MISR abstract
Xue Liu, Menas Kafatos, Richard B. Gomez and S. J. Goetz (2003). Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research. 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data. 27-28 Oct 2003. 4.pp 80-85.
Accurate and reliable information about land cover
and land use is essential to carbon cycle and climate change
modeling. While historical regional-to-global scale land cover
and land use data products had been produced by AVHRR and
MSS/TM, this task has been advanced by sensors such as
MODIS and ETM since the latter 1990s. While the accuracies
and reliabilities of these data products have been improved,
there have been reports from the modeling community that
additional work is needed to reduce errors so that the
uncertainties associated with the global carbon cycle and
climate change modeling can be addressed. Remotely sensed
data collected in different wavelength regions, at different
viewing geometries, usually provide complementary
information. Their combination has the potential to enhance
remote sensing capabilities in discriminating important land
cover components. In this paper, we studied multi-angle data
fusion, and optical - SAR data fusion for land cover
classification at regional spatial scale in the temperate forests of
the eastern United States. Data from EOS-MISR, Landsat-
ETM+ and RadarSat-SAR were used. The results showed
significantly improved land cover classification accuracy when
using the data fusion approach. These results may benefit
future land cover products for global change research.