Detecting greenhouse changes from QuickBird imagery on the Mediterranean coast

In this study a very high resolution image from the QuickBird satellite was used to detect new greenhouses built since the last update of the information system utilized for the study area. The area, located in the southeast of Spain, has the highest concentration of greenhouses in Europe, which makes it the heart of the economy of this region. The methodology proposed in this paper is based on the comparison of the classification of a current image with the information system corresponding to the last update. Maximum likelihood classification method was employed and different band combinations were used to define the training areas and to carry out the classification process. The optimal band combination for the detection of greenhouses was calculated by means of a variance analysis. The process was completed with the delineation of the new greenhouses with two algorithms programmed in Visual Basic 6.0, one to eliminate the loops shown around the greenhouses detected, and the other one, based on the Hough transformation, to delineate the contour of the polygons corresponding to the new greenhouses. The proposed methodology achieved (1) a value for true greenhouse surface of about 91.45% of the whole surface, (2) a very low value for undetected greenhouses (five greenhouses from a total of 202 that were built, representing 1.49% of the surface of new greenhouses), and (3) a low number of pixels wrongly classified as greenhouses.

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