: field crops fruit and nutsl livestock and apiary vegetables nursery and floriculture Projection: California Teale Albers Kilometers 0 50 100 200 300 400 500 Kristen Hestir 5/01/200 Source: University of California at Davis, Statistical Review of Californias Organic Agriculture, 1998-2003 and Pesticide Action Network GIS and Cartography
    3. 3. Cartography Cartogram of Banana Exports to the United States, 2002 Banana Mexico exports fromSouth America Honduras Jamaica Guatemala to the USA. Nicaragua Costa Rica Venezuela Cartograms Panama Colombia use distorted Banana Exports in 1000 Kilogram Units 1 - 200,000map geometry 200,001 - 400,000 400,001 - 600,000 Ecuador in order to 600,001 - 800,000 Bolivia Brazil 800,001 - 1,022,347 Peru convey Includes all bananas as food either fresh or dried thematicinformation in Exports in 1000 Kilogram Units a visually stimulating 225 450 900 ± way. Krsiten Hestir, 4/24/2009, Cartography & GIS Source: Tariff and trade data from the U.S. Department of Commerce, the U.S. Treasury, and the U.S. International Trade Commission.
    4. 4. Spatial Analysis Viewshed AM/FM Radio Coverage of Dona Ana County Viewshedillustrates an area of land that is “visible” from a fixed vantage point.
    5. 5. Top Ranked 100 Countries by Gross Domestic Product and Quality of Life, 2005 Top 5 countries: Ireland, Switzerland, Norway, Luxemborg, Sweden Rank by GDP per capita Rank (best to least) 9 Criteria for Quality of Life quality of life 1-5 Material wellbeing Climate and geography 6 - 10 1-5 Life expectancy Job security Political stability and security Political freedom ³ 11 - 50 6 - 10 Low divorce rate Gender equality 51 - 100 11 - 50 Community life Not in top 100 51 - 100 0 1,750 3,500 5,250 7,000Source: The Economist Intelligence Unit Quality of Life Index KilometersKristen Hestir, 4/15/2009
    6. 6. Autism Prevalence (2006), Superfund Sites (2007) and Arsenic Groundwater Contamination (2001) WA MT MN ND ME OR VT WI NY NH SD ID MA WY MI RI CT IA PA NV NE OH NJ IN IL DE UT CO MO MD VA KS WV KY CA NC AZ OK TN NM AR SC GA MS AL TX TX LAWells with Unsafe Arsenic Levels Representative Densities: FL Percent of Childrenper 1,000 Square Kilometers Number of Superfund Sites 0.16 to 0.20 per 125 Square Kilometers No unsafe wells 0.21 to 0.40 ± 0.01 to 0.04 5 Superfund sites 0.41 to 0.60 0.05 to 0.25 30 Superfund sites 0.61 to 0.80 0.26 to 0.50 0.51 to 1.00 60 Superfund sites 0.81 to 0.95 1.01 to 2.13 One dot represents 5 superfund sites Kilometers Dot placement is randomized at the state level 0 250 500 1,000 1,500
    7. 7. Wyoming Nebraska Mesilla Valley, New Mexico Nevada Utah Colorado California Kansas Oklahoma Arizona New MexicoStudy Ri o Gr an Texas deArea Ri v MEXICO erMaps Projection: Lambert Conformal Conic Yuma Valley, Projection: UTM, WGS 84, Zone 13S River Arizona o rad lo Mesilla Valley Study Area Co Yuma Valley Study Area Las Cruces Metro Yuma 1990 Metro Yuma 2007 Metro 0 10 20 Kilometers ¯ Projection: UTM, WGS 84, Zone 11S
    8. 8. Leaf-On (Min: 300°K; Max: 329°K) Image Derivative TemperatureLand Surface (Degrees Kelvin) High: 329Temperature Maps Low : 279 Yuma Valley, AZ Leaf-Off (Min: 279°K; Max: 311°K) 0 5 10 15 Kilometers ¯
    9. 9. Leaf-On Image (Min: 0.11; Max: 0.97)Derivative NDISINormalized High : 0.98DifferenceImpervious Low : 0.11 Surface YumaValley, AZ Leaf-Off (Min: 0.11; Max: 0.98) 0 5 10 15 Kilometers ¯
    10. 10. Leaf-On Band 1 Min: 46 Max: 16811 Band 2 Min: -729 Max: 5877 Band 3 Min: -7101Max: 3270 Image Derivative Tasseled Cap TCTTransformation RGB Red: Band 1 Green: Band 2 Yuma Valley, Blue: Band 3 AZ Leaf-Off Band 1 Min: 469 Max: 14905 Band 2 Min: -913 Max: 4457 Band 3 Min: -6160 Max: 3622 0 5 10 15 Kilometers ¯
