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Working with Rasters

Working with Rasters. Spatial modeling in raster format. Basic entity is the cell Region represented by a tiling of cells Cell size = resolution Attribute data linked to individual cells. Issue #1 - resolution. Larger cells: less precise spatial fix line + boundary thickening

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Working with Rasters

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  1. Working with Rasters CS 128/ES 228 - Lecture 5a

  2. Spatial modeling in raster format • Basic entity is the cell • Region represented by a tiling of cells • Cell size = resolution • Attribute data linked to individual cells CS 128/ES 228 - Lecture 5a

  3. Issue #1 - resolution Larger cells: • less precise spatial fix • line + boundary thickening • features too close overlap - less detail possible Fig. 3.10, 3rd ed. CS 128/ES 228 - Lecture 5a

  4. Why not always use tiny cells? • Data inputs may have limited spatial resolution - pixel size for aerial, satellite photos- reliability of coordinate measurements • Size of data files • Speed of analysis CS 128/ES 228 - Lecture 5a

  5. Issue #2 - determining cell values • Data inputs may already contain cell values: aerial, satellite photos • Cell values may be assigned: “pseudocolors” • Ultimately all cell values must be coded numerically CS 128/ES 228 - Lecture 5a

  6. Image depth • minimum = 1 bitB/W image or P/A data • 8-bit image = 256 levels of gray (can be pseudo-colored) • 24-bit image = true-color. Each primary color has separate layer CS 128/ES 228 - Lecture 5a

  7. Determining cell values CS 128/ES 228 - Lecture 5a

  8. Filtering raster data • Neighborhood averaging • Smoothes “holes” and transitions • Other techniques available Chang 2002, p. 203 CS 128/ES 228 - Lecture 5a

  9. Issue #3 - layers in raster format • Each layer must be referenced in common coordinates • Thematic data can be combined and revised (reclassified) CS 128/ES 228 - Lecture 5a

  10. Analysis by raster overlay Fig. 6.17, 3rd ed. CS 128/ES 228 - Lecture 5a

  11. Lack of spatial registration CS 128/ES 228 - Lecture 5a

  12. Georeferencing raster images • Spatial coordinates may be absent or purely map coordinates (i.e. inches from one corner) • Control points: point features visible on both the image and the map • Linear or nonlinear transformations • “Rubber sheeting” CS 128/ES 228 - Lecture 5a

  13. Issue #4 – mosaicking rasters http://www.microimages.com/featupd/v57/mosaic/ CS 128/ES 228 - Lecture 5a

  14. Mosaicking: mismatched tiles Ex. Aerial photographs of Kinzua Reservoir What do you suppose caused the drastic differences in water clarity in the lake? Google map of Onoville, NY. Accessed 6 Oct 2008 CS 128/ES 228 - Lecture 5a

  15. Mosaicking: adjusting color values Histogram matching: • Computer compiles histogram of color (or gray) values in 1 tile • 2nd tile’s colors adjusted to match CS 128/ES 228 - Lecture 5a

  16. Raster data editing CS 128/ES 228 - Lecture 5a

  17. Clip to rectangle ... CS 128/ES 228 - Lecture 5a

  18. … vs.clip to shapefile CS 128/ES 228 - Lecture 5a

  19. Summary • A huge amount of spatial data are available in raster format • Rasters make excellent “base maps” • Easy to layer but watch coordinate systems! • Difficult/impossible to edit or reproject USGS Digital Raster Graphic (DRG) Quadrangle(1:24,000 scale - UTM Zone 17, NAD 27) CS 128/ES 228 - Lecture 5a

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