1. CS 128/ES 228 - Lecture 5a1 Working with Rasters

2. CS 128/ES 228 - Lecture 5a2 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

3. CS 128/ES 228 - Lecture 5a3 Issue #1 - resolution Larger cells:  less precise spatial fix  line + boundary thickening  features too close overlap - less detail possible Fig. 3.10, 3 rd ed.

4. CS 128/ES 228 - Lecture 5a4 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

5. CS 128/ES 228 - Lecture 5a5 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

6. CS 128/ES 228 - Lecture 5a6 Image depth minimum = 1 bit B/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

7. CS 128/ES 228 - Lecture 5a7 Determining cell values

8. CS 128/ES 228 - Lecture 5a8 Filtering raster data  Neighborhood averaging  Smoothes “holes” and transitions  Other techniques available Chang 2002, p. 203

9. CS 128/ES 228 - Lecture 5a9 Issue #3 - layers in raster format Each layer must be referenced in common coordinates Thematic data can be combined and revised (reclassified)

10. CS 128/ES 228 - Lecture 5a10 Analysis by raster overlay Fig. 6.17, 3 rd ed.

11. CS 128/ES 228 - Lecture 5a11 Lack of spatial registration

12. CS 128/ES 228 - Lecture 5a12 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”

13. CS 128/ES 228 - Lecture 5a13 Issue #4 – mosaicking rasters http://www.microimages.com/featupd/v57/mosaic/

14. CS 128/ES 228 - Lecture 5a14 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

15. CS 128/ES 228 - Lecture 5a15 Mosaicking: adjusting color values Histogram matching: Computer compiles histogram of color (or gray) values in 1 tile 2 nd tile’s colors adjusted to match

16. CS 128/ES 228 - Lecture 5a16 Raster data editing

17. CS 128/ES 228 - Lecture 5a17 Clip to rectangle...

18. CS 128/ES 228 - Lecture 5a18 … vs. clip to shapefile

19. CS 128/ES 228 - Lecture 5a19 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)