Balaji Kannan K.P. Ragunath R. Kumaraperumal R. Jagadeeswaran R. Krishnan


Importance of remotely sensed data for inventorying, mapping, monitoring and for the management and development planning for the optimum utilization of natural resources has been well established. Though, a lot of applications have been attempted using remote sensing tool, mapping of coconut growing areas has not been attempted at a regional level. Hence, this study was envisaged to map the coconut growing areas in Tamil Nadu, India using Survey of India Toposheet grid (1:50,000 scale) and Digital Globe data. The temporal window of these datasets ranged from March 2012 to June 2014. The data sets have a spatial resolution of 41 cm. It has been observed that Coimbatore has largest area under coconut among all districts of Tamil Nadu, followed by Tiruppur, Thanjavur and Dindigul. In terms of percentage of coconut area to the total geographical area of the district, Tiruppur, leads the list, followed by Kanyakumari, Coimbatore and Thanjavur. On comparing the area obtained by this study with the area as per Coconut Development Board using a paired t-test, a p-value of 0.005 was obtained and hence, there is no significant difference between the two. Hence, it can be said that geospatial technologies like remote sensing and geographical information system are the best tools for accurate assessment and spatial data creation for crop mapping and area assessment.


Download data is not yet available.


Metrics Loading ...




Area mapping, Coconut, Geographical information system, Remote sensing, Spatial data

Anonymous (2013). Crop production techniques of Horticultural crops. Tamil Nadu Agricultural University, Coimbatore Pp. 197, India
Anonymous (2016). Horticulture Division, Dept. of Agriculture and Cooperation, Ministry of Agriculture and Farmers Welfare, Government of India
Kumar, M. and Roy, P. S. (2013). Utilizing the potential of World View ?2 for discriminating urban and vegetation features using object based classification techniques. J Indian Soc Remote Sens.
Nagendra, H. and Rocchini, D. (2008). High resolution satellite imagery for tropical biodiversity studies: The devil is in the detail. Biodivers. Conserv., 17:3431–3442
Palaniswami, C., Upadhyay, A. K. and Maheswarappa, P. (2006). Spectral mixture analysis for subpixel classification of coconut. Current Science, 91:12-25
Agarwal, S., Lionel Sujay Vailshery, Madhumitha Jaganmohan and Harini Nagendra (2013). Mapping urban tree species using very high resolution satellite imagery: comparing pixel-based and object-based approaches. ISPRS Int. J. Geo-Inf., 2:220-236
Theerkhapathy, S.S. and S. Chandrakumarmangalam (2014). Coconut Processing Industries: An Outlook. Global Journal of Commerce and Management Perspective, 3(5):219-221
Wang, K., Franklin, E. S., Guo, X. and Cattet, M. (2010). Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists. Sensors, 10: 9647–9667
Citation Format
How to Cite
Kannan, B., Ragunath, K., Kumaraperumal, R., Jagadeeswaran, R., & Krishnan, R. (2017). Mapping of coconut growing areas in Tamil Nadu, India using remote sensing and GIS. Journal of Applied and Natural Science, 9(2), 771–773. https://doi.org/10.31018/jans.v9i2.1272
More Citation Formats:
Research Articles

Most read articles by the same author(s)