HR: 1330h
AN: G43A-09    [Abstracts]
TI: Quantifying the Uncertainty in Land Loss Estimates in Coastal Louisiana
AU: * Wales, P M
EM: pmwales@olemiss.edu
AF: University of Mississippi, 118 Carrier Hall, University, MS 38677 United States
AU: Kuszmaul, J S
EM: kuszmaul@olemiss.edu
AF: University of Mississippi, 118 Carrier Hall, University, MS 38677 United States
AU: Roberts, C
EM: croberts19@cox.net
AF: PixSell Inc., 1014 Priory Place, McLean, VA 22101 United States
AB: For the past twenty-five years the land loss along the Louisiana Coast has been recognized as a growing problem. One of the clearest indicators of this land loss is that in 2000 smooth cord grass (spartina alterniflora) was turning brown well before its normal hibernation period. In 2001 data were collected using low altitude helicopter based transects of the coast, with 8,400 data points being collected. The surveys contained data describing the characteristics of the marsh, including; latitude, longitude, marsh condition, marsh color, percent vegetated, and marsh die-back. The 2001 data were compared with previously collected data from 1997. Over 100,000 acres of marsh were affected by the 2000 browning. Satellite imagery can be used to monitor changes in coastlines, vegetation health, and conversion of land to open water. An unsupervised classification was applied to 1997 Landsat TM imagery from the Louisiana coast. Based on the classification, polygons were delineated surrounding areas of water. Using the Kappa Classification Statistical Analysis extension in ArcView, kappa statistics were calculated to quantify the amount of agreement between the unsupervised classification and field checked data while correcting for agreement due to chance. Numerical results reveal that a straightforward unsupervised classification does a reasonable job of approximating the actual field checked data. Kappa values of 0.57 and higher have been obtained, which is considered fair to good agreement. This agreement adds credibility to imagery based estimates of coastal land loss, which affords the opportunity for significant savings of time, labor, and cost compared to field based monitoring. Refined classifications and use of higher resolution imagery are expected to yield improved costal land loss estimates.
DE: 1645 Solid Earth
DE: 1699 General or miscellaneous
DE: 1890 Wetlands
DE: 3360 Remote sensing
SC: Geodesy [G]
MN: 2005 Joint Assembly