    11. 11. Landcover Assessment from Landsat TM5 Image,Mesilla Valley 2009Digitized Land CoverChange Maps Land Cover Classes Residential Cropland and Pasture Streams and Canals Strip Mines, Quarries, Gravel Pits Industrial and Commercial Orchards, Etc. Reservoirs Transitional Areas Transportation Confined Feeding Operations Forested Wetland Mixed Barren Land Mixed Urban or Built-Up Land Mixed Rangeland Sandy Areas other than Beaches 0 7.5 15 22.5 30 Kilometers /
    12. 12. Land Agricultural Land Cover Barren Land RangelandChange Urban Water Maps Wetlandand Pie 2% 1%Charts 9% 13% 9% 1985 66% Overall Accuracy = 76%
    13. 13. Land Agricultural Land Cover Barren Land RangelandChange Urban Water Maps Wetlandand Pie 3% 2%Charts 5% 16% 10% 2009 64% Overall Accuracy = 83.7%
    14. 14. Process Flow ChartStage 1: Leaf-on Tasseled Cap Principal Land Surface Normalized Leaf-on Component Analysis Temperature Leaf-on Difference Leaf-off Leaf-on Impervious Surface Tasseled Cap Land Surface Leaf-on Leaf-on, Leaf- Leaf-off Principal Temperature Leaf-off off Component Analysis Normalized Tasseled Cap Leaf-off Land Surface Difference Leaf-on, Leaf- Temperature Leaf-on, Impervious Surface off Principal Leaf-off Leaf-off Component Analysis Leaf-on, Leaf-off Normalized Difference Classify: Maximum Likelihood Evaluate: Confusion Matrices and McNemar tests. Impervious Surface Leaf-on, Leaf-offStage 2: Select top performers and apply: 5 textures: entropy, angular second moment, homogeneity, correlation, contrast 3 x 3, 5 x 5 and 7 x 7 windows, Classify: Maximum Likelihood Evaluate: Confusion Matrices and McNemar tests.Stage 3: Select top performers and apply: Combined feature stacks: textures, derivatives etc. Classify with: Maximum Likelihood, Support Vector Machine, Artificial Neural Network Evaluate: Confusion Matrices
    15. 15. Matrices - Error Assessment and Statistical Test of Significance Ground Reference Data (Pixels) Map Data Agriculture Barren Rangeland Urban Water Wetland TotalConfusion Agriculture 45 0 0 1 1 0 47 Barren 5 28 14 7 1 1 56Matrix: Rangeland 61 5 308 7 1 4 386 Urban 9 21 33 155 1 5 224 Water 27 0 5 21 51 0 104 Wetland 64 3 55 11 7 43 183 Total 211 57 415 202 62 53 1000Accuracy Overall accuracy, Kappa coefficientMeasures Map 1 ₂₁ wrong ₁₂ correctMcNemar Map 2 wrong sum both wrong M total wrong Map 2Matrix: correct M sum both right total right Map 2 total wrong Map 1 total right Map 1
    16. 16. Comparative Analysis – Bar Chart Confusion Matrices Results Mesilla Valley Yuma Valley 83 83 78Overall Accuracy (%) 78 73 L-On 73 68 68 L-Off 63 63 12B 58 58 No LST NDISI PCA TCT No LST NDISI TCT PCA Derivative Derivatives Feature Stacks Feature Stack
    17. 17. Comparative Analysis - Scattergram McNemar Tests Results Mesilla Valley Yuma Valley 600 550 L-Off PCA 225 12B TCT L-Off TCTMcNemar Sums 500 6B Off NDISI 450 175 12B LST 400 L-Off 12B TCT L-Off LST 350 125 L-On L-Off NDISI 12B NDISI 300 L-On LST 12B 250 L-On TCT 75 12B PCA L-On NDISI 12B LST 6B-Off 6B Off PCA 200 12B PCA L-On PCA 6B OFF LST 150 25 0 3 6 9 12 15 0 3 6 9 12 15 High ------- Overall Accuracy Rank ------ Low High ------- Overall Accuracy Rank ------Low Statistically similar
    18. 18. Comparative Analysis – Line Chart Confusion Matrices Results 74 72Overall Accuracy (%) Mesilla Valley 70 overall accuracy 68 66 64 Yuma Valley overall 62 accuracy 60 Feature Stacks from Stage 1
    19. 19. Comparative Analysis – Line Chart Confusion Matrices Results 86 Mesilla Valley Stage 1 76 Mesilla ValleyOverall Accuracy (%) Stage 2 66 Mesilla Valley 56 Stage 3 Yuma Valley 46 Stage 1 36 Yuma Valley Stage 2 26 Yuma Valley 1 3 5 7 9 11 13 15 Stage 3 High ------------ Overall Accuracy Rank ------------ Low
    20. 20. AnAssessment Using RemoteSensing and GIS Salt CedarDynamics in Northern Doña Ana County, N M
    21. 21. New Mexico Site 1 Site 2 Salt CedarDynamics in Las Cruces Site 3Study Areas Site 4 0 5 10 20 Kilometers Projection: UTM Zone 13N, NAD 83 M. Smith, T. Jones, V. Prileson, and K. Hestir, 2010/04/11 ¯
