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ISPRS Int. J. Geo-Inf. 2017, 6(9), 275; doi:10.3390/ijgi6090275

Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques

1
Department of Biology, Soil and Environnement Microbiology Team, Faculty of Sciences, Moulay Ismail University, BP11201, Zitoune, Meknès, Morocco
2
Department of Geology, Water Sciences and Environnement Engineering Team, Faculty of Sciences, Moulay Ismail University, BP11201, Zitoune, Meknès, Morocco
3
Earth Sciences Institute (ICT) and Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
*
Author to whom correspondence should be addressed.
Received: 26 July 2017 / Revised: 12 August 2017 / Accepted: 29 August 2017 / Published: 3 September 2017
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Abstract

The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem. View Full-Text
Keywords: Sentinel-2A; spectral angle mapper (SAM); QGIS; mapping Sentinel-2A; spectral angle mapper (SAM); QGIS; mapping
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Mohajane, M.; Essahlaoui, A.; Oudija, F.; El Hafyani, M.; Cláudia Teodoro, A. Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques. ISPRS Int. J. Geo-Inf. 2017, 6, 275.

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