Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations

Abstract

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.

Keywords

• land cover
• support vector machines
• random forest
• SWAT model
• ArcGIS
• Landsat