1. Classification GEOG370 Instructor: Christine Erlien

2. Overview Classification Reclassification Buffers Neighborhood functions, filters, & roving windows

3. Classification A method of generalization Categorizing groups of objects Data grouped into classes according to some common characteristics; reduces the number of data elements Advantage: Reduction in # of data elements (& map complexity) Disadvantage: Variation exists within a class

4. Classification A good classification: –Classes are mutually exclusive (e.g., and object will belong to one & only one class) –Classes are exhaustive (e.g., well-defined enough so that need for “Other” category is eliminated) –Serves a useful function

5. Classifications Binary (yes/no)  simple –Ex.: Forest/non-forest –Disadvantage: Significant within-group variation (possibly > than between groups) –Solution: Establish more classes Issues –Graphic portrayal more complex –Boundaries Equal interval, quartile, natural breaks, standard deviation

6. Classification: Land Land classifications depend on the types of objects to group –Geological formations –Wetlands –Agriculture, land use, and land cover

7. Land Classifications Anderson –Level I: Obtained from Landsat data –Level II: Obtained from high altitude aerial photography –Level III: Obtained from medium altitude aerial photography

8. Anderson Classification Level I 1 Urban or Built-up Land 2 Agricultural Land 3 Rangeland 4 Forestland 5 Water 6 Wetland 7 Barren Land 8 Tundra 9 Perennial Ice and Snow Level II 11 Residential 12 Commercial and Services 13 Industrial 14 Transportation, Communications, and Utilities 15 Industrial and Commercial Complexes 16 Mixed Urban or Built-up Land 17 Other Urban or Built-up Land

9. Land Classifications National Land Cover Dataset (NLCD) –Modified version of Anderson classification Some level II classes consolidated Level III of Anderson classification not compatible with remote sensing resolution Why standardize?

10. Useful in targeting a particular attribute of imagery Example: Reclassification Land cover classClassificationReclassification Forest10 1 Water11 0 Settlement12 0 Agriculture13 0

11. Reclassification 0110 0010 0000 0101 0200 0220 0000 0000 0: black soil 1: red soil 0: forest 2: urban + = 0310 0230 0000 0101 ValueMeaning 01230123 Black soil and forest Forest on red soil Urban on black soil Red soil and urban Solution: reclassify attribute values Create an expression: [landuse]+[soil] Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography

12. Reclassification Raster Change the attribute codes http://www.itc.nl/ilwis/applications/application07.asp

13. Reclassification Original classification: Row crops (1-4): Corn, Potatoes, Vegetables, Other. Grain crops (5-10): Oats, Barley, Rye, Wheat, Buckwheat, and Other. 5 62 3 1 Reclassification: 1-4=>15-10=>2 Line dissolve: Lines that separate classes that are going to be combined will be removed 1 2 Vector  Change entities & attributes; line dissolve Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography

14. Reclassification Various measurement levels Nominal Ordinal Interval/ratio –Range-graded classifications: Grouping ranges of numerical values into classes

15. Buffers Create a zone of interest around an entity Buffer: A polygon created through reclassification at a specified distance from a point, line, or polygon. Example: Point buffer Finding stores within specified distance of an address Graphic by Jun Liang, UNC-Chapel Hill, Department of Geography

16. Buffers Example: Line buffer To locate all houses within 1 mile of major highway Example: Polygon buffer To locate all factories within 10 miles of a city Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography

17. Buffers Doughnut buffer: Multiple buffers around the same spatial object. Setbacks: Area available to the city for lighting and utility work; measured from the center of a suburban street some distance into each property. Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography

18. Buffers Variable buffer: Buffer based on friction, barriers, or any other neighborhood functions; buffer width changes from one line segment to another. –Can be arbitrary, based on measurable component of landscape, or mandated by law 100 meter influence 45 meter influence 150 meter influence Graphic by Jun Liang, UNC-Chapel Hill, Department of Geography

19. Neighborhood Functions Neighborhood function: GIS analytical function that operates on regions of the database within proximity of some starting point –Filter: A matrix of numbers used to modify grid cell/pixel values of original data using mathematical procedures

20. Filter Types High-pass filter: Enhances values that change rapidly from place to place; used to isolate edges –Directional filter: High pass filter that enhances linear objects with a particular orientation Low-pass filter: Emphasizes trends by eliminating unusual values through averaging

21. High Pass Filter http://isis.astrogeology.usgs.gov/IsisWorkshop/Lessons/PowerSpatialFilters/FilterIntro/highpassfilter.html Original 3x3 High Pass Filter Edges are sharp and small features stand out, while larger features are neutral. 7x7 High Pass Filter Edges are sharp and larger features have been enhanced, while the largest features are neutral.

22. Low-pass filter http://rst.gsfc.nasa.gov/Sect1/Sect1_13.html

23. Roving window From Demers (2005) Fundamentals of Geographic Information Systems

24. Roving window: High pass filter 4145 4445 404543414342 3944 4240 41434439 43 354039374340 38 363435 9 316053455671 266437234847 185755453132 445359172053 266643446257 75241217949 Differences are enlarged.

25. Roving window: Low pass filter Low-pass filter: Emphasizes trends by eliminating small pockets of unusual values. Low-pass filters generally serve to smooth the appearance of an image. 1/9 10060 100 60 100 95100 91826977 96918287 99 96 99 100 Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography

26. Directional pass filter Directional pass filters (Edge detection filters): Designed to highlight linear features; can also be designed to enhance features which are oriented in specific directions. Useful applications in geology, for the detection of linear geologic structures. 1/9 2 2 2 222 Can be used to detect east-west oriented linear objects. Can be used to detect northeast-southwest oriented linear objects. Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography

27. Neighborhood Functions Focal function: Considers neighborhoods; the output cell is the result of a calculation performed on a window of cells (kernel) around the cell of interest –e.g., filters Block function: Performs a function that produces a block of cells with new values Zonal function: Performs functions based on a group of cells with a common value (a zone).

28. Block function From Demers (2005) Fundamentals of Geographic Information Systems This example: Maximum Other block function types: Majority Minimum Total Average Range Standard deviation

29. Zonal functions http://courses.washington.edu/esrm590/lessons/raster_analysis1/index.html Here, the zones are defined by the zone grid. The function is a zonal sum, which sums all the input cells per zone, and places the output in each corresponding zone cell in the output.

30. Focal function application Mosaicking topographic quads to produce DEMs for watershed analysis Quadrangle boundaries  NoData values  gaps in data Focal mean function used to calculate values to assign to NoData cells http://www.esri.com/news/arcuser/0701/moredem.html

31. Wrapping up