Image%20Data%20Sources%20and%20Georeferencing%20of%20Imagery:%20The%20use%20of%20Remote%20Sensing%20in%20GIS%20Applications

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Image Data Sources and Georeferencing of Imagery
The use of Remote Sensing in GIS Applications

• Christopher M. U. Neale
• Utah State University
• GIS in Water Resources

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Images in GIS and Water Resources

• Images are a raster data source which can be
  analyzed to extract hydrologic parameters and
  variables for use in water resources
  applications. Sources of imagery can be
• Black and white or color digital imagery from
  aerial photography either from metric digital
  cameras or scanned negatives
• Multispectral imagery from satellite or airborne
  sensors
• Thermal infrared imagery from satellite or
  airborne sensors
• Active and Passive Microwave Imagery

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Remote Sensing

• Obtaining information about an object or a
  surface without coming into physical contact with
  it.
• Basis Interpretation of emitted or reflected
  electromagnetic radiation from a surface
• Requires knowledge of the physics behind the
  interaction of radiation at certain wavelengths
  with a surface
• Sensors and instruments
• Active and Passive Systems (microwave)

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Raster Data Format
John Jensen Remote Sensing of Environment, 2000
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Electromagnetic Spectrum and Atmospheric
Transmission
Atmospheric windows are regions of high
transmission resulting from low absorption by
atmospheric gases. Satelite instrument spectral
bands are usually selected within the atmspheric
window regions
John Jensen Remote Sensing of Environment, 2000
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Resolution of a sensor

• Spatial resolution is related to the
  instantaneous field-of-view of each pixel. It is
  the size of the footprint or pixel for which
  radiance is being integrated
• Spectral resolution is related to the number of
  spectral bands of a radiometers or imager as well
  as the spectral bandwidth
• Radiometric resolution is related to the
  digitization of the radiance into a digital
  number. An 8 bit digitization will result in 28
  or 256 gray scale levels (0 to 255). A 10 bit
  digitization (210) will have a higher radiometric
  resolution, or 1024 gray scale or digital number
  levels

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• Radiometric resolution of an imaging sensor

John Jensen Remote Sensing of Environment, 2007
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Resolution of the Landsat Sensors
John Jensen Remote Sensing of Environment, 2007
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• Pixel Spatial Resolution of Different Sensors
• USU Airborne 1.5 meters Landsat TM
  30 meters

Near-infrared band on red color, red band on
green color, green band on blue color of RGB
display
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• Pixel Spatial Resolution of Different
  Sensors
• USU Airborne 1.5 meters Landsat TM
  30 meters

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• Pixel Spatial Resolution of Different
  Sensors
• Displayed in Full
  Resolution
• USU Airborne 1.5 meters Landsat TM
  30 meters

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• Pixel Spatial Resolution of Different
  Sensors
• Thermal IR Band
• Landsat TM7 60 meters USU Airborne 6
  meters

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Spectral bands of Remote Sensing Instruments

• Bandwidths are selected based on knowledge of
  interaction of light with known earth surfaces
• Factors that affect reflectance of soils mineral
  composition, organic matter, water content,
  particle size, roughness, texture
• Factors that affect reflectance of vegetation
  chlorophyll and pigment content, water content,
  spongy mesophyll, canopy geometry and leaf
  orientation
• Factors that affect reflectance from water
  surfaces sediment content, organic matter
  content, algae and chlorophyll content, substrate
  reflectance, depth to substrate
• Snow and Ice
• Clouds

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Reflectance Property of Vegetation

• Typical Leaf Spectra
• Reflectance of a leaf is low in visible
  wavelengths due to absorption by pigments, high
  in near infrared due to high reflectance and
  transmittance of the spongy mesophyl structure
  and lower in wavelengths beyond 1.3 µm due to
  absorption by water

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Reflectance Property of Vegetation

• Typical Leaf Spectra
• Different mechanisms control the absorptance and
  reflectance in the visible and near-infrared

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Reflectance Property of Leafs

• Typical Leaf Spectra
• Beyond 1.3 µm, absorption by water in plant
  tissues controls the reflectance

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Reflectance Property of Vegetation and water

• Absorption by Pigments -
  Algae
• Reflection in the visible part of the spectrum is
  controlled by leaf pigments mainly chlorophyll
  (green). Other pigments are carotene (orange),
  xanthophyll (yellow).

