<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The North African steppe areas represent a heritage of great economic and ecological importance. These areas, which were once prosperous, are currently experiencing significant degradation and a decrease in productivity due to several factors. The objective of this study was to evaluate the dynamics of the steppe vegetation, identify the responsible factors, and present perspectives for their management and restoration. The methodological approach adopted for the characterization of climatic and socioeconomic conditions and the analysis of vegetation dynamics combined classical geomatics methods with a data mining method by mobilizing several sources and on important temporal horizons. This study was optimized by using the Google Earth Engine platform. Results showed that steppe areas are characterized by their plant richness and great potential for resilience despite their low vegetation cover. The analysis of the dynamics of change has highlighted a regressive trend in steppe vegetation during the study period (1995–2020). This study has been able to highlight the extent of the dynamics of the steppes in the study area during the last 26 years and to identify the human activity as the main trigger for the transformation that steppe areas are currently undergoing. Such results improve our knowledge of these areas and open perspectives for their management.</p>
</abstract>

The North African steppe areas represent a heritage of great economic and ecological importance. These areas, which were once prosperous, are currently experiencing significant degradation and a decrease in productivity due to several factors. The objective of this study was to evaluate the dynamics of the steppe vegetation, identify the responsible factors, and present perspectives for their management and restoration. The methodological approach adopted for the characterization of climatic and socioeconomic conditions and the analysis of vegetation dynamics combined classical geomatics methods with a data mining method by mobilizing several sources and on important temporal horizons. This study was optimized by using the Google Earth Engine platform. Results showed that steppe areas are characterized by their plant richness and great potential for resilience despite their low vegetation cover. The analysis of the dynamics of change has highlighted a regressive trend in steppe vegetation during the study period (1995–2020). This study has been able to highlight the extent of the dynamics of the steppes in the study area during the last 26 years and to identify the human activity as the main trigger for the transformation that steppe areas are currently undergoing. Such results improve our knowledge of these areas and open perspectives for their management.

Vegetation Dynamics of North African Steppe (Case Study of the Moulouya Plateau In Morocco)

Laboratory of Plant Biotechnology, Ecology and Ecosystems Valorization, Faculty of Sciences

Ekológia (Bratislava)

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The North African steppe areas represent a heritage of great economic and ecological importance. These areas, which were once prosperous, are currently...

Vegetation Dynamics of North African Steppe (Case Study of the Moulouya Plateau In Morocco)

Data publikacji: 25 gru 2023

Zakres stron: 354 - 361

Otrzymano: 18 cze 2023

Przyjęty: 06 wrz 2023

DOI: https://doi.org/10.2478/eko-2023-0040

Słowa kluczowe
LULC, degradation, remote sensing, GEE

© 2023 Mohammed El Madihi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Vegetation Dynamics of North African Steppe (Case Study of the Moulouya Plateau In Morocco)

Data publikacji: 25 gru 2023

Zakres stron: 354 - 361

Otrzymano: 18 cze 2023

Przyjęty: 06 wrz 2023

DOI: https://doi.org/10.2478/eko-2023-0040

Słowa kluczoweLULC, degradation, remote sensing, GEE

© 2023 Mohammed El Madihi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
LULC, degradation, remote sensing, GEE