    22. 22. Site 1 Site 21936 Site 3 Site 4LandCover Built-up Barren Land Cover Type Salt cedar high Salt cedar medium Row crops Pecans 0 250 ¯ 500 Meters 1,000 Projection: UTM, Zone 13N, NAD83 Water Salt cedar low Other vegetation Data source: NAIP 2009 Natural Color Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    23. 23. Site 1 Site 21955 Site 3 Site 4LandCover Built-up Barren Land Cover Type Salt cedar high Salt cedar medium Row crops Pecans 0 250 ¯ 500 Meters 1,000 Projection: UTM, Zone 13N, NAD83 Water Salt cedar low Other vegetation Data source: NAIP 2009 Natural Color Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    24. 24. Site 1 Site 21983 Site 3 Site 4LandCover Built-up Barren Land Cover Type Salt cedar high Salt cedar medium Row crops Pecans 0 250 ¯ 500 Meters 1,000 Projection: UTM, Zone 13N, NAD83 Water Salt cedar low Other vegetation Data source: NAIP 2009 Natural Color Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    25. 25. Site 1 Site 22009 Site 3 Site 4LandCover Built-up Barren Land Cover Type Salt cedar high Salt cedar medium Row crops Pecans 0 250 ¯ 500 Meters 1,000 Projection: UTM, Zone 13N, NAD83 Water Salt cedar low Other vegetation Data source: NAIP 2009 Natural Color Aerial Photography Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    26. 26. Site 1 Site 2 Land Site 3 Site 4 CoverDynamics 1936- 1955 ¯ Salt Cedar Dynamics Salt cedar increase Water persistent Salt cedar persistent Other land covers persistent Meters 0 250 500 1,000 Salt cedar decrease Other land cover changes Projection: UTM, Zone 13N, NAD83 Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    27. 27. Site 1 Site 2 Land Site 3 Site 4 CoverDynamics 1955- 1983 ¯ Salt Cedar Dynamics Salt cedar increase Water persistent Salt cedar persistent Other land covers persistent Meters 0 250 500 1,000 Salt cedar decrease Other land cover changes Projection: UTM, Zone 13N, NAD83 Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    28. 28. Site 1 Site 2 Land Site 3 Site 4 CoverDynamics 1983- 2009 ¯ Salt Cedar Dynamics Salt cedar increase Water persistent Salt cedar persistent Other land covers persistent Meters 0 250 500 1,000 Salt cedar decrease Other land cover changes Projection: UTM, Zone 13N, NAD83 Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    29. 29. Site 1 Site 2 Land Site 3 Site 4 CoverDynamics 1983- 2009 ¯ Salt Cedar Dynamics Salt cedar increase Water persistent Salt cedar persistent Other land covers persistent Meters 0 250 500 1,000 Salt cedar decrease Other land cover changes Projection: UTM, Zone 13N, NAD83 Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)
    30. 30. WILL THE JAGUAR (Panthera onca) PERSIST IN NEW MEXICO AND ARIZONA? Kristen Hestir Department of Geography, New Mexico State UniversityFigure 1. JungleWalk.com (*). Figure 2. JungleWalk.com (*). Introduction Methods Conservation Efforts and Threats Jaguars (Panthera onca), the largest felids in the Americas, once The methods in this study are Conservation Efforts: were common in the southwestern United States. based on a literature review: In 1997 the jaguar was placed on the endangered species list by the United States Department of the Interior, Fish and Wildlife Service. Jaguars have been sighted in Arizona and New Mexico but with decreasing frequency in the past 100 years (McCain and Childs • General description of the In 2009, the U. S. Fish and Wildlife Service declared designation of 2008). Only four males sighted in last 20 years. species. critical habitat is necessary and is developing proposed sites. Why try to conserve the Arizona and New Mexico part of their • Range (historical and current) Disagreement within the jaguar conservation community. Use time range? Populations that reside on the periphery of ranges can habitat requirements. and money to save peripheral populations, essential to survival of be critical for the long-term survival of the species. species • Conservation efforts and OR threats to survival in Arizona concentrate time and money on the more densely populated Research Question and New Mexico. Figure 7. http://www.destination360.com/south- america/brazil/images/st/amazon ranges. -animals-jaguar.jpg. Will jaguars persist in the New Threats: Mexico and Arizona part of U.S.