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VEGETATION CHANGE DETECTION IN LANDSAT TM TIME SERIES USING SINGULAR SPECTRUM ANALYSIS AND REGULAR FOREST INVENTORY DATA

Gulbe, Linda, Hilkevica, Galina

First published: 2014-06-20https://doi.org/10.5593/sgem2014/b23/s10.050View metrics

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Title
VEGETATION CHANGE DETECTION IN LANDSAT TM TIME SERIES USING SINGULAR SPECTRUM ANALYSIS AND REGULAR FOREST INVENTORY DATA
Authors
Gulbe, Linda, Hilkevica, Galina
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 14th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING
Publisher
Stef92 Technology
Year
2014
Pages
Not available yet
ISSN
1314-2704
ISBN
978-619-7105-12-4
Language
en
Publication type
Conference Paper
References18

  1. Abd El-Kawy, O. R., Rød, J. K., Ismail, H. A., & Suliman, A. S. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), pp. 483-494, 2011.

  2. Lu, D., Mausel, P., Brondizio, E., & Moran, E. Change detection techniques. International journal of remote sensing, 25(12), pp. 2365 -2401, 2004.

  3. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. Review Article Digital change detection methods in ecosystem monitoring: a review. International journal of remote sensing, 25(9), pp. 1565-1596, 2004.

  4. Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products. Biogeosciences, 10(6), pp. 4055-4071, 2013.

  5. Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D., Neigh, C. S., & Reichstein, M. Trend change detection in NDVI time series: effects of inter-annual variability and methodology. Remote Sensing , 5(5), pp. 2113-2144, 2013.

  6. Elmore, A. J., Mustard, J. F., Manning, S. J., & Lobell, D. B. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote sensing of environment , 73 (1), pp. 87-102, 2000.

  7. Sonnenschein, R., Kuemmerle, T., Udelhoven, T., Stellmes, M., & Hostert, P. Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate. Remote Sensing of Environment, 115(6), pp. 1408-1420, 2011.

  8. Chang, C. I., & Plaza, A. A fast iterative algorithm for implementation of pixel purity index. Geoscience and Remote Sensing Letters, IEEE, 3(1), pp. 63-67, 2006.

  9. Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. A. Analysis of time series structure: SSA and related techniques. CRC Press, 2001.

  10. Abd El-Kawy, O. R., Rød, J. K., Ismail, H. A., & Suliman, A. S. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), pp. 483-494, 2011.

  11. Lu, D., Mausel, P., Brondizio, E., & Moran, E. Change detection techniques. International journal of remote sensing, 25(12), pp. 2365 -2401, 2004.

  12. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. Review Article Digital change detection methods in ecosystem monitoring: a review. International journal of remote sensing, 25(9), pp. 1565-1596, 2004.

  13. Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products. Biogeosciences, 10(6), pp. 4055-4071, 2013.

  14. Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D., Neigh, C. S., & Reichstein, M. Trend change detection in NDVI time series: effects of inter-annual variability and methodology. Remote Sensing , 5(5), pp. 2113-2144, 2013.

  15. Elmore, A. J., Mustard, J. F., Manning, S. J., & Lobell, D. B. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote sensing of environment , 73 (1), pp. 87-102, 2000.

  16. Sonnenschein, R., Kuemmerle, T., Udelhoven, T., Stellmes, M., & Hostert, P. Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate. Remote Sensing of Environment, 115(6), pp. 1408-1420, 2011.

  17. Chang, C. I., & Plaza, A. A fast iterative algorithm for implementation of pixel purity index. Geoscience and Remote Sensing Letters, IEEE, 3(1), pp. 63-67, 2006.

  18. Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. A. Analysis of time series structure: SSA and related techniques. CRC Press, 2001.

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