Vol. 11, No. 2, 132-154, 2012

Agricultural crop change detection by means of hybrid classification and high resolution images
Eva Savina Malinverni, Michele Rinaldi, and Sergio Ruggieri

The most widespread application of remote sensing technology is regarding Land Use/Land Cover (LULC) automatic map production to optimize land monitoring and management. These tasks represent new challenges. The improved performances of automatic classification approaches become a fast and accurate tool for a reliable GIS decision support system.

The main aims of this work were i) to test the ability of using multispectral variability and high spatial information from different remote sensing images to recognize land use changes by means of a new hybrid classification method and ii) to quantify and evaluate the temporal variation of main crop rotations.

The test area covered the 132 square kilometres of the Capitanata plain in Southern Italy (Apulia Region), including mostly agricultural landscape (wheat, sugar beet, asparagus, vineyard, olive grove). The comparison was made between a land use classification with a series of SPOT 5 images acquired in May 2007 and a set of WorldView-2 images acquired in April and July 2010. This new data set underlined better performances in terms of ground (2 m) and spectral resolution (eight bands: Blue, Coastal (blue), Green, Yellow, Red, Red edge, NIR 1, NIR 2).

The approach was based on a hybrid classification implemented in T-Map software by the spin-off company SI2G. The results showed an overall accuracy of more than 82%, were displayed on maps and used to calculate some indicators of crop sequences: This allowed of a quantification of more frequent rotation types (monoculture, rotations every 2 or 3 years) to promote ways of managing crop sequences compatible with environmental protection. Another indicator was the water requirement. According to the crops and the fields cropped, the total yearly average irrigation requirement was evaluated to correctly plan the irrigation water distribution.

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Submitted: 17 Aug 2011
Revised: 31 Oct 2012
Accepted: 08 Nov 2012
Published: 03 Dec 2012
Responsible editor: Zbignew Bochenek

Malinverni E S, M Rinaldi & S Ruggieri, 2012. Agricultural crop change detection by means of hybrid classification and high resolution images. EARSeL eProceedings, 11(2): 132-154


EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France


BIS Library and Information System, Carl von Ossietzky University of Oldenburg


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