NDLI: New DTW-based method to similarity search in sugar cane regions represented by climate and remote sensing time series

Content Provider	IEEE Xplore Digital Library
Author	Romani, L.A.S. Goncalves, R.R.V. Zullo, J. Traina, C. Traina, A.J.M.
Copyright Year	2010
Description	Author affiliation: Computer Science Department, USP at Sao Carlos, PB 668 13560-970, Brazil (Romani, L.A.S.; Traina, C.; Traina, A.J.M.) \|\| CNPq and Cepagri, University of Campinas, Brazil (Goncalves, R.R.V.; Zullo, J.)
Abstract	Brazil is an important sugar cane producer, which is the main resource for ethanol production, a renewable source of energy. This agricultural commodity is important to the country economy, becoming fundamental to improve models that assist the crops monitoring process. Vegetation indexes originated from remote sensing images and agrometeorological indexes can be combined to represent sugar cane fields in a regional scale. However, finding different regions with similar patterns to classify or analyze their characteristics is a non-trivial task. Accordingly, this paper presents a method to find similar sugar cane fields represented by series of vegetation and agrometeorological indexes. The proposed method combines a weighted distance function with an algorithm to find similar objects. Results were coincident in the most cases with the classification done by experts, finding regions with similar characteristics of climate and productivity. Consequently, this approach can help in decision making processes by agricultural entrepreneurs.
Starting Page	355
Ending Page	358
File Size	274457
Page Count	4
File Format	PDF
ISBN	9781424495658
ISSN	21537003
e-ISBN	9781424495665
e-ISBN	9781424495641
DOI	10.1109/IGARSS.2010.5652003
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sugar industry Time series analysis Correlation Indexes Meteorology Remote sensing Vegetation mapping data mining Vegetation index ethanol agrometeorological index climate change
Content Type	Text
Resource Type	Article

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