Evolution of Land Use/Land Cover in Mediterranean Forest Areas – A Case Study of the Maamora in the North-West Morocco

Ghouldan, Abderrahym; Benhoussa, Abdelaziz; Ichen, Abdellah

doi:10.12912/27197050/191413

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Tytuł artykułu

Evolution of Land Use/Land Cover in Mediterranean Forest Areas – A Case Study of the Maamora in the North-West Morocco

Autorzy

Ghouldan Abderrahym , Benhoussa Abdelaziz , Ichen Abdellah

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DOI

10.12912/27197050/191413

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Abstrakty

Land use/land cover (LULC) change information is crucial for monitoring purposes, formulating strategies, socioeconomic progress, and decision-making. The main objective of this study was to analyze and quantify the changes in land use as well as land cover patterns within the Maamora forest in Morocco, and to identify the key factors that influenced its trend from 1989 to 2022. In this study, multispectral remote sensing (RS) data were employed to detect land cover changes in the Maamora forest using Landsat images for the years 1989, 1999, 2009, 2019 and 2022. The maximum likelihood classification (MLC) method was applied to classify the Landsat images using ArcMap 10.4 software to analyze the current state of the study area. Seven LULC classes (cork oak, eucalyptus, pine, acacia, bare land, daya, and others) were successfully classified, achieving overall accuracies surpassing 86% and Kappa coefficients greater than 0.85 for all selected dates. The results of the land use/land cover change detection indicate a decrease in the cork oak area from 60.71% to 44.42%, along with an increase in the eucalyptus area from 18.11% to 39.31%. Moreover, the pine, acacia, bare land, daya, and other classes went from 17.22, 2.80, 0.95, 0.05, and 0.12% to 4.58, 0.02, 10.84, 0.34, and 0.48% respectively. Indeed, from 1989 to 2022, around 50.84% of the study area’s surface remained unchanged, whereas 49.16% underwent changes, transitioning to other land cover classes or endured degradation. This research underscored the anthropogenic transformation of the Maamora woodland, which has led to the degradation of its natural resources. Broadly, these findings can serve as foundational data for future research endeavors and offer valuable insights to concentrate on the key factors driving forest degradation in order to inform the development of interventions aimed at preserving the sustainability of natural species and the overall ecosystem.

Słowa kluczowe

mapping remote sensing maximum likelihood classification LULC Maamora forest Morocco

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2024

Tom

Vol. 25, iss. 10

Strony

134--149

Opis fizyczny

Bibliogr. 75 poz., rys., tab.

Twórcy

autor

Ghouldan Abderrahym

ghouldan.1997@gmail.com

Biodiversity, Ecology and Genom Laboratory, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, 4 Avenue Ibn Battouta, BP, Agdal, Rabat, Morocco

https://orcid.org/0009-0000-6370-0424

autor

Benhoussa Abdelaziz

a.benhoussa@um5r.ac.ma

Biodiversity, Ecology and Genom Laboratory, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, 4 Avenue Ibn Battouta, BP, Agdal, Rabat, Morocco

https://orcid.org/0000-0002-5596-9797

autor

Ichen Abdellah

a.ichen@um5r.ac.ma

Biodiversity, Ecology and Genom Laboratory, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, 4 Avenue Ibn Battouta, BP, Agdal, Rabat, Morocco

https://orcid.org/0000-0001-6139-3109

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Bibliografia

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