Remote Sensing of Wetlands

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Remote Sensing of Wetlands Josh Kauffman
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Brief Outline

• Why study wetlands?
• Remote Sensing benefits/drawbacks
• The Landsat program
• Aerial Image Spectroscopy
• The future

http//commons.wikimedia.org/wiki/FileWetlands_Ca
pe_May_New_Jersey.jpg
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Why Study Wetlands?

• Wetlands are ecologically vital areas which
  provide habitat for diverse organisms from plants
  to fish to birds.
• Wetlands also filter out pollutants from rivers
  and streams used by people.
• They also act as buffer zones, protecting the
  inland from storms and flooding.

http//walton.ifas.ufl.edu/images/hurricane-ivan.j
pg
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Benefits of Studying Wetlands Remotely
http//www.calistogatroop18.org/photos/2006101420
Anderson20Marsh20Hike20353.jpg

• Salt marshes and other habitat are difficult or
  impossible to traverse on foot. Remote sensors
  greatly reduce the need for painstaking
  groundwork.
• NASA's Landsat satellites and airplanes fitted
  first with cameras, then Multi Spectral Scanners
  (MSS), and later Thematic Mappers (or TM) and
  Enhanced Thematic Mappers (ETM) with LiDAR can
  capture vegetation even down to species in some
  cases. These technologies can also identify
  areas of water, leaf greenness, and exposed soil.
  Time series can be used to identify habitat loss,
  soil erosion, and water inundation.

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The Landsat Program

• In 1972 the Landsat I satellite launched into a
  sun-synchronous, 900 kilometer-high orbit with a
  99.2 degree inclination for near global coverage,
  a period of 103 minutes, and a repeat pattern
  every 18 days. Landsat II and later III took over
  while following the same orbit parameters until
  the launch of Landsat IV which packed newer
  technology (1).

http//upload.wikimedia.org/wikipedia/en/4/41/Land
sat1.jpg
Equipped with Multispectral Scanner Systems,
these satellites were best for very large wetland
studies due to low resolution and shaky geometric
precision(2).
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Multispectral Scanning Systems

• The MSS systems on the Landsat satellites are
  passive sensors that measure radiation
  perpendicular to the orbital path via a rotating
  mirror which passes light reflected off the Earth
  into 24 sensors (6 for each band). The four bands
  measured are the 500-600, 600-700, 700-800, and
  the 800-1000 nanometer spectra. Red, Green, and
  Blue are bands 7, 5, and 4 respectively. With a
  pixel size of 68m x 83m, the MSS system is only
  really useful for large scale land-use coverage
  (2).

http//www.geology.iastate.edu/gcp/satellite/image
s/image36.gif
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The Landsat Program cont'd.

• The introduction of Landsat IV in 1982 and
  Landsat V in brought about a new technology
  called Thematic Mapping. Greatly increased
  resolution allowed scientists to map and study
  wetlands ecology as never before.
• These two satellites were launched into a
  sun-synchronous 705 km, 98.2 degree of
  inclination with a 99 minute period and repeat
  coverage every 16 days. Unfortunately the U.S.
  Government privatized satellites at this time
  inflating data prices and causing scientists to
  stop collecting data. This led to a loss of very
  valuable satellite imagery because the data went
  unstored during this period (3).

http//www.geog.ucsb.edu/jeff/115a/history/landsa
t45.gif
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Thematic Mapping

• Thematic Mapping on Landsat IV and V operated in
  a whisk-broom method with a mirror oscillating
  left and right. Secondary mirrors fill in the
  gaps left by this method(5). Data is collected
  across 7 bands. Bands 1-4 are the visible
  spectrum, band 5 can detect leaf/soil moisture.
  Band 6 is an infrared thermal imager, and band 7
  detects moisture content as well.

http//geology.com/novarupta/maps/landsat- novarup
ta-region-large.jpg
The TM instrument has allowed scientists to reach
30 meter resolution which in wetlands study is
very important (though greater resolution is
always better). This is 2.5 times better than the
MSS resolution. TM also has better geometric
stability.
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Landsat 7 and ETM

• Landsat 6 failed during launch in 1993, and in
  1995 Landsat 7 took over. In the same orbit as
  Landsat 4 and 5, this new satellite carried a
  payload that included a very precise radiometric
  calibration unit, an onboard data collector, and
  the Enhanced Thematic Mapper with a panchromatic
  band achieving 15 meter resolution (multispectral
  30 meter resolution)(6).

http//landsat.gsfc.nasa.gov/images/lg_jpg/l7satel
lite.jpg
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Enhanced Thematic Mapping

• Enhanced thematic mapping is better for wetlands
  evaluation than TM because of the greater spacial
  resolution, better instrument calibration, and
  higher geometric fidelity thanks to GPS systems.
• Both technologies are used to study aspects of
  wetlands such as vegetation cover, high water
  mark, habitat loss/fragmentation, and water
  quality. The biggest use of these technologies is
  studying land-use and change over time.

