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Journal of Applied Remote Sensing • Open Access

Early identification of cotton fields using mosaicked aerial multispectral imagery
Author(s): Chenghai Yang; Charles P. C. Suh; John K. Westbrook

Paper Abstract

Early identification of cotton fields is important for advancing boll weevil eradication progress and reducing the risk of reinfestation. Remote sensing has long been used for crop identification, but limited work has been reported on early identification of cotton fields. The objective of this study is to evaluate aerial imagery for identifying cotton fields before cotton plants start to bloom. A two-camera imaging system was used to acquire red-green-blue and near-infrared images with 1-m pixel resolution along two flight lines over an 8    km × 12    km cropping area. The images were mosaicked using two approaches: manual georeferencing followed by position-based mosaicking in Erdas Imagine and content-based automatic mosaicking in Pix4DMapper. The mosaicked images were then classified into different crops and cover types using supervised classification techniques. Results showed that both types of mosaics were effective for cotton identification and that maximum likelihood classification produced the best overall accuracy of 90% for the position-based approach and 91% for the content-based approach. The methodologies presented in this study will be useful for boll weevil eradication program managers to quickly and efficiently identify cotton fields at relatively early growth stages using mosaicked aerial imagery.

Paper Details

Date Published: 12 January 2017
PDF: 13 pages
J. Appl. Remote Sens. 11(1) 016008 doi: 10.1117/1.JRS.11.016008
Published in: Journal of Applied Remote Sensing Volume 11, Issue 1
Show Author Affiliations
Chenghai Yang, Agricultural Research Service (United States)
Charles P. C. Suh, Agricultural Research Service (United States)
John K. Westbrook, Agricultural Research Service (United States)


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