Transcript Remote Sensing of Wetlands Josh Kauffman Brief Outline      Why study wetlands? Remote Sensing benefits/drawbacks The Landsat program Aerial Image Spectroscopy The future http://commons.wikimedia.org/wiki/File:Wetlan ds_Cape_May_New_Jersey.jpg.

Remote Sensing of
Wetlands
Josh Kauffman
Brief Outline
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Why study wetlands?
Remote Sensing
benefits/drawbacks
The Landsat program
Aerial Image
Spectroscopy
The future
http://commons.wikimedia.org/wiki/File:Wetlan
ds_Cape_May_New_Jersey.jpg
Why Study Wetlands?
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Wetlands are ecologically
vital areas which provide
habitat for diverse
organisms from plants to
fish to birds.
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Wetlands also filter out
pollutants from rivers and
streams used by people.
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They also act as buffer
zones, protecting the inland
from storms and flooding.
http://walton.ifas.ufl.edu/images/hurricane-
Benefits of Studying Wetlands Remotely
http://www.calistogatroop18.org/photos/20061014%20Anderson%20Marsh%20Hike
%20353.jpg
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Salt marshes and other habitat are difficult or impossible to traverse on
foot. Remote sensors greatly reduce the need for painstaking groundwork.
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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.
The Landsat Program
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In 1972 the Landsat I satellite
launched into a sunsynchronous, 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/wik
ipedia/en/4/41/Landsat1.jpg
Equipped with Multispectral Scanner
Systems, these satellites were best for
very large wetland studies due to low
resolution and shaky geometric
precision(2).
Multispectral Scanning Systems
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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/images/i
mage36.gif
The Landsat Program cont'd.
http://www.geog.ucsb.edu/~jeff/115a/history/land
sat45.gif
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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.
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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).
Thematic Mapping
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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/landsatnovarupta-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.
Landsat 7 and ETM
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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/i
mages/lg_jpg/l7satellite.jpg
Enhanced Thematic Mapping
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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.
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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/nan
grong/data/spatial_data/remote_sen
sing/satellite_imagery/ (7)
IKONOS Satellite
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The IKONOS-2 commercial
satellite has brought spacebased 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.
http://borrowedearth.files.wordpress.com/2
008/05/mangrove0459sm.jpg
681 kilometer, 98.2 degree of
inclination orbit and a repeat time Can be used to classify mangrove
communities at a very high resolution
of around 4 days (8).
by assigning unique spectral identities
to vegetation cover and use that
information to predictively analyze
unexplored or inaccessible mangrove
forests.
Airborne Visible/InfraRed Imaging
Spectrometer
http://aviris.jpl.nasa.gov/html/aviris.overvi
ew.html
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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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).
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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).
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Gets great resolution which
varies with height above ground
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More predictive of community
composition than ETM.
CAO Systems
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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/caow
eb/uploads/kohala_puu-1.jpg
Looking Ahead
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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?
Citations
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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.html
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2. Multispectral Scanner (MSS). United States Geological Survey; [2009 April 16,
cited 2009 Nov 30]. Available from:
http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/MSS
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3. S. Johnston and J. Cordes, Public good or commercial opportunity? Case
studies
in remote sensing commercialisation. Space Policy 19 (2003), pp.
23–31.
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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
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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/TM
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6. Williams D. Landsat 7. National Aeronautics and Space Administration; [2009
Dec
09, cited 2009 Nov 29]. Available from
Citations (2)
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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/spatial_data/remote_sensing/satell
ite_imagery
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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 (2–3) 248–257]
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9. AVIRIS Concept. NASA Jet Propulsion Laboratory; [2007 Oct 30, cited 2009
Nov
30]. Available from: http://aviris.jpl.nasa.gov/html/aviris.concept.html
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10. Hirano, A., Madden, M., Welch, R. 2003. Hyperspectral Image Data for
Mapping
Wetland Vegetation [Wetlands June 2003 (2) 436-448]
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11. CAO Systems. Carnegie Airborne Observatory; [cited 2009 Nov 30]. Available
from http://cao.stanford.edu/?page=cao_systems
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12. Asner et al 2008, Invasive species detection in Hawaiian rainforests using
airborne imaging spectroscopy and LiDAR [Remote Sensing of Environment 112
(2008), pp. 1942–1955].