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Reflectance Property of Soils

• Soil Moisture Effects
• Effects are more pronounced in clay soils

John Jensen Remote Sensing of Environment, 2007
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John Jensen Remote Sensing of Environment, 2007
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A remote sensor from space is usually seeing
a combination of vegetation and soils over
landSpectral Reflectance of Soil and Vegetation

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Vegetation Indices

• Vegetation Indices are mathematical combinations
  of reflectance of a surface in different spectral
  bands with the intent of obtaining a parameter
  that is more sensitive to growing vegetation than
  the individual bands by themselves

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Some early Vegetation Indices

• Ratio Vegetation Index NIR/Red
• Normalized Difference Vegetation
• Index (NDVI)
• NDVI (NIR Red) / (NIR Red)
• Varies between 0 and 1.
• Can become negative on certain soils

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Growth of a Corn Canopy

• Normalized Difference
• Vegetation Index (NDVI)
• NDVI (NIR-Red)/(NIR Red)

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The NDVI is very sensitive to growing vegetation
from bare soil up to a Leaf Area Index (LAI) of
around 3. It then becomes insensitive to
additional leaf layers. Thus the NDVI is good
for monitoring sparse canopies.The band ratio
will continue to be sensitive beyond a leaf area
of 3, as the near-infrared band will continue to
increase, being more suitable for dense canopies.

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Soil and Vegetation in 2-D Spectral Space
Soil reflectance will plot along the soil line
with vegetation plotting away from the line in a
distribution that looks like a tasseled cap
John Jensen Remote Sensing of Environment, 2007
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Kauth-Thomas Tasseled Cap Transformation

• Linear transformation of the Landsat
  Multispectral Scanner (MSS) or Thematic Mapper
  (TM) bands to enhance certain characteristics in
  the principal planes of the transformation
• Brightness is the soil line
• Greeness is the vegetation line
• Wetness is the moisture line

John Jensen Remote Sensing of Environment, 2007
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Select VIs listed in table fromJensen (2007)

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Select VIs listed in table fromJensen (2007)

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Relationship between a vegetation index (OSAVI)
and leaf area index (LAI), vegetation water
content of corn and soybeans
Anderson, Neale, Li, Norman, Kustas, Jayanthi,
and Chavez, 2004. Remote Sensing of Environment,
92, 447-464.
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Spatially Distributed Leaf Area Index, Canopy
Height, Fraction of Vegetation Cover
Leaf Area Index Airborne (1.5 meter)
Satellite (30 meter)
Anderson, Neale, Li, Norman, Kustas, Jayanthi,
and Chavez, 2004. Remote Sensing of Environment,
92, 447-464.
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• Landsat Orbital Tracks in relation to its
  equatorial crossing every 103 minutes. The
  satellite completes 14 orbits per day

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Geo-stationary satellites are in an orbit over
the equator at 40000 km away from earth
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• NOAA AVHRR Polar Orbiter

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• Examples of NOAA AVHRR Satellite tracks
• Equatorial Crossing either 230 pm and 230 am
  or 730 pm and 730 am. Sun-synchronous polar
  orbiter

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• SPOT Satellite

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• Because the SPOT HRV instrument allows
  off-nadir viewing, it can be used to acquire
  multitemporal imagery thus increasing its return
  frequency

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• Indian Remote Sensing Satellite

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• Recent high-resolution satellite instruments

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• MODIS is a scientific remote sensing
  instrument on the EOS platforms

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Application of Image Products

• Digital Orthophotos
• A digital orthophoto is an image map based on
  aerial photography projected over the terrain so
  that it correctly represents the area from a
  vertical or nadir view
• Source imagery can be a digital photo from a
  metric camera or obtained from scanning the
  negative from a metric film camera
• A digital elevation model is a by-product of the
  process of rendering an orthophoto

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Digital Aerial Photograph The basic input in the
production of orthophoto
Results from scanning the film negative and using
a special software to produce a color digital
positive
120,000 scale
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Diapositive of the original photo Used in
analytical stereoplotters in the production of
orthophotos and updating of topographic maps
Diapositives are transparent photos like a slide
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Analytical Stereo Plotter Leica SD2000
Diapositives of sequential aerial photographs
with 60 overlap are placed side -by-side on the
machine to view the terrain in 3-D
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Digital Orthophoto with Contour Lines Dominican
Republic
Produced through softcopy photogrammetry
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Digital Orthophoto at 15000 scale
The orthophoto is a digital photograph with
geographic coordinates that has been adjusted to
the terrain so that areas can be
correctly measured
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Detail of a Digital Orthophoto with Contours
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Application Example Development of a Digital
Water Users Database

• It is a database containing information on all
  the property owners and irrigation water users
  within an irrigation project. It consists of
• An up-to-date property boundary map containing
  information on property owner or water user
• Information on total area and irrigated area of
  each property
• Information on canal system that delivers water
  to the property
• gt Because of the geographic and distributed
  nature of the information as well as the
  requirement to have an associated database, USU
  opted in using Geographic Information System
  (GIS) technology to develop this product.

