Raster data

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Raster data
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Where do we get raster data? Four sources

• Data that are collected in a raster format (e.g.,
  satellite data)
• Data in vector format converted to raster format
• Data in a paper map converted to raster format
• DRG
• Converted into a tessellation database
• Interpolating data from points

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One satellite data

• Example Landsat Thematic Mapper (TM) data from
  USGS

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Multispectral

• Multispectral meaning that each cell has more
  than one value (different sections of the
  electromagnetic spectrum) associated with it
  (these are called bands)

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Bands and Resolution

• Fixed spatial resolution (either 30 meters or 120
  meters) depending on the band

Landsats 4-5 Wavelength (micrometers) Resolution
(meters) Band 1 0.45-0.52 30 Band 2
0.52-0.60 30 Band 3 0.63-0.69 30 Band
4 0.76-0.90 30 Band 5 1.55-1.75 30
Band 6 10.40-12.50 120 Band 7 2.08-2.35
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What can we do with the bands?

• Band 1 penetrates water for bathymetric mapping
  along coastal areas and is useful for
  soil-vegetation differentiation and for
  distinguishing forest types.
• Band 2 detects green reflectance from healthy
  vegetation, and
• Band 3 is designed for detecting chlorophyll
  absorption in vegetation.
• Band 4 data is ideal for detecting near-IR
  reflectance peaks in healthy green vegetation and
  for detecting water-land interfaces.
• The two mid-IR red bands on (bands 5 and 7) are
  useful for vegetation and soil moisture studies
  and for discriminating between rock and mineral
  types.
• The thermal-IR band on (band 6) is designed to
  assist in thermal mapping, and is used for soil
  moisture and vegetation studies.

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False color

• Bands 4, 3, and 2 can be combined to make
  false-color composite images where band 4
  represents the red, band 3 represents the green,
  and band 2 represents the blue portions of the
  electromagnetic spectrum. This combination makes
  vegetation appear as shades of red, brighter reds
  indicating more vigorously growing vegetation.
  Soils with no or sparse vegetation range from
  white (sands) to greens or browns depending on
  moisture and organic matter content. Water bodies
  will appear blue. Deep, clear water appears dark
  blue to black in color, while sediment-laden or
  shallow waters appear lighter in color. Urban
  areas appear blue-gray in color. Clouds and snow
  appear bright white. Clouds and snow are usually
  distinguishable from each other by the shadows
  associated with clouds

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False Color Example
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False Color example
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False Color example
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Remote Sensing and Plants

• Many natural surfaces are about equally as bright
  in the red and near-infrared part of the spectrum
  with the notable exception of green vegetation.
• Red light is strongly absorbed by photosynthetic
  pigments (such as chlorophyll a) found in green
  leaves, while near-infrared light either passes
  through or is reflected by live leaf tissues,
  regardless of their color.
• This means that areas of bare soil having little
  or no green plant material will appear similar in
  both the red and near-infrared wavelengths, while
  areas with much green vegetation will be very
  bright in the near-infrared and very dark in the
  red part of the spectrum.

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Example

• To illustrate how this works, we picked three
  kale leaves and placed them on a bare soil.
• One of the leaves was yellow with just a spot of
  pale green. The other two leaves were a healthy
  green. The first image below is a digital photo
  taken with a red filter, so only red light was
  detected. Notice that the two green leaves are
  very dark, as only about 4 of the red light is
  reflected from these leaves. The yellow leaf
  appears much brighter since there is no
  chlorophyll to absorb red light.

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Red Image
Near Infra-red Image
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• Since these images are 8-bit digital, every dot
  in the picture corresponds to a number from 0 to
  255.
• Black 0
• White 255
• This is a raster data form upon which we can
  perform numeric operations. For example, if you
  subtract a white pixel from a white pixel, that
  would be 255 255 0, or a black pixel. If you
  subtract a black pixel from a white pixel, that
  would be 255 0 255, or a white pixel.

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Back to the plants

• So, if we subtract the red light image from the
  near-infrared image, everything that has about
  the same brightness level in the two wavelengths
  becomes dark, and everything that is brighter in
  the near-infrared becomes light.
• The image on the next slide is the result of
  subtracting the red image from the near-infrared
  image plus a bit more mathematical trickery
  discussed later.
• Notice that even the ribs of the green leaves
  disappear since there is no chlorophyll in that
  part of the leaf. The little patch of green on
  the mostly yellow leaf is about the only part of
  that leaf still visible. The soil and stones
  have completely disappeared.

