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Identifying Agricultural Systems Using SVM Classification Approach Based on Phenological Metrics in a Semi-arid Region of Morocco

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Abstract

To understand changes in agricultural systems, it is necessary to monitor vegetation dynamics based on the spatio-temporal characterization of phenological parameters. The purpose of this study is to identify the main agricultural systems using a phenology-based classification method in a semi-arid context. Phenological metrics were derived from Normalized Difference Vegetation Index time series extracted from MOD13Q1 product between 2012 and 2016. Furthermore, Support Vector Machine classification method was applied based on phenological metrics, to identify the main agricultural system classes in the study area. The main classes are; (1) irrigated annual crop, (2) irrigated perennial crop, (3) rainfed area and (4) fallow. The classification overall accuracy reached 88%, with a kappa coefficient of 0.83 and values of F1-score greater than 0.76. The results demonstrated the ability of phenological parameters to identify and monitor the main agricultural system classes in the study area and to control the illegal pumping zones.

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Adapted from Jönsson and Eklundh (2004)

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Acknowledgements

The authors are grateful to NASA’s team, for creating and making the vegetation index product MOD13Q1 freely available. We also would like to thank Pr. Eklundh and his team for their available help on TIMESAT software.

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Authors and Affiliations

  1. Water Resources Management and Valorisation and Remote Sensing Team, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Y. Lebrini & A. Boudhar

  2. Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir, Morocco

    A. Boudhar

  3. National Institute of Agronomic Research, Rabat, Morocco

    Y. Lebrini, R. Hadria, H. Lionboui & T. Benabdelouahab

  4. Agronomic and Veterinary Institute Hassan II, Rabat, Morocco

    L. Elmansouri

  5. Ministry of Agriculture, Fisheries, Rural Development, Water and Forests, Direction of Strategy and Statistics, Rabat, Morocco

    R. Arrach

  6. International Research Institute for Climate and Society, The Earth Institute, Columbia University, New York, USA

    P. Ceccato

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  1. Y. Lebrini
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  2. A. Boudhar
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  3. R. Hadria
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  4. H. Lionboui
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  6. R. Arrach
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  7. P. Ceccato
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  8. T. Benabdelouahab
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Correspondence to Y. Lebrini.

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Lebrini, Y., Boudhar, A., Hadria, R. et al. Identifying Agricultural Systems Using SVM Classification Approach Based on Phenological Metrics in a Semi-arid Region of Morocco. Earth Syst Environ 3, 277–288 (2019). https://doi.org/10.1007/s41748-019-00106-z

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