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:Wetlands_Cape_May_New_Jersey.jpg

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.jpg

Benefits of Studying Wetlands Remotely

• 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.

http://www.calistogatroop18.org/photos/20061014%20Anderson%20Marsh%20Hike%20353.jpg

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).

Equipped with Multispectral Scanner Systems, these satellites were best for very large wetland studies due to low resolution and shaky geometric precision(2).

http://upload.wikimedia.org/wikipedia/en/4/41/Landsat1.jpg

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/images/image36.gif

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/landsat45.gif

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.

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.

http://geology.com/novarupta/maps/landsat-novarupta-region-large.jpg

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/l7satellite.jpg

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/nangrong/data/spatial_data/remote_sensing/satellite_imagery/ (7)

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).

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.

http://borrowedearth.files.wordpress.com/2008/05/mangrove0459sm.jpg

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.

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).

http://aviris.jpl.nasa.gov/html/aviris.overview.html

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_puu-1.jpg

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?

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.html

• 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

• 3. S. Johnston and J. Cordes, Public good or commercial opportunity? Case studies in remote sensing commercialisation. Space Policy 19 (2003), pp. 23–31.

• 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/TM

• 6. Williams D. Landsat 7. National Aeronautics and Space Administration; [2009 Dec 09, cited 2009 Nov 29]. Available from

http://landsat.gsfc.nasa.gov/about/landsat7.html

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/spatial_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 (2–3) 248–257]

• 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

• 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/?page=cao_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. 1942–1955].