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  Vegetation Index Image (SAVI)  
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Remote Sensing Measures

• Satellites and digital cameras measure light as a
  digital number (DN).
• If these sensors are calibrated, the DN can be
  converted to radiance, which is the amount of
  light coming from a surface.
• If the amount of irradiance (incoming light) is
  known, then the surfaces reflectance can be
  calculated as the radiance divided by the
  irradiance plus compensation for atmospheric
  clarity at the time the image is acquired.
• Reflectance is by far the hardest value to get,
  but it is the most valuable since it is a
  characteristic of the surface itself and not
  affected by the intensity of light shining on it.

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RVI

• In the following equations, p (the Greek letter
  rho) stands for reflectance. NIR is
  near-infrared light, and Red is red light. All
  of the equations can and have been used with DNs
  and radiances instead of reflectances. However,
  vegetation indices that are not calculated from
  reflectance may not give consistent results when
  used to compare images of the same area taken on
  two different dates.
• The Ratio Vegetation Index (RVI)

 
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What RVI Means

• This index divides one wavelength by the other,
  instead of subtracting as in our earlier example.
  
• The result is similar however, with the
  denominator getting smaller and numerator getting
  larger as the amount of green vegetation
  increases.
• Typical ranges are a little more than 1 for bare
  soil to more than 20 for dense vegetation.

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NDVI

• This is the most commonly used index for
  satellite imagery.
• The difference in reflectances is divided by the
  sum of the two reflectances.
• This compensates for different amounts of
  incoming light and produces a number between 0
  and 1. The typical range of actual values is
  about 0.1 for bare soils to 0.9 for dense
  vegetation.
• NDVI is thought to be more sensitive to low
  levels of vegetative cover, while the RVI is more
  sensitive to variations in dense canopies.
• Normalized Difference of Vegetation Index

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With the same data (NDVI)
Normalized difference vegetation index
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Vector conversion

• Data that are in another format (either vector or
  paper map) and need to be converted to a raster
  format
• Continuous data
• Examples elevation, temperature
• Square grid tessellation also called raster

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Tessellation Models

• Location-based spatial data model process of
  dividing an area into smaller, contiguous tiles
  with no gaps between them
• Types
• regular and irregular
• Uses continuous surfaces
• Pros easy to implement and manipulate
• Cons high data storage, output not cartographic
  quality

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Spatial and Attribute Data

• Combined in a single file
• Unlike the scanned maps, they can be searched

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Tessellation Models

• Regular

Most common
Rarely used
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Tessellation models
Regular grid
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Data

• Rows and columns containing the attribute value
  associated with each data layer
• The row/column location of the data value
  represents the spatial position
• Exact geographic position is typically
  established with header information before the
  rows and columns of data
• Also need knowledge of what the values represent
  (e.g., elevation in meters) typically part of
  the metadata

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Rows and Columns
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Geographic Position
origin
orientation
size of each cell
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Sample data
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Each cell has a value
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Data File
Origin (x,y) Ymax (x,y) Row,col Cell size
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Tessellation models
Hexagonal mesh
Primary advantage over square grid tessellation
is distance measurements. Important in
applications that need to spread distances evenly
- e.g., spread of forest fires
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Distance between adjacent cells?
Example modeling the spread of a fire from one
cell to the next adjacent cell.
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Distance measurements between cells is the same
in the hexagon model
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Land use in vector format
To convert it, we need to decide what size each
cell needs to be. How do we decide? Minimum
mapping unit and spatial resolution.
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Sort the database
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Minimum mapping unit
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Better
This would give us a 2 m cell size
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Default settings
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Resulting data
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Resulting data
755 (default)
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200 meters
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100 meters
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10 meters
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Scanned Maps (1 of 4 sources)

• Scanned map as a photograph
• The value of each cell represents the color on
  the map needs to be interpreted the way a
  paper/analog map is interpreted

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Digital Raster Graphic (DRG)
There is typically another file linked with the
DRG, so that the geographic position of the
graphic is known
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MapQuest
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Maps or Images??
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Summary of scanned maps

• Have the characteristics of an analog map in that
  the location information and the attributes are
  stored as a visual product
• No queries can be made based on the database