Remotely Sensed Phenology Monitoring and Land-cover Classification for the Localization of the Endemic Argan Tree in the Southern-west of Morocco
2021
Sebbar, B. | Moumni, A. | Lahrouni, A. | Chehbouni, A. | Belghazi, T. | Maksoudi, B. | Centre d'études spatiales de la biosphère (CESBIO) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] (UM6P) | Université Cadi Ayyad [Marrakech] (UCA)
International audience
Показать больше [+] Меньше [-]Английский. Argania spinosa also known as the argan tree is an endemic plant of Morocco. Despite having the ability to subsist in extreme drought conditions, it is threatened by soil land clearing, overexploitation, and absence of natural regeneration, causing a worrying decline in both spatial extent and density. The spatial extent of dryland forests is debated, as estimates of forest areas in drylands are uncertain. The present study aims to map and locate the spatial distribution of the argan trees at Smimou community located in Essaouira province, south-eastern Morocco, using satellite images and a double-classification process to overcome separability problems. The work focuses on the characterization and comparison of the unique phenological patterns of argan with the other present land-cover classes. NDVI products were derived from a Sentinel-2 time-series covering one year (2018 to 2019), then ground samples were used to extract phenological profiles at parcel level then at tree level, to feed representative calibration samples to Support Vector Machine classifier. The outcome was integrated with an elevation model in a Decision Tree to reclassify mixed areas. The results indicated an F1-score and an overall accuracy of 91.27% and 92.60% respectively, a promising technique for updating argan extent at national scale.
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Эту запись предоставил Institut national de la recherche agronomique