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Article

Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

1
Department of Engineering, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
2
Eurac Research, Institute for Renewable Energy, Via A. Volta 13/A, 39100 Bolzano, Italy
3
Department of Geography, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1751; https://doi.org/10.3390/rs10111751
Received: 8 September 2018 / Revised: 2 November 2018 / Accepted: 3 November 2018 / Published: 6 November 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8. View Full-Text
Keywords: Landsat 8; Sentinel-2; WorldView-2; time series; object-based classification; greenhouse mapping; crop types classification Landsat 8; Sentinel-2; WorldView-2; time series; object-based classification; greenhouse mapping; crop types classification
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MDPI and ACS Style

Nemmaoui, A.; Aguilar, M.A.; Aguilar, F.J.; Novelli, A.; García Lorca, A. Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain). Remote Sens. 2018, 10, 1751. https://doi.org/10.3390/rs10111751

AMA Style

Nemmaoui A, Aguilar MA, Aguilar FJ, Novelli A, García Lorca A. Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain). Remote Sensing. 2018; 10(11):1751. https://doi.org/10.3390/rs10111751

Chicago/Turabian Style

Nemmaoui, Abderrahim, Manuel A. Aguilar, Fernando J. Aguilar, Antonio Novelli, and Andrés García Lorca. 2018. "Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)" Remote Sensing 10, no. 11: 1751. https://doi.org/10.3390/rs10111751

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