Crop Identification by Fuzzy C-Mean in Ravi Season Using Multi-Spectral Temporal Images
Information regarding spatial distribution of different crops in a region of multi-cropping system is required for management and planning. In the present study, multi dated LISS-III and AWiFS data were used for crop identification. The cultivable land area extracted from the landuse classification of LISS-III image was used to generate spectral-temporal profile of crops according to their growth stages with Normalised Difference Vegetation Index (NDVI) method. The reflectance from the crops on 9 different dates identified separate spectral behavior. This combined NDVI image was then classified by Fuzzy C-Mean (FCM) method again to get 5 crop types for around 12,000 km2 area on Narmada river basin of Madhya Pradesh. The accuracy assessment of the classification showed overall accuracy of 88 % and overall Kappa of 0.83. The crop identification was done for one entire Ravi season from 23 October 2011 to 10 March 2012.
KeywordsCrop identification NDVI Fuzzy C-Mean Narmada river basin
The authors are thankful to the National Remote Sensing Centre (NRSC) for providing the AWiFS and LISS-III satellite images for the study area and to the UGC for financial assistance.
- 1.Wardlow, B., Egbert, S., Kastens, J.: Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Rem. Sens. Environ. 108, 290–310 (2007)Google Scholar
- 3.Vincent, S., Pierre, F.: Identifying main crop classes in an irrigated area using high resolution image time series. In: IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS’03), pp. 252–254 (2003)Google Scholar
- 7.Wardlow, B., Egbert, S.: Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U.S. Central Great Plains. Rem. Sens. Environ. 112, 1096–1116 (2008)Google Scholar
- 9.Doriaswamy, P.C., Akhmedov, B., Stern, A.J.: Improved techniques for crop classification using MODIS imagery. In: IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS’06), pp. 2084–2087 (2006)Google Scholar
- 10.Ying, L., Xiuwan, C., Hongwei, D., Lingkui, M.: An improved multi temporal masking classification method for winter wheat identification. In: International Conference on Audio Language and Image Processing (ICALIP’10), pp. 1648–1651 (2010)Google Scholar
- 14.Zurita-Milla, R., Gomez-Chova, L., Guanter, L., Clevers, J.G.P.W., Camps-Valls, G.: Multi temporal un-mixing of medium spatial resolution satellite images: a case study using MERIS images for land cover mapping. In: IEEE Transactions on Geoscience and Remote Sensing Symposium, pp. 1–10 (2011)Google Scholar
- 18.Liang, P., Chunyu, Y.: Study on mixed pixel classification method of remote sensing image based on fuzzy theory. In: IEEE Conference Urban Remote Sensing Joint Event, pp. 1–7 (2009)Google Scholar
- 21.Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Remote sensing and image interpretation. Wiley, New York (2008)Google Scholar