Research Article | Published: 20 December 2019

Use of Savitzky - Golay Filters to Minimize Multi-temporal Data Anomaly in Land use Land cover mapping

Ram  Kumar Singh, Vinay Shankar Prasad Sinha, Pawan Kumar Joshi and Manoj Kumar

Indian Journal of Forestry | Volume: 42 | Issue: 4 | Page No. 362-368 | 2019
DOI: https://doi.org/10.54207/bsmps1000-2019-650ST3 | Cite this article

Abstract

Land use land cover characterization and mapping have become a prerequisite in all environmental Planaing. The array of satellites deployed in the space provides multi-temporal images that can be used for the land use land cover classification. But, much often these multi-temporal images have data noise and anomaly owing to the cloud and atmospheric effects. This brings pseudo hikes and lows in data adding classification with possible errors. We present a method for the removal of data anomaly where monthly data of MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (MODIS 13Q1) was used for the classification of images over a large area encompassing the SAARC nations. MODIS multi-temporal data were filtered usinga Savitzky-Golay (S-G) algorithm which provided smoothened data and the seasonality (start, end of the season) were identified. Phenology profile curves were created for the characterization of the agriculture and forestry feature classes. The S-G filtered images and raw MODIS data phenology profile curves were compared for the eleven classes of land cover, viz., ever green needle forest, ever green broad leave, deciduous broad leave, shrub, savannas, grass, agriculture, built-up, water, snow (ice), and barren. Spectral signature separability was also compared using Euclidean spectral distance method. In conclusion, it was observed that multi-spectral S-G filtered data were more useful for the classification of agriculture and forestry classes for a larger coverage.

Keywords

Savitzky-Golay, Time-series, SAARC nations, AFOLU

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References

1. Atzberger, C., Eilers, P.H.C. (2011). A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int J Digit Earth. 4 (5) https://doi.org/10.1080/17538947.2010.505664

Google Scholar

2. Brown, J.C., Kastens, J.H., Coutinho, A.C., Victoria, D. deC., Bishop, C.R. (2013). Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sens Environ. 130 https://doi.org/10.1016/j.rse.2012.11.009

Google Scholar

3. Chen, Jun., Chen, Jin., Liao, A., Cao, X., Chen, L., Chen, X., He C, Han G, Peng, S., Lu, M. (2014). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J Photogramm Remote Sens [Internet]. 103:7-27 https://doi.org/10.1016/j.isprsjprs.2014.09.002

Google Scholar

4. Dai, W., Selesnick, I., Rizzo, J.R., Rucker, J., Hudson, T. (2017). A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades. J Vis. 17 (9) :10 https://doi.org/10.1167/17.9.10

Google Scholar

5. Friedl, M.A., Gopal, S., Muchoney, D., Strahler, A.H. (2002). Global land cover mapping from MODIS: algorithm design and preliminary results. Remote Sens Environ. 83. (1,2) 287-302 https://doi.org/10.1016/S0034-4257(02)00078-0

Google Scholar

6. Geerken, R., Zaitchik, B., Evans, J.P. (2005). Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. Int J Remote Sens. 26:5535-5554 https://doi.org/10.1080/01431160500300297

Google Scholar

7. Jensen, J.R. (2014). Remote sensing of the environment: an earth resource perspective. 2nd edition, Pearson India, Noida

Google Scholar

8. Jia, K., Liang, S., Zhang, L., Wei, X., Yao, Y., Xie, X. (2014). Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. Int J Appl Earth Obs Geoinf [Internet]. 33:32-38 https://doi.org/10.1016/j.jag.2014.04.015

Google Scholar

9. Jin, Chen., Per, Jonsson., Masayuki, Tamura., Zhihui, Gu., Bunkei, M., Eklundah, L. (2014). A simple method for reconstructing a high Savitzky- Golay filter. Remote sen. Environ 91, (3-4):332-344 quality NDVI time-series data set based on the

Google Scholar

10. Jönsson, P., Eklundh, L. (2004). TIMESAT-a program for analyzing time-series of satellite sensor data. Comput Geosci. 30:833-845 https://doi.org/10.1016/j.cageo.2004.05.006

Google Scholar

11. Kogan, F.N. (2000). Satellite-observed sensitivity of world land ecosystems to El Niño/La Niña. Remote Sens Environ. 74 (3):445-462 https://doi.org/10.1016/S0034-4257(00)00137-1

Google Scholar

12. Li, K.J., B.W., Q. (2013). Crop classification using HJ satellite multispectral data in the North China Plain. J Appl Remote Sens. 7 https://doi.org/10.1117/1.JRS.7.073576

Google Scholar

13. Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., Worthy, L.D. (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ. 105:142-154 https://doi.org/10.1016/j.rse.2006.06.018

Google Scholar

14. Schriever, J.R., Congalton, R.G. (1995). Evaluating Seasonal Variability as an Aid to Cover-Type Mapping from Landsat Thematic Mapper Data in the Northeast. Photogramm Eng Remote Sensing. 61(3):321-327

Google Scholar

15. Shao, Y., Lunetta, R.S., Wheeler, B., Iiames, J.S., Campbell, J.B. (2016). An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sens Environ. 174:258-265 https://doi.org/10.1016/j.rse.2015.12.023

Google Scholar

16. Stöckli, R., Vidale, P.L. (2004). European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int J Remote Sens. 25 (17) https://doi.org/10.1080/01431160310001618149

Google Scholar

17. White, M.A., Thornton, P.E., Running, S.W. (1997). A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem Cycles https://doi.org/10.1029/97GB00330

Google Scholar

18. Xiao X, Boles S, Liu J, Zhuang D, Liu M. (2002). Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens Environ. 82:335-348 https://doi.org/10.1016/S0034-4257(02)00051-2

Google Scholar

19. Zhang, X., Sun, R., Zhang, B., Tong, Q. (2008). Land cover classification of the North China Plain using MODIS_EVI time series. ISPRS J Photogramm Remote Sens. 63:476-484 https://doi.org/10.1016/j.isprsjprs.2008.02.005

Google Scholar

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How to cite

Singh, R.K., Sinha, V.S.P., Joshi, P.K. and Kumar, M., 2019. Use of Savitzky - Golay Filters to Minimize Multi-temporal Data Anomaly in Land use Land cover mapping. Indian Journal of Forestry, 42(4), pp.362-368. https://doi.org/10.54207/bsmps1000-2019-650ST3

Publication History

Manuscript Published on 20 December 2019

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