The raster data model divides a geographic area into a grid of cells or pixels that represent attribute values. Each cell has a specific resolution depending on the real world area it represents. Common types of raster data include satellite imagery, digital elevation models (DEMs), digital orthophotos, scanned maps, and graphic files. Raster data is stored using different structures like cell-by-cell encoding, run-length encoding, and quadtree encoding to reduce storage requirements. Raster data can be projected and integrated with vector data for analysis and display in GIS.

1. Raster Data Model
(Chang’s Chapter 7)
Elements of the Raster Data Model
Raster model divides the area into grid cells
or pixel.
Each grid cell is filled with the measured
attribute values.
It can represent points, lines and area (Figure
7.1).
Resolution depends on real world area
represented by each grid cell.
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2. Raster Data Model
The larger the area represented, the lower
the resolution of data.
Cells are identified by their positions in the
grid.
Raster data is geo-referenced by:
• Real world coordinates of the reference
point
• Cell size in real world distance
• Use the upper-left or lower-left corner of
grid as the reference point.
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IDRISI Metadata
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3. Raster Data Model
Storage requirement is high.
Ex: If the area is 100 km x 100 km and cell
size is 10 m. It needs 10,000 rows x 10,000
columns or 100,000,000 pixels.
If one byte is used per pixel, it requires 100
MB storage
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Types of Raster Data
1. Satellite Imagery
Remotely sensed satellite data are
recorded in raster format.
Spatial resolution varies:
• 30 m. for Landsat 4 and 5 (use the
Thematic Mapper scanner), and
Landsat 7 (use Enhanced Thematic
Mapper-Plus, ETM+ scanner).
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4. • 20 m. for SPOT images (Multi-spectral
sensor), and 10 m. for SPOT
Panchromatic sensor).
• 4 m. and 1 m. for IKONOS Multi-spectral
and Panchromatic images respectively.
The pixel value in a satellite image represents
light energy reflected or emitted from the
Earth’s surface.
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The measurement of light energy is based on
electromagnetic spectrum.
Panchromatic images are comprised of a
single spectral band.
Multi-spectral images have multiple bands.
– Landsat TM has 7 band.
Land use, land cover and hydrography can
be classified from image processing system.
Satellite images can be diaplayed in black
and white or in color.
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5. Landsat TM Bands
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Composite Color Images
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6. 2. Digital Elevation Models (DEM)
DEM consists of an array of uniformly spaced
elevation data.
DEM are produced from:
– a stereoplotter and aerial photograph with
overlapping areas.
– Satellite imagery such as SPOT stereo
model using special software.
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3. Digital Orthophotos
Prepared from aerial photograph or other
remotely sensed data.
Displacement caused by camera tilt and
terrain relief has been removed.
They are geo-referenced and can be
registered with topographic and other maps.
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7. Digital Orthophoto
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4. Binary Scanned Files
Scanned image containing values of 1 and 0.
Maps to be digitized are typically scanned at
300 or 400 dpi (dots per inch).
5. Graphic Files
Maps, photographs and images can be stored
as digital graphic files.
– e.g. TIFF (Tagged Image File Format), GIF
(Graphic Interchangeable Format), JPEG
(Joint Photographic Exports Group), etc.
– GeoTIFF is a geo-referenced version of
TIFF format.
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8. Raster Data Structure
Refers to storage of raster data so that
it can be processed by the computer.
Cell-by Cell Encoding
A raster model is stored as a matrix.
Its cell values are written into a file by
row and column. (Figure 7.2)
Ideal to store the cell values that
change continuously, e.g.,DEM.
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9. For multi-spectral satellite image, each
cell has more than one value, data are
stored in either of the following formats.
– The band interleaved by line (.bil):
this method stores the 1st value of
every row sequentially, followed by
the second value of every row, and so
on in one image.
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Multi-band Satellite Data Structure
.bsq
.bil
Figure 7.x
.bip
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10. The Band Sequential (.bsq) method:
stores values of each band sequentially
in one image.
The Band Interleave by Pixel (.bip): each
row of an image is stored sequentially,
row 1 all bands, row 2 all bands, and so
on.
(See Figure 7.x)
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Multi-band Satellite Data Structure
.bsq
.bil
Figure 7.x
.bip
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11. Run-length Encoding
Records the cells by row and by group
Each group includes a cell value and the
number of cells with that value.
If all cells in a row contain the same value,
only one group is recorded, hence save
computer memory.
See Figure 7.3.
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12. Chain Code Method
Represent the boundary of a region by using
a series of cardinal directions and cells.
– Ex: N1 means moving north by 1 cell,
S4 means moving south by 4 cells.
See Figure 7.4
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13. Block Code Method
Uses square blocks to represent the region.
– A unit square represents 1 cell.
– 4-square block represents 2 x 2 cells
– 9-square block represents 3 x 3 cells, and
so on.
Each square block is coded only with the
location of a cell (lower left of the block), and
the side length of the block.
See Figure 7.5
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14. Quad Tree Method
Uses recursive decomposition to divide a grid
into a hierarchy of quadrants. (Figure 7.6).
A quadrant having cells with the same value
will not be sub-divided, and it is stored as a
leaf node.
Leaf nodes are coded with the value
homogeneous quadrant.
A quadrant having different cell values will be
subdivided until a quadrant at the finer level
contains only one value.
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15. This method is efficient for storing and
processing data.
Different raster GIS software use different
method of storing data.
– IDRISI and GRASS use either cell-by-cell
or run length encoding method.
– SPANS uses a quad-tree data structure.
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Data Compression
Refers to the reduction of raster data
volumes.
Run length encoding method may reach 10:1
compression ratio.
TIFF and GIF files use lossless compression
which allows the original image to be
precisely reconstructed.
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16. Data Compression
JPEG files use lossy compression which can
achieve high compression ratios but can not
reconstruct the original image fully.
MrSid (Multi-resolution Seamless Image
Database) has capability of recalling image
data at different resolution or scales and also
can compress a large image.
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Projection of Raster Data
Projected raster data are based on rows and
columns but the rows and columns are
measured in real-world coordinates.
– Ex:
• Rows: 463, Columns: 318, Cell size: 30
m
• UTM coordinates at the lower left corner:
499995, 5177175
• UTM coordinates at the upper right
corner: 509535, 5191065
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17. • The cell in Row 1 and Column 1 at the
upper left corner has UTM coordinates of
499995, 5191035.
Data Conversion
Conversion of vector to raster data is called
rasterization.
Conversion of raster to vector data is called
vectorization. (Figure 7.8)
Both require use of computer algorithms which
most GIS software have.
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18. Integration of Raster and Vector Data
Can take place in data display, data
processing, data conversion, or data analysis.
DEM are input data to extract topographic
features such as contour, drainage network,
watersheds, etc.
Most GIS packages allow simultaneous
display of raster and vector data.
Data conversion must be performed first if the
analysis of both raster and vector data is
required.
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