CS 128/ES 228 - Lecture 5a 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 - resolutionLarger 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 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

CS 128/ES 228 - Lecture 5a 7

Determining cell values

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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.

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Lack of spatial registration

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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 tilesEx. 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 valuesHistogram 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)