This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts.

Les plans d'aménagement de la forêt de la Maâmora au Maroc ont favorisé l'extension des plantations d'espèces exotiques aux dépens du Chêne-liège (Quercus suber), espèce climacique. Les sécheresses récurrentes et la perspective des changements climatiques imposent de revoir le choix des essences pour les reboisements dans cette forêt. L'objectif de ce travail consiste à évaluer les relations hydriques et la croissance des plants du Gommier rouge (Eucalyptus camaldulensis), du Thuya de Berbérie (Tetraclinis articulata), du Chêne-liège (Quercus suber) et du Pin maritime des Landes (Pinus pinaster ssp. atlantica), soumis à un déficit hydrique édaphique continu. Celui-ci a négativement et significativement affecté le statut hydrique des plants et leur croissance, mais l'effet a varié significativement selon les espèces et la durée d'assèchement. Le statut hydrique et la croissance des plants du Gommier rouge ont été plus affectés négativement par l'assèchement que ceux des autres espèces. La perte de croissance des plants du Gommier rouge a été de 22 à 65 % selon les paramètres d'évaluation. Le reboisement dans la forêt de la Maâmora devra favoriser le Chêne-liège, le Pin maritime des Landes et éviter le Gommier rouge. Par ailleurs, le Thuya de Berbérie mériterait d'être introduit dans les biotopes les moins favorables au Chêne-liège.

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.

In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R² value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R² of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management.

Les plans d'aménagement de la forêt de la Maâmora au Maroc ont favorisé l'extension des plantations d'espèces exotiques aux dépens du Chêne-liège (Quercus suber), espèce climacique. Les sécheresses récurrentes et la perspective des changements climatiques imposent de revoir le choix des essences pour les reboisements dans cette forêt. L'objectif de ce travail consiste à évaluer les relations hydriques et la croissance des plants du Gommier rouge (Eucalyptus camaldulensis), du Thuya de Berbérie (Tetraclinis articulata), du Chêne-liège (Quercus suber) et du Pin maritime des Landes (Pinus pinaster ssp. atlantica), soumis à un déficit hydrique édaphique continu. Celui-ci a négativement et significativement affecté le statut hydrique des plants et leur croissance, mais l'effet a varié significativement selon les espèces et la durée d'assèchement. Le statut hydrique et la croissance des plants du Gommier rouge ont été plus affectés négativement par l'assèchement que ceux des autres espèces. La perte de croissance des plants du Gommier rouge a été de 22 à 65 % selon les paramètres d'évaluation. Le reboisement dans la forêt de la Maâmora devra favoriser le Chêne-liège, le Pin maritime des Landes et éviter le Gommier rouge. Par ailleurs, le Thuya de Berbérie mériterait d'être introduit dans les biotopes les moins favorables au Chêne-liège.