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Time Series Multispectral Images Processing for Crops and Forest Mapping: Two Moroccan Cases

Time Series Multispectral Images Processing for Crops and Forest Mapping: Two Moroccan Cases
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Author(s): Loubna El Mansouri (Institut Agronomique et Vétérinaire Hassan II, Morocco), Said Lahssini (National School of Forestry Engineering, Morocco), Rachid Hadria (National Institute of Agriculture Research, Morocco), Nadia Eddaif (Mohammed V University, Morocco), Tarik Benabdelouahab (INRA, Morocco) and Asmae Dakir (Mohammed V University, Morocco)
Copyright: 2019
Pages: 24
Source title: Geospatial Technologies for Effective Land Governance
Source Author(s)/Editor(s): Moha El-Ayachi (Institut Agronomique et Vétérinaire Hassan II, Morocco) and Loubna El Mansouri (Institut Agronomique et Vétérinaire Hassan II, Morocco)
DOI: 10.4018/978-1-5225-5939-9.ch006

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Abstract

This chapter highlights time series image processing for accurate agriculture characterization through two Moroccan experiences. The first case aims at crop mapping. A new classification approach based on multiple classifiers combination (MCC) was developed and applied to multi-temporal enhanced vegetation index (EVI) bands. The whole process is performed in three stages: (1) Landsat data preparation and multi-temporal staked EVI image extraction, (2) MCC construction from six advanced and supervised classifiers, and (3) stacked EVI image classification using the build-up MCC. Some post-classification contextual rules were also added in order to optimize the crops classification and the final parcel shape. In the second case, a post-classification change detection process was implemented to detect changes in forest area. Many classification schemes with different vegetation and texture indices were investigated. The two experiences are cost-effective, reproducible, and transferable. Consequently, they can regularly be used to produce up-to-date land use maps.

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