| Title | Landsat 7 SLC-Off gap-filling for interim data continuity in northern regions using a robust radiometric normalisation technique |
| Download | Downloads |
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| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Orazietti, J; Olthof, I; Fraser, R |
| Source | Proceedings of the 26th Canadian Symposium on Remote Sensing; 2005, 8 pages, https://doi.org/10.4095/220696  Open Access |
| Year | 2005 |
| Alt Series | Earth Sciences Sector, Contribution Series 2005116 |
| Meeting | 26th Canadian Symposium on Remote Sensing; Wolfville, Nova Scotia; CA; June 14-16, 2005 |
| Document | book |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | Nature and Environment |
| Program | Reducing Canada's Vulnerability to Climate Change
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| Released | 2005 01 01 |
| Abstract | Much 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 ID | 220696 |
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