Monitoring Land Use and Land Cover Change Using Remote Sensing Techniques and the Precipitation-Vegetation Indexes in Morocco

Haj, Fatiha Ait El; Ouadif, Latifa; Akhssas, Ahmed

doi:10.12912/27197050/154937

Cited by 10 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the same region, Mohajane et al (2018) studied the vegetation change of Azrou forest with three classes (vegetation of low‐density, moderate‐density and high‐density) using Landsat data and the Maximum Likelihood classification method, which produced an overall accuracy ranging from 66.8% to 99.9%. Ait El Haj et al (2022) obtained an overall accuracy ranging from 80% to 93% in a study of land cover/land use change and vegetation cover distribution in the Moroccan region of Béni Mellal‐Khénifra, based on Landsat images and the Maximum Likelihood classification method. This algorithm provided higher accuracies (100%) in another land cover change study conducted in the Moroccan Western High Atlas (Omdi et al, 2017) using the same data source (Landsat images).…”

Section: Discussionmentioning

confidence: 99%

A geospatial approach to assess habitat diversity and inform conservation efforts using a case study from North Africa

et al. 2023

View full text Add to dashboard Cite

Geographic Information System (GIS) combined with remote sensing and field data was used to identify and map Souss-Massa National Park (SMNP) habitats, an exceptionally biodiverse coastal area located in Morocco. Habitats in fenced and unfenced areas were compared to assess the fencing effectiveness for conserving semi-arid/ arid environments. Sentinel-2A level-2A data acquired on March 25, 2021, were analysed using the Semi-Automatic Classification Plugin in QGIS software. The supervised classification technique and the maximum likelihood classifier were used. Ground truthing consisted of training data used for the supervised classification and reference data (850 validation points) used for accuracy assessment based on the confusion matrix method. SMNP composed of 11 natural habitat classes (70.91%) and 6 disturbed habitat classes (29.09%). The overall accuracy (>96%) and kappa coefficient (0.95) showed a high classification accuracy. Fenced areas were dominated by natural habitats (>88%), whereas unfenced areas contained more disturbed habitats (>51%), indicating that fencing was an effective conservation tool. Natural habitats in unfenced areas are largely being modified and degraded, and therefore, monitoring and conservation actions should be further strengthened. The presented methodology shows a promising application potential in future and related ecological studies conducted in other regions, notably developing countries. Résumé Le Système d'Information Géographique (SIG) combiné à la télédétection et aux données de terrain a été utilisé pour identifier et cartographier les habitats du Parc National de Souss-Massa (PNSM), une zone côtière à la biodiversité exceptionnelle située au Maroc. Les habitats dans des zones clôturées et non clôturées ont été comparés pour évaluer l'efficacité des clôtures pour la conservation des environnements semi-arides/ arides. Les données de Sentinel-2A de niveau 2A acquises le 25 mars 2021 ont été analysées à l'aide du Plugin de Classification Semi-Automatique du logiciel QGIS. La technique de classification supervisée et le classificateur de vraisemblance maximale ont été utilisés. La vérification au sol consistait en des données de formation utilisées pour la classification supervisée et des données de référence (850 points de validation) | 423 ELBAHI et al.

show abstract

Section: Discussionmentioning

confidence: 99%

A geospatial approach to assess habitat diversity and inform conservation efforts using a case study from North Africa

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Monitoring LULC changes provides comprehensive information on changes in the potential of carbon sequestration of forest land [25].LULC used by Benzougagh et al2023 to evaluate soil susceptibility in the Inaouene watershed [26]. Ait El Haj et al,2022 used LULCC maps to assess the vegetation cover subjected to soil erosion in Lakhdar sub-basin [27].LULC was used in a study focusing on the relationship between LST and urban morphological parameters in Nanjing, China [6]. Zeng et al, 2018 conducted a LULC change study using the Global Forest Change of Hansen to evaluate Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century [28].…”

Section: Introductionmentioning

confidence: 99%

Accuracy assessment and uncertainty of 10-m resolution land use land cover maps at the local scale. Case: Talassemtane National Park, Morocco

