Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research

Xue Liu; M. Kafatos; R. B. Gomez; S. J. Goetz

doi:10.1109/WARSD.2003.1295177

Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research

Xue Liu, M. Kafatos, R. B. Gomez, S. J. Goetz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	80-85
Number of pages	6
ISBN (Electronic)	0780383508, 9780780383500
DOIs	https://doi.org/10.1109/WARSD.2003.1295177
State	Published - 2004
Externally published	Yes
Event	2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data - Greenbelt, United States Duration: Oct 27 2003 → Oct 28 2003

Publication series

Name	2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data

Conference

Conference	2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
Country/Territory	United States
City	Greenbelt
Period	10/27/03 → 10/28/03

Keywords

Carbon cycle
Classification
Data fusion
Global change
Land cover and land use
Remote sensing
Temperate forest

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1109/WARSD.2003.1295177

Cite this

Liu, X., Kafatos, M., Gomez, R. B., & Goetz, S. J. (2004). Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research. In 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (pp. 80-85). Article 1295177 (2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WARSD.2003.1295177

Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research. / Liu, Xue; Kafatos, M.; Gomez, R. B. et al.
2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data. Institute of Electrical and Electronics Engineers Inc., 2004. p. 80-85 1295177 (2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, X, Kafatos, M, Gomez, RB & Goetz, SJ 2004, Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research. in 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data., 1295177, 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Institute of Electrical and Electronics Engineers Inc., pp. 80-85, 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Greenbelt, United States, 10/27/03. https://doi.org/10.1109/WARSD.2003.1295177

Liu X, Kafatos M, Gomez RB, Goetz SJ. Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research. In 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data. Institute of Electrical and Electronics Engineers Inc. 2004. p. 80-85. 1295177. (2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data). doi: 10.1109/WARSD.2003.1295177

Liu, Xue ; Kafatos, M. ; Gomez, R. B. et al. / 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. Institute of Electrical and Electronics Engineers Inc., 2004. pp. 80-85 (2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data).

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AB - 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.

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Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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