Side by side comparison of TM (left) and ETM
(right) images of harvest time in Nangrong,
Thailand. From http//www.cpc.unc.edu/projects/na
ngrong/data/spatial_data/remote_sensing/satellite_
imagery/ (7)
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IKONOS Satellite

• The IKONOS-2 commercial satellite has brought
  space-based spectral imaging resolution down to
  just 3.2 meters (0.82 m panchromatic!). This
  provides an incredible opportunity to gather data
  not just on large tracts of wetland/estuarine
  habitat, but also within-habitat variations and
  features.
• 681 kilometer, 98.2 degree of inclination orbit
  and a repeat time of around 4 days (8).

http//borrowedearth.files.wordpress.com/2008/05/m
angrove0459sm.jpg
Can be used to classify mangrove communities at a
very high resolution by assigning unique spectral
identities to vegetation cover and use that
information to predictively analyze unexplored or
inaccessible mangrove forests.
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Airborne Visible/InfraRed Imaging Spectrometer

• The latest in remote sensing of wetlands is the
  use of AVIRIS and similar systems. These consist
  of a spectrometer array attached to an airplane
  flown at extremely high altitudes. NASA flies
  this system on a U-2 plane at 20,000 meters (9).
• The technology essentially a plane-mounted
  version of the thematic mapper of the Landsat
  satellites. Though with 224 simultaneous bands
  covering 400-2500 nanometers (9).
• Gets great resolution which varies with height
  above ground
• More predictive of community composition than ETM.

http//aviris.jpl.nasa.gov/html/aviris.overview.ht
ml
One study successfully used the AVIRIS system to
produce a vegetation map of the Everglades down
to individual species with a roughly 66 accuracy
(very good at this point in time)(10).
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CAO Systems

• The Carnegie Airborne Observatory has developed a
  system like AVIRIS, but also incorporates a LiDAR
  to map beautifully at resolutions of 0.1 to 4
  meters depending on the research. With up to 288
  channels in the visible and near-infrared, and a
  high quality digital camera this system can
  create incredibly detailed three-dimensional maps
  of the target phenomena. This stereo image is
  incredibly useful in wetlands research where
  rugged terrain and inaccessibility is a deciding
  factor for study design (11).

One study used a hybrid CAO-AVIRIS system to map
invasive plant species in Hawaii with incredible
sensitivity. Using the LiDAR and hyperspectral
imaging they could study not only canopy
vegetation, but 3-dimensional vegetation with an
identification accuracy better than 93 (12).
http//dgeweb.stanford.edu/caoweb/uploads/kohala_p
uu-1.jpg
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Looking Ahead

• In its current state, space-based remote sensing
  of wetland ecosystems is more cost-effective but
  less predictive in its modeling than
  plane-mounted systems like AVIRIS and CAO. This
  may be due to Rayleigh scatter from the
  atmosphere, but either way it seems that
  airplane-based systems are the future of
  hyperspectral imaging technology. Carnegie
  Airborne Observatory is currently at work on what
  they are calling Airborne Taxonomic Mapping
  System (AtoMS) which will greatly increase the
  system's overall resolution and incorporate rapid
  pulse-rate LiDAR (6).

With great resolution comes great responsibility
data load. Will satellites and land-based
stations be equipped to transmit huge data files
in a timely fashion without compressing images?
How can models of wetland ecosystems be made more
predictive of community structure? As satellites
achieve greater and greater resolution, what
happens to personal privacy? Is increasing
resolution of current systems the most
cost-effective approach?
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Citations

• 1. Williams D. Landsat I. National Aeronautics
  and Space Administration 2009 Dec 09, cited
  2009 Nov 29 . Available from
  http//landsat.gsfc.nasa.gov/about/landsat1.h
  tml
• 2. Multispectral Scanner (MSS). United States
  Geological Survey 2009 April 16, cited 2009
  Nov 30. Available from http//eros.u
  sgs.gov//Find_Data/Products_and_Data_Available/MS
  S
• 3. S. Johnston and J. Cordes, Public good or
  commercial opportunity? Case studies in remote
  sensing commercialisation. Space Policy 19
  (2003), pp. 2331.
• 4. The Thematic Mapper. National Aeronautics and
  Space Administration 2009 Dec 09, cited 2009
  Nov 29. Available from http//landsat.gsfc.nasa.
  gov/about/tm.html
• 5. Thematic Mapper (TM). United States
  Geological Survey 2009 April 16, cited 2009
  Nov 29. Available from http//eros.
  usgs.gov//Find_Data/Products_and_Data_Available/T
  M
• 6. Williams D. Landsat 7. National Aeronautics
  and Space Administration 2009 Dec 09, cited
  2009 Nov 29. Available from http//lan
  dsat.gsfc.nasa.gov/about/landsat7.html

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Citations (2)

• 7. Satellite Imagery 1970s-2000s. University of
  North Carolina Populations Center 2004 April
  05, cited 2009 Nov 30. Available from
  http//www.cpc.unc.edu/projects/nangrong/data/spat
  ial_data/remote_sensing/satellite_imagery
• 8. Mumby, P.J. And Alasdair Edwards 2002,
  Mapping marine environments with IKONOS
  imagery enhanced spatial resolution can deliver
  greater thematic accuracy Remote Sens.
  Environ. Oct 2002 (23) 248257
• 9. AVIRIS Concept. NASA Jet Propulsion
  Laboratory 2007 Oct 30, cited 2009 Nov 30.
  Available from http//aviris.jpl.nasa.gov/html/av
  iris.concept.html
• 10. Hirano, A., Madden, M., Welch, R. 2003.
  Hyperspectral Image Data for Mapping Wetland
  Vegetation Wetlands June 2003 (2) 436-448
• 11. CAO Systems. Carnegie Airborne Observatory
  cited 2009 Nov 30. Available from
  http//cao.stanford.edu/?pagecao_systems
• 12. Asner et al 2008, Invasive species detection
  in Hawaiian rainforests using airborne imaging
  spectroscopy and LiDAR Remote Sensing of
  Environment 112 (2008), pp. 19421955.