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Remote Sens. 2015, 7(6), 7378-7401; doi:10.3390/rs70607378

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

1
Department of Engineering, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, Almería 04120, Spain
2
Department of Sciences and Technologies, University of Naples "Parthenope", Centro Direzionale Isola C4, Naples 80143, Italy
3
Department of Geography, University of Almería, Ctra Sacramento s/n, La Cañada de San Urbano, Almería 04120, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 24 April 2015 / Revised: 26 May 2015 / Accepted: 29 May 2015 / Published: 3 June 2015
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Abstract

Greenhouse detection and mapping via remote sensing is a complex task, which has already been addressed in numerous studies. In this research, the innovative goal relies on the identification of greenhouse horticultural crops that were growing under plastic coverings on 30 September 2013. To this end, object-based image analysis (OBIA) and a decision tree classifier (DT) were applied to a set consisting of eight Landsat 8 OLI images collected from May to November 2013. Moreover, a single WorldView-2 satellite image acquired on 30 September 2013, was also used as a data source. In this approach, basic spectral information, textural features and several vegetation indices (VIs) derived from Landsat 8 and WorldView-2 multi-temporal satellite data were computed on previously segmented image objects in order to identify four of the most popular autumn crops cultivated under greenhouse in Almería, Spain (i.e., tomato, pepper, cucumber and aubergine). The best classification accuracy (81.3% overall accuracy) was achieved by using the full set of Landsat 8 time series. These results were considered good in the case of tomato and pepper crops, being significantly worse for cucumber and aubergine. These results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves. View Full-Text
Keywords: Landsat 8; WorldView-2; time series; object-based classification; greenhouse crops; decision tree Landsat 8; WorldView-2; time series; object-based classification; greenhouse crops; decision tree
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Aguilar, M.A.; Vallario, A.; Aguilar, F.J.; Lorca, A.G.; Parente, C. Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain. Remote Sens. 2015, 7, 7378-7401.

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