HR: 15:10h
AN: B23F-07    [Abstracts]
TI: The Suitability of Landsat SLC-off Data to Characterize Mid-decadal Tropical Forest Cover in the Congo River Basin, Africa.
AU: * Lindquist, E
EM: erik.lindquist@sdstate.edu
AF: South Dakota State University Geographic Information Science Center of Excellence, Wecota Hall 109 Box 506B, Brookings, SD 57007, United States
AU: Hansen, M
EM: matthew.hansen@sdstate.edu
AF: South Dakota State University Geographic Information Science Center of Excellence, Wecota Hall 109 Box 506B, Brookings, SD 57007, United States
AU: Roy, D
EM: david.roy@sdstate.edu
AF: South Dakota State University Geographic Information Science Center of Excellence, Wecota Hall 109 Box 506B, Brookings, SD 57007, United States
AB: Accurate depictions of tropical land cover and land cover change are required for important applications relating to human livelihoods and ecosystem services. Landsat data has been used to map forest cover change in many tropical areas at high spatial resolution. Data loss due to persistent cloud cover and atmospheric contamination are frequent problems in these efforts. Contemporary forest cover characterization and change estimates in the tropics are further hampered by the Landsat 7 scan-line corrector (SLC-off) malfunction which creates scan gaps and increases data loss in every acquisition. This paper examines the effects of Landsat scan line corrector problems and cloud cover on data requirements necessary to produce a contemporary, mid-decadal forest cover characterization for the Congo River Basin. 30 Landsat SLC-off acquisitions from 2004 through 2006 were obtained for six unique path/row combinations for a forested area in the Democratic Republic of Congo. Images were combined into five final, six path/row mosaics of one, two, three, four and five scenes per path/row using an automated, pixel-based mosaic method in which the highest quality pixel from all image inputs is used for analysis. Data loss due to missing scan lines and clouds totaled nearly 25 percent in the single acquisition landscape mosaic; 23 percent due to SLC-off issues and two percent due to clouds. Total data loss decreased to nine percent with one additional acquisition per path/row but 4.5 percent of this was due to cloud cover. After compiling five acquisitions per path/row, total data loss was approximately one percent. Cloud cover impacts are present in each mosaic as data used to fill scan gaps frequently contained cloud. High spatial resolution forest characterization and change detection in the tropics of central Africa is not possible using a single best image approach nor likely to be mapped on a frequent time step as clouds and scan gaps will require multiple acquisitions over the same area in each time period. A conservative estimate of the number of Landsat SLC-off acquisitions needed to map the entire Congo basin for the mid-decadal time period is 550 spanning three years time.
DE: 0540 Image processing
DE: 1632 Land cover change
DE: 1640 Remote sensing (1855)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting