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

Abstract

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 is to analyze and quantify the changes in land use and 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 underscores 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.

Keywords

• mapping
• remote sensing
• maximum likelihood classification
• lulc
• maamora forest
• morocco