Chemchaoui,

Alaoui,

Ghazi

et al. 2024

Preprint

View full text Add to dashboard Cite

Global land use land cover (LULC) maps are essential for monitoring forest dynamics and are a useful tool for any spatial planner. An increasing number of institutes are interested in producing such maps, especially with freely available high-resolution imagery and the emergence of new classification techniques. However, these maps should be submitted to an accuracy assessment for more accurate and effective use at the regional, national, and local scales. In this study, two products, the European Space Agency World Cover 2020 (ESA WC2020) and Sentinel-2 10 m Land Use/Land Cover Time Series (ESRI LC2020), were subjected to an accuracy assessment at the local scale, particularly in Talassemtane National Park (TNP). The results showed an overall accuracy of 56.90 % for the ESA product and 83.56% for the ESRI product. Forest cover is the most accurate land cover class for both products. In addition, area-based accuracy was used to adjust each class’s area and evaluate uncertainty. Finally, spatial agreement between the two products was achieved using change detection analysis, which showed a result of 70% at the local level. This work will be used in studies in the TNP area to distinguish between forest and non-forest land, particularly to monitor deforestation.

show abstract

“…Landsat is widely used (Ait El Haj et al, 2023;Cai et al, 2020;Dhaloiya et al, 2023;Milanović et al, 2019) by application of NDVI index (Ozyavuz, et al, 2015;Sonali et al, 2021) and supervised classification (Bispo et al, 2013;Salata, 2021). A study in Benslimane, nearby area, is carried out by Hammouyat et al, using the same process, revealed that the forest is declined by 11.4 % losing nearly 200 ha/year (Hammouyat et al, 2022), andHoussni et al (2018) showed that high formations, low formations, and matorrals have regressed by 62%, 70%, and 49% respectively in the Western Rif region of Morocco (Houssni et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of climate and demographic changes on the vegetation of the M’goun Geopark UNESCO of Morocco (1984-2021)

Youssef,

Abdelaziz,

Aboubakre

et al. 2024

Ingeo

View full text Add to dashboard Cite

The primary goal of this study is to identify changes in the vegetation cover of the geopark M’goun region of Morocco, as a sample area to track the Moroccan vegetation status, and the crucial factors that influenced its evolution between 1984 and 2021. The NDVI (Normalized Difference Vegetation Index) method has been used, combined with supervised classification manipulated with the Google Earth Engine and through statistical analysis using R, together with field trips and Google Earth records for supervised classification. The percentage of vegetation density, made up of oak groves, Matorrals, red juniper, and thuja, in addition to a highly biodiverse cover, displays a declining trend. Medium-cover vegetation decreased by 29.5%, dense vegetation decreased considerably by 70.9%, and low-cover vegetation saw a minimal decline of 0.02%. As a result, dense and medium vegetation are being replaced by bare lands or poor-quality cover formed by sparse plants and solitary trees. In addition, precipitation increased slightly, showing an irregular trend, with average minimum and maximum temperatures rising by +1.7°C and +1.4°C, respectively. Furthermore, the population increased by 84.47%. Statistical analysis showed that the most important parameters affecting medium and dense vegetation cover are temperatures (Tmin and Tmax) and population density, as evidenced by the strong correlation between them. In contrast, sparse vegetation cover showed less correlation due to its heterogeneity. However, precipitation played a minimal role in vegetation evolution. Change detection maps revealed a significant negative impact on vegetation cover. This degradation was localized in the southern part of the area of study, where, according to the demographic density map, the majority of the population resides. Deforestation continues due to the lifestyle and economic activities of the local population. These factors, combined with climate change, are exerting large-scale pressure on the forest throughout the geopark.

show abstract

Monitoring Land Use and Land Cover Change Using Remote Sensing Techniques and the Precipitation-Vegetation Indexes in Morocco

Cited by 10 publications

References 10 publications

A geospatial approach to assess habitat diversity and inform conservation efforts using a case study from North Africa

A geospatial approach to assess habitat diversity and inform conservation efforts using a case study from North Africa

Accuracy assessment and uncertainty of 10-m resolution land use land cover maps at the local scale. Case: Talassemtane National Park, Morocco

Impact of climate and demographic changes on the vegetation of the M’goun Geopark UNESCO of Morocco (1984-2021)

Contact Info

Product

Resources

About