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Article

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

1
Department of Engineering, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, Almería 04120, Spain
2
Politecnico di Bari, via Orabona n. 4, I-70125 Bari, 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: James Campbell and Prasad S. Thenkabail
Received: 16 May 2016 / Revised: 7 June 2016 / Accepted: 14 June 2016 / Published: 18 June 2016
Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively. View Full-Text
Keywords: Landsat 8; WorldView-2; time series; object-based classification; greenhouse mapping; decision tree; Moment Distance Index Landsat 8; WorldView-2; time series; object-based classification; greenhouse mapping; decision tree; Moment Distance Index
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MDPI and ACS Style

Aguilar, M.A.; Nemmaoui, A.; Novelli, A.; Aguilar, F.J.; García Lorca, A. Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series. Remote Sens. 2016, 8, 513. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060513

AMA Style

Aguilar MA, Nemmaoui A, Novelli A, Aguilar FJ, García Lorca A. Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series. Remote Sensing. 2016; 8(6):513. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060513

Chicago/Turabian Style

Aguilar, Manuel A., Abderrahim Nemmaoui, Antonio Novelli, Fernando J. Aguilar, and Andrés García Lorca. 2016. "Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series" Remote Sensing 8, no. 6: 513. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060513

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