-Mexico border fence (from 2007), partitions northern their range given the current range, reduces natural prey, limits water supplies, reduces mating status of the species and Results potential, shifts migrant traffic and law enforcement activities into ongoing conservation mountain habitats (further degrading habitats and increasing efforts? Species Description:: encounters with humans). Northern jaguars are smaller than their South American relatives. Figure 3 JungleWalk.com (*). Jaguars have fur with small dots, large irregular spots and rosette Illegal killing continues due to cattle depredations, pelts markings (Figures 1-3, 5-7). No two are alike, distinctive patterns (Figure 9) and incidental takes from traps and snares. are used to identify individuals. Loss of habitat due to urban expansion, mineral Study Site mining, increased cattle grazing, water mining. Study site located in southern portions of Arizona and New Size ranges from 1.7 to 2.4 meters (nose to tail tip) in Mexico (Figure 4), bordering Mexico. Based on historical ranges length, weighing between 45 to 113 kilograms. Climate change: models predict widespread Figure 9. http://www.flickr.com/photos and recent remote camera sightings. Prey: cattle (57% of biomass consumed), white-tailed deer, wild ecosystem disruptions in Mexico. /barcdog/2409633979/. pig, rabbits,jackrabbits, coatis (raccoon family), skunk, coyote, and reptiles (Rosas-Rosas 2006). Conclusions Range: Persistence in Arizona and New Mexico depends largely upon the Variety of habitats from rain forest critical habitat proposal by the U.S. Fish and Wildlife Service and to arid scrub. In the Sonoran the fate of the U.S.-Mexico border fence. Jaguars have a grim Figure 5. JungleWalk.com (*). desert they use scrub, mesquite, prognosis for survival in the study area. grassland, woodlands. Range size varies widely, Acknowledgements 33 km2 to 1300 km2 per individual I would like to thank Dr. Carol Campbell for the interesting topic. (Figure 8). Density 1 to 10 individuals per 100 km2. depending on resource (*) http://www.junglewalk.com/photos/jaguar-pictures-I6147.htm References Brown, D. E. 1983. On the status of the jaguar in the southwest. The Southwestern Naturalist 28 (4):459-460. Conde, D. A., F. Colchero, H. Zarza, N. L. Christenssen, J. O. Sexton, C. Manterola, C. Chávez, A. Rivera, D. Azuara, and G. Ceballos. Sex matters: modeling male and female habitat differences for jaguar conservation. Biological Conservation 143:1980-1988. availability and habitat fragmentation. Federal Register, January 13 75 (8): 1741-1744. Foster, R. J., B. J. Harmsen, and C. P. Doncaster. 2010. Habitat use by sympatric jaguars and pumas across a gradient of human disturbance in Belize. Biotropica 42 (6):724-731. Grigione, M. M., K. Menke, C. López-González, R. List, A. Banda, J. Carrera, R. Carrera, A. J. Gordano, J. Morrison, M. Sternberg, R. Thomas, and B. Van Pelt. 2009. Identifying potential conservation areas for felids in the USA and Mexico: integrating reliable knowledge across an international border. Fauna and Flora International, Oryx 43 (1):78-86. Haag, T., A. S. Santos, D. A. Sana, R. G. Morato, L. Cullen. P. G. Crawshaw, C. De Angelo, M. S. Di Bitetti, F. M. Salzano, and E. Eizirik. 2010. The effect of habitat fragmentation on the genetic structure of a top predator: loss of diversity and high differentiation among Figure 8. Estimated historical range of remnant populations of Atlantic Forest jaguars (Panthera onca). Molecular Ecology. 