@article{fdi:010081091,
  title = {{R}emotely sensed phenology monitoring and land-cover classification for the localization of the endemic argan tree in the {S}outhern-west of {M}orocco},
  author = {{S}ebbar, {B}. and {M}oumni, {A}. and {L}ahrouni, {A}. and {C}hehbouni, {A}bdelghani and {B}elghazi, {T}. and {M}aksoudi, {B}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A}rgania spinosa also known as the argan tree is an endemic plant of {M}orocco. {D}espite 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. {T}he spatial extent of dryland forests is debated, as estimates of forest areas in drylands are uncertain. {T}he present study aims to map and locate the spatial distribution of the argan trees at {S}mimou community located in {E}ssaouira province, south-eastern {M}orocco, using satellite images and a double-classification process to overcome separability problems. {T}he 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 {S}entinel-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 {S}upport {V}ector {M}achine classifier. {T}he outcome was integrated with an elevation model in a {D}ecision {T}ree to reclassify mixed areas. {T}he results indicated an {F}1-score and an overall accuracy of 91.27% and 92.60% respectively, a promising technique for updating argan extent at national scale.},
  keywords = {{A}rgan tree ; remote sensing ; {NDVI} ; land-cover ; classification ; forest ; management ; {MAROC} ; {ZONE} {SEMIARIDE} ; {ESSAOUIRA} {PROVINCE}},
  booktitle = {},
  journal = {{J}ournal of {S}ustainable {F}orestry},
  volume = {41},
  numero = {10},
  pages = {1014--1028},
  ISSN = {1054-9811},
  year = {2022},
  DOI = {10.1080/10549811.2021.1897018},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081091},
}