GEOG370 Ch9

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Information about GEOG370 Ch9

Published on January 19, 2008

Author: Marianna


Classification:  Classification GEOG370 Instructor: Christine Erlien Overview:  Overview Classification Reclassification Buffers Neighborhood functions, filters, & roving windows Classification:  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 Classification:  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 Classifications:  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 Classification: Land:  Classification: Land Land classifications depend on the types of objects to group Geological formations Wetlands Agriculture, land use, and land cover Land Classifications:  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 Anderson Classification:  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 Land Classifications:  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? Reclassification:  Useful in targeting a particular attribute of imagery Example: Reclassification Slide11:  Reclassification 0: black soil 1: red soil 0: forest 2: urban + = Solution: reclassify attribute values Create an expression: [landuse]+[soil] Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography Reclassification :  Reclassification Raster Change the attribute codes Reclassification :  Reclassification Original classification: Row crops (1-4): Corn, Potatoes, Vegetables, Other. Grain crops (5-10): Oats, Barley, Rye, Wheat, Buckwheat, and Other. Reclassification: 1-4=>1 5-10=>2 Line dissolve: Lines that separate classes that are going to be combined will be removed Vector  Change entities & attributes; line dissolve Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography Reclassification:  Reclassification Various measurement levels Nominal Ordinal Interval/ratio Range-graded classifications: Grouping ranges of numerical values into classes Buffers:  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 Buffers :  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 Buffers:  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 Buffers:  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 Graphic by Jun Liang, UNC-Chapel Hill, Department of Geography Neighborhood Functions :  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 Filter Types:  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 High Pass Filter:  High Pass Filter 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. Low-pass filter:  Low-pass filter Roving window:  Roving window From Demers (2005) Fundamentals of Geographic Information Systems Roving window: High pass filter :  Roving window: High pass filter Differences are enlarged. Roving window: Low pass filter:  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. Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography Directional pass filter:  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. 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 Neighborhood Functions:  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). Block function:  Block function From Demers (2005) Fundamentals of Geographic Information Systems This example: Maximum Other block function types: Majority Minimum Total Average Range Standard deviation Zonal functions:  Zonal functions 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. Focal function application:  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 Wrapping up:  Wrapping up

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