Andrea Petroselli

This study carefully assesses the capability of supervised machine learning classification algorithms in identifying land cover (LC) in the context of the Jhelum River basin in Kashmir. Sentinel 2 and Landsat 8 high-resolution data from two satellite sources were used. Through preprocessing techniques, we removed any potential noise inherent to satellite imagery and assured data consistency. The study then utilized and compared the skills of the supervised algorithms random forest (RF) and support vector machine (SVM). A hybrid approach, amalgamating classifications from both methods, was also tested for potential synergistic enhancements in accuracy. Using a stratified random sampling approach for validation, the SVM algorithm emerged with a commendable accuracy rate of 82.5%. Using simulations from 2000 to 2015, the soil and water assessment tool (SWAT) model was used to further explore the hydrological effects of LC alterations. Between 2009 and 2019, there were discernible changes in the land cover, with a greater emphasis on ranges, forests, and agricultural plains. When these changes were combined with the results of the hydrologic simulation, a resultant fall in average annual runoff—from above 700 mm to below 600 mm—was seen. With runoff values possibly ranging between 547 mm and 747 mm, the statistics emphasize the direct effects of urban communities encroaching upon forest, agricultural, and barren lands. This study concludes by highlighting the crucial role that technical pipelines play in enhancing LC classifications and by providing suggestions for future water resource estimation and hydrological impact evaluations.

In the last decades, the Rif area in Morocco has been frequently affected by soil erosion due to intense rainfall events. In order to help farmers improve their lives and avoid damages caused by this phenomenon, a management project (the MCA Project) aiming to grow fruit trees has been realized. The objective of this study was to evaluate, in three provinces of Morocco, the effect on the hydrological response of selected erosion control management techniques combined with olive tree plantations. The investigated variables were the final infiltration (If), the imbibition of rainwater (Pi), the runoff coefficient (Kr), and the soil detachment (D). In particular, for each investigated soil utilization, three replications of a rain simulation test (80 mm/h) and soil sampling were conducted. Results for surface conditions demonstrate that under vegetation in matorral and fallow, the surface is covered at more than 75% with a high content of organic matter (OM) at 4.5% and 2.6%, respectively. Despite the compaction observed in those land uses, the surface area opened exceeded 90% in the study area. Regarding the soils physical properties, they were rich in silt at more than 40%; the rate of porosity is high where bulk density is low. At the Taounate site, low porosity was at 62% in fallow and at 55% in plowing, with high densities of 1.01 g/cm2 and 1.2 g/cm2, respectively. Tests also demonstrate that vegetation has an important role in moisture conservation in the depths of 0 to 10 cm at all sites with macroagregate stability (MA%) compared to plowing sites. In terms of soil hydrology, vegetation reduces the runoff because, under matorral (it was at 0%), it avoids soil erosion.