A diachronic study of Ourika watershed land in the High Atlas of Morocco

Elmalki, M.; Mounir, Fouad; Ichen, Abdellah; Khai, Thami; Aarab, Mohammed

doi:10.1051/e3sconf/202123400080

Cited by 3 publications

(2 citation statements)

References 7 publications

(3 reference statements)

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“…Its climate is variable, with differences in rainfall and temperature driven by factors such as altitude, topography, and proximity to the Atlantic Ocean. The higher elevations have cooler temperatures and receive much more precipitation, which can reach 700 mm/year, while the lower elevations are warmer and drier [46]. The region experiences seasonal variability in rainfall characterized by a wet season from November to March and a dry season from April to October.…”

Section: Study Areamentioning

confidence: 99%

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Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning

Meliho

Boulmane²,

Khattabi

et al. 2023

Remote Sensing

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Soil organic carbon (SOC) is an essential component, which soil quality depends on. Thus, understanding the spatial distribution and controlling factors of SOC is paramount to achieving sustainable soil management. In this study, SOC prediction for the Ourika watershed in Morocco was done using four machine learning (ML) algorithms: Cubist, random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM). A total of 420 soil samples were collected at three different depths (0–10 cm, 10–20 cm, and 20–30 cm) from which SOC concentration and bulk density (BD) were measured, and consequently SOC stock (SOCS) was determined. Modeling data included 88 variables incorporating environmental covariates, including soil properties, climate, topography, and remote sensing variables used as predictors. The results showed that RF (R2 = 0.79, RMSE = 1.2%) and Cubist (R2 = 0.77, RMSE = 1.2%) were the most accurate models for predicting SOC, while none of the models were satisfactory in predicting BD across the watershed. As with SOC, Cubist (R2 = 0.86, RMSE = 11.62 t/ha) and RF (R2 = 0.79, RMSE = 13.26 t/ha) exhibited the highest predictive power for SOCS. Land use/land cover (LU/LC) was the most critical factor in predicting SOC and SOCS, followed by soil properties and bioclimatic variables. Both combinations of bioclimatic–topographic variables and soil properties–remote sensing variables were shown to improve prediction performance. Our findings show that ML algorithms can be a viable tool for spatial modeling of SOC in mountainous Mediterranean regions, such as the study area.

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Section: Study Areamentioning

confidence: 99%

“…lower elevations are warmer and drier [46]. The region experiences seasonal variability in rainfall characterized by a wet season from November to March and a dry season from April to October.…”

Section: Study Areamentioning

confidence: 99%

Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning

Meliho

Boulmane²,

Khattabi

et al. 2023

Remote Sensing

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Impact of climate change on forest resources: Case of Quercus rotundifolia, Tetraclinis articulata, Juniperus phoenicea, J. oxycedrus, J. thurifera and Pinus halepensis

Elmalki

Ichen²,

Aarab³

et al. 2022

Biosys. divers.

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Forest resources in the Ourika watershed are subject to several anthropogenic and climatic degradation factors. As for the human factor, this degradation of forest resources is explained by the bad practices exercised by the local population expressed by the cutting of live wood, carbonization, and overgrazing. In terms of the climatic factor, the decrease in the amount of rainfall and the increase in temperature contribute to the exacerbation of the degradation of these resources. In order to better understand the evolution of plant cover in a changing climate context, this study highlights an assessment of the impact of climate change on forest dynamics based on a process-based model at the forest landscape scale which makes it possible to simulate the changes according to growth, succession, disturbances (fire, wind, insects, etc), forest management, and land use change. This analysis is based on the use of the LANDIS-II model and the PnET-succession extension. Projections of the dynamics of forest communities are made using climate projections from the Japanese global circulation model adopted by Morocco (model for interdisciplinary research on climate – earth system models) and this by adopting the two climate scenarios , representative concentration pathways 4.5 and 8.5. The results obtained highlight the spatial distribution of the ecosystems studied after 100 years with a quantitative evaluation of the total average biomass of these resources as a function of climatic disturbances. In general, the estimated total biomass will decline over the coming years under the joint effect of the climate change and the aging of forest stands, while on the other hand, the distribution of potential areas for species settlement remains independent of the effect of these climate changes.

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The degradation of forest areas in Morocco: Case of Benslimane province

et al. 2022

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This article aims to shed light on the process of known degradation of the forest area of Benslimane province during the period 1990–2020 and to specify the most important human causes which contributed to it (quarries, extension of the built-up area, the impact of agricultural activities, grazing and collection of firewood), by using remote sensing techniques (spatial images for the years 1990–2000–2010–2020) to produce Land Cover maps. The following satellite images were used, Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI, with a spatial precision of 30 m, the Semi-Automatic Classification Plugin (SCP) in QGIS was used for atmospheric correction, and the Spectral Angle Mapping algorithm for the images’ classification. The rating evaluation of the Kappa coefficient shows the following ratios for the years 1990–2000–2010–2020 respectively ; 0.89–0.90–0.90–0.93. The results showed that the forest area of Benslimane province has declined by 11.4% or about 6,027.7 ha between 1990–2020 at the rate of 200 ha/year, which has been turned into matorral land or bare land. This forest also lost 35.2% of its vegetative density and has become much sparser, while the original grazing areas surrounding it have been reduced by 50.4%. Moreover, the area of quarries increased by 1,097.4%, the percentage of built-up area increased by 328.2%, and the agricultural area expanded by 32.7%. These results can be used as preliminary data for future studies and can help policymakers focus on the real drivers of forest degradation, in order to develop interventions to ensure the sustainability of natural resources.

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A diachronic study of Ourika watershed land in the High Atlas of Morocco

Cited by 3 publications

References 7 publications

Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning

Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning

Impact of climate change on forest resources: Case of Quercus rotundifolia, Tetraclinis articulata, Juniperus phoenicea, J. oxycedrus, J. thurifera and Pinus halepensis

The degradation of forest areas in Morocco: Case of Benslimane province

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