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Digital Orthophoto at 15000 scaleThe map base
for the water users geographical database
The orthophoto is a digital photograph with
geographic coordinates that has been adjusted to
the terrain so that areas can be
correctly measured
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Printed Orthophoto at 14000 scale for field
verification
Field brigades used printed and laminated maps yo
identify the property boundaries together with
the land owner or a local facilitator that has a
good knowledge of the irrigation system
(president of a water association, ditch rider)
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Survey Form used in Information Gathering
The cartographers conduct the survey with the
property owner to obtain basic information on
the water user (complete name, nickname,
address) and on the property (water
distribution problems, crops planted, salinity or
drainage problems)
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On-screen digitizing of the parcel boundaries
using ArcINFO with the digital ortho as a backdrop
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Some characteristics of the digital water users
database

• Union of the geographic layer containing the
  property boundaries with the information in the
  surveys
• The geographic coordinates come from the digital
  orthos
• The property area is exact
• The database is complete because all properties
  within the command area of a canal or irrigation
  systm are considered
• Can be readily and easily updated
• The property boundaries can be updated if a
  consolidation or division of properties ocurrs

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Map Objects Application to Manage the Digital
Database Search for a Water User
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Digital Database
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Information can be easily updated
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Hydro-Agricultural Information System Marcos A.
Cabral
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Irrigation Water Control Structures for Operation
and Maintenance
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Application Classification of Multispectral
Imagery to Obtain Land cover and Vegetation Types
of Watersheds

• Vegetation cover affects the interception of
  precipitation and its distribution (snow)
• Different vegetation types consume water through
  evapotranspiration (ET) at different rates
• Thus vegetation cover affects the hydrologic
  response of a watershed
• Hydrologic response units are areas where soil
  infiltration and water holding capacity, runoff,
  ET, relief, vegetation cover etc. combine
  spatially to result in a similar hydrologic
  response to precipitation inputs

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Vegetation cover map obtained from the
classification of an Aster multispectral image
From Chrysoulakis et al, 2003
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Application Wetland delineation from high
resolution multispectral imagery

• Wetlands have an important function for water
  quality preservation and wildlife habitat
  maintenance
• Delineation of wetlands can be obtained through
  the classification of multispectral imagery
• Wetlands in semi-arid regions have small scale
  variability requiring high-resolution imagery
• Large scale wetlands such as the Pantanal,
  Everglades cover very large areas and scale of
  variability is large and can be mapped using
  satellite imagery

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Sample of Wetland Types
Upland Grasses
Marsh
Playa
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Example of Emergent Marsh Wet Meadow
Spike Rush
Emergent Marsh
Salt Grass and Other wet meadow species
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Final Recoded Image Cut to Property Boundaries
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Area Statistics by Wetland Class (acres)
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Changes in TimeMay 13, 2003
April 27, 2007
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Changes in Time, Detail May 13, 2003
April 27, 2007
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Timing of Image Acquisition April 27, 2007
May 30, 2007 June 27, 2007
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Landsat Thematic Mapper Images of the
Pantanal180 x 180 KmDry Season
Wet
SeasonTM7, August 2002
TM5, November 2004
The Pantanal is the largest inland wetland in the
world, located in central west Brazil
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Changes in Time, Detail Dry Season
Wet
SeasonTM7, August 2002
TM5, November 2004
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Classified Vegetation Map
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Classified Vegetation Map withRoads, fence lines
and small water body locations
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Livestock depredation by jaguars in the southern
Pantanal
GIS work and slides by Sandra Cavalcanti
New York, 2005
a preliminary assessment of the spatial
distribution of kills
Advanced Training in GIS for Wildlife
Conservation
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Can we predict areas where jaguars are more prone
to kill cattle?
Adapt cattle management accordingly
Minimize economic losses
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Other Applications with Remote Sensing Inputs

• Passive microwave remote sensing of snow extent,
  snow depth and water equivalent gt large areas
  and relatively flat like US and Canadian central
  plains
• Active and Passive remote sensing of surface soil
  moisture
• Thermal infrared remote sensing Surface energy
  balance and evapotranspiration

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Where to go for this neat stuff

• In Utah
• IRDIAC http//earth.gis.usu.edu/

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Image and Image Product Data Sources

• IRDIAC Landcover from GAP analysis

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Image and Image Product Data Sources

• UTAH AGRC Products http//agrc.its.state.ut.us/

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Image and Image Product Data Sources

• Utah AGRC

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Image and Image Product Data Sources

• TEXAS http//magic.csr.utexas.edu/index.htm

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Image and Image Product Data Sources

• TEXAS MAGIC NAIP Imagery

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Image and Image Product Data Sources

• TEXAS http//www.tnris.state.tx.us/StratMap.aspx?
  layer126

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Purchasing Landsat Imagery

• USGS http//landsat.usgs.gov/

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Purchasing Landsat Imagery

• GLOVIS http//glovis.usgs.gov/

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GLOVIS Image Selection Window
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Need Imagery from other Satellite Sensors?

• GOOGLE IT!
• Next chapter
• Image rectification in ArcGIS
• Remote Sensing and GIS Application Energy
  balance and evapotranspiration