
Proceedings Paper
Identification of sugarcane and onion crops using digital image processing of multidate multisensor high-resolution satellite dataFormat | Member Price | Non-Member Price |
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Paper Abstract
Timely assessment of area under different standing crops and their spatial distribution is essential for irrigation planning
and management in command areas. The conventional methods of collecting information on crop acreage are time-consuming
and uneconomical, especially when large areas are involved. The modern Remote Sensing technology
provides real-time, accurate and cost-effective data on crop acreage due to its multi-spatial, multi-spectral and multi-temporal
nature. The attempt has been made in this study to use the multi-date data of RESOURCESAT sensors like
AWiFS, LISS-III and LISS-IV as well as multispectral data of IKONOS with suitable digital imaging processing
techniques to identify Sugarcane, which is a long duration crop and Onion, which is a short duration crop, in order to
plan need based irrigation facilities, to collect the revenue from the farmers in the form of water assessment, better crop
management and future crop planning.
Paper Details
Date Published: 7 December 2006
PDF: 10 pages
Proc. SPIE 6411, Agriculture and Hydrology Applications of Remote Sensing, 641108 (7 December 2006); doi: 10.1117/12.697882
Published in SPIE Proceedings Vol. 6411:
Agriculture and Hydrology Applications of Remote Sensing
Robert J. Kuligowski; Jai S. Parihar; Genya Saito, Editor(s)
PDF: 10 pages
Proc. SPIE 6411, Agriculture and Hydrology Applications of Remote Sensing, 641108 (7 December 2006); doi: 10.1117/12.697882
Show Author Affiliations
Dhananjay S. Pandit, ISRO Regional Remote Sensing Service Ctr. (India)
Yelisetti V. N. Krishna Murthy, ISRO Regional Remote Sensing Service Ctr. (India)
Yelisetti V. N. Krishna Murthy, ISRO Regional Remote Sensing Service Ctr. (India)
V. Jayaraman, ISRO Earth Observation Systems (India)
Published in SPIE Proceedings Vol. 6411:
Agriculture and Hydrology Applications of Remote Sensing
Robert J. Kuligowski; Jai S. Parihar; Genya Saito, Editor(s)
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