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TitleLandsat 7 SLC-Off gap-filling for interim data continuity in northern regions using a robust radiometric normalisation technique
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LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorOrazietti, J; Olthof, I; Fraser, RORCID logo
SourceProceedings of the 26th Canadian Symposium on Remote Sensing; 2005, 8 pages, https://doi.org/10.4095/220696 Open Access logo Open Access
Year2005
Alt SeriesEarth Sciences Sector, Contribution Series 2005116
Meeting26th Canadian Symposium on Remote Sensing; Wolfville, Nova Scotia; CA; June 14-16, 2005
Documentbook
Lang.English
Mediapaper; on-line; digital
File formatpdf
SubjectsNature and Environment
Released2005 01 01
AbstractMuch of the current climate change research in Canada involves monitoring northern environments, which require a continuous source of data for change detection. Unfortunately, data continuity of Landsat 7 imagery has been interrupted due to the recent failure of the Scan Line Corrector (SLC), which compensates for the forward motion of the satellite. Although Landsat 7 data are still being acquired, each scene has gaps of no-data values due to the SLC failure. We have created a methodology to produce a consistent source of data for medium-resolution northern biomass study and change detection until a suitable replacement for Landsat 7 is launched. The relatively short growing season and low sun angle in the north provide few opportunities for acquisition of usable earth observation data, and coupled with the failure of the SLC, further reduces the amount of usable data per scene. Fortunately, the large overlap between adjacent orbits in northern Landsat scenes provides a potentially large sample to use for gap infilling. We propose normalization of scenes using overlap regions, semi-invariant targets and a robust regression technique called Thiel-Sen prior to infilling gaps in SLC-Off imagery using the normalised adjacent scene. The new technique enhances the Level 1G (L1G) products provided by the USGS and is modification to gap-filling procedures in the data flow of the Gap-Fill Algorithm currently in use by the USGS. The resulting imagery is much more suitable for biomass modelling using vegetation indices such as the Reduced Simple Ratio (RSR) and Normalised Difference Vegetation Index (NDVI). The current methodology shows great potential for maintaining data continuity of Landsat 7 imagery for northern environmental monitoring procedures requiring the use of vegetation indices.
GEOSCAN ID220696

 
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