19:4906–4921. Figure 4. Study area and locations of jaguars reported Hamilton, S. D. 2010. Investigative Report Macho B. U.S. Fish and Wildlife Service. Figure 6. JungleWalk.com (*). jaguars based on expert opinion (Grigione et Hatten, J. R.., A. Averill-Murray, and W. E. Van Pelt. 2005. A spatial model of potential jaguar habitat in Arizona. Journal of Wildlife Management 69 (3):1024-2005. killed in Arizona and New Mexico 1900-1980 (adapted McCain, E. B., and J. L. Childs. 2008. Evidence of resident jaguars (Panthera onca) in the southwestern United States and the implications for conservation. Journal of Mammalogy 89 (1):1-10. Navarro-Sermentc, C., C. A. López-González, J. P. Gallo-Reynoso. 2005. Occurrence of jaguar (Panthera onca) in Sinaloa, Mexico. The Southwestern Naturalist 50 (1):102-106. from Brown 1983). al. 2009). Rabinowitz, A., and K. A. Zeller, 2010. A range-wide model of landscape connectivity and conservation for the jaguar, Panther onca. Biological Conservation 143 (4):939-945. Rosas-Rosas, O. C. 2006. Ecological status and conservation of jaguars (Panthera onca) in northeastern Sonora, Mexico. Dissertation, New Mexico State University, Las Cruces, New Mexico, USA. 1. Spangle, S. L. 2007. Biological opinion 22410-2007-F-0416: pedestrian fence projects at Sasabe, Nogales and Naco-Douglas, Arizona. United States Fish and Wildlife Service, Phoenix, Arizona..
    31. 31. LAND COVER CLASSIFICATION IN AN ARID REGION: AN EVALUATION OF REMOTE SENSING APPROACHES Kristen Hestir1 and Dr. Michaela Buenemann1 1Department of Geography, New Mexico State University PROBLEM STATEMENT CHALLENGES OF CLASSIFYING LAND COVER IN ARID REGIONS • Human induced land cover change is occurring at unprecedented rates worldwide and is affecting an estimated 39 to 50% of Earth’s land surface. • Spectral responses of bright desert 4500 4000 5000 4500 • Drylands are of particular concern, they cover 41% of Earth’s land surface, are home to 35% of world population and are experiencing soils are often confused with the spectral 3500 4000 Reflectance x100 Reflectance x 100 3500 rapid population growth. response of impervious (urban) surfaces 3000 3000 2500 • Land cover change information can provide a basis for understanding what dryland areas are at risk, what this means for desert (Figure 3). 2000 2500 Impervious Surface Barren Land ecosystems. • Soils dominate the spectral response 1500 Rangeland 2000 1500 Rangeland • Landsat Thematic Mapper satellite imagery can provide spatially explicit and continuous information on land cover change. By using the weaker signal of sparse vegetation 1000 1000 500 500 various classification algorithms and feature stacks, land cover types can be differentiated in the imagery based on their unique spectral can be lost. 0 0 and spatial characteristics. • Physiological qualities of dryland 1 2 3 4 5 6 1 2 3 4 5 6 Bands • There are, however, some characteristics of drylands which make land cover classification challenging. vegetation decreases the strong red Bands edge and reduces absorption in the Figure 3. Comparison rangeland spectra (pink) and Figure 4. Comparison of rangeland spectra (white) visible bands compared to typical impervious (urban) surfaces . and barren land (yellow). OBJECTIVES non-dryland vegetation. • Dryland vegetation is highly sensitive to resources, so the same species at different locations can have variable spectral responses • Classify land cover of the Mesilla Valley (Figures 1 & 2) using two classification algorithms and various combinations of Landsat TM- (Figure 4). derived spectral and textural information • Soils dominate spectral responses; however, they can have heterogeneous mineral content, causing variable spectral responses (Figure4). • Compare the land cover maps in terms of their overall accuracies. METHODS AND ACCURACY ASSESSMENT RESULTS AND DISCUSSION • A leaf-on image of July 29, 2009 was georectified to a 2009 National Aerial Imagery Program Digital Ortho-Quarter Quad (DOQQ) and 95.00% A land cover map (Figure 6) was produced for radiometrically corrected using ENVI FLAASH atmospheric correction module. A leaf-off image of March 23, 2009 was georectified to 90.00% each classification algorithm and various the leaf-on image and radiometrically corrected to the leaf-on image using empirical line calibration. 85.00% combinations of Landsat TM-derived spectral • 1000 GPS and DOQQ points representing 5 land covers (agriculture, barren, rangeland, water, built-up) and shadow were used to train O verall Accuracy 80.00% and textural information. 75.00% the two classifiers, Maximum Likelihood (MLC) and Support Vector Machine (SVM). Leaf-on 70.00% Leaf-off • Image stacks (Figure 5) included combinations of 6 bands leaf-on, 6 bands leaf-off, Principal Components Analysis (PCA), Tasseled Cap Stage 1: Initial classifications show stacking leaf-on Leaf-on Leaf-off 65.00% (TC), Land Surface Temperature (LST), and Normalize Difference Impervious Surface Index (NDISI). and leaf-off imagery gives equal or improved accuracy 60.00% • Map accuracies were assessed using error (confusion) matrices based on 1000 randomly generated reference points. Methods over single date stacks (Figure 7). 55.00% 50.00% 6 bands PCA 4 TC LST NDISI PROCESS FLOW Land Covers STUDY AREA Built-Up Agriculture Water Barren Figure 7: Classification accuracies for Stage 1. Rangeland 94.00% Utah Colorado Stage 1: Leaf-on Tasseled Cap Principal Component Land Surface Normalized Difference 92.00% Leaf-on Analysis Leaf-on Temperature Leaf-on Impervious Surface 90.00% § ¦ ¨ I-25 Leaf-off Leaf-on O verall Accuracy Tasseled Cap Principal Component Land Surface 88.00% Leaf-on, Leaf-off Leaf-off Analysis Leaf-off Temperature Leaf-off 86.00% Normalized Difference Initial Accuracies Figure 6: Example classified map. Tasseled Cap Principal Component Land Surface Impervious Surface 84.00% Entropy Arizona New Mexico Leaf-off Stage 2: The texture filters entropy and Homogeneity Leaf-on, Leaf-off Analysis Leaf-on, Temperature Leaf-on, 82.00% Leaf-off Leaf-off homogeneity, with 7 by 7 80.00% New Mexico Normalized Difference Impervious Surface window, improved stage 1 initial 78.00% Texas Leaf-on, Leaf-off accuracy by 2.5 %, 8.9% , 8.3%, 5.0 % 76.00% 6 Bands PCA 4 TC LST NDISI Select top 5 and apply textures: and 2.1% for 6 § ¦ ¨I-10 Stage 2: bands, PCA4, TC, LST, and NDISI Figure 8: Classification accuracies for Stage 2 with top Mexico 3 x 3, 5 x 5 and 7 x 7 windows stacks respectively (Figure 8). two textures. 5 textures 94 Stage 3: Multiple image derivatives improved Texas 93.5 classification accuracy even further (1.2%, 1.5% Overall Accuracy U.S. Bureau of the Census, Map of United States 93 0 125 250 500 and 1.8% improvement over stage 2 for the 3 Kilometers Select top 3 and apply combined feature stacks: 92.5 mlc combinations. MLC and SVM classification 92 svm Boundaries and Roads textures, derivatives etc. 91.5 algorithms performed equally well. Differences Stage 3: Add classification algorithm: Leaf-on Leaf-off + Leaf-on Leaf-off + PCA 4 + homo + TC homo + pca 4 homo + TC homo in overall accuracy ranged from ( 0.2 % to 1.6 %) Las Cruces Study Interstate Maximum Likelihood Image Stacks and Multiple Derivatives between the two classifiers (Figure 9). Metro Area Highway Projection: UTM Zone 13N, Datum: WGS 84 Support Vector Machine Figure 9: Classification accuracies for Stage 3. Ü 0 2.5 5 10 15 20 Kilometers ACKNOWLEDGMENTSFigure 1: Location of the study area. Figure 2: Imagery from: USGS Global Visualization Figure 5: Flowchart of Image Processing. This work was supported by NSF Grant DEB-0618210, as a contribution to the Jornada Long-Term Ecological Viewer. Research (LTER) program, by the United States Department of Agriculture, Agricultural Research Service
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