The integration of remote sensing (RS) and Geographic Information Systems (GIS) has become a cornerstone in spatial analysis and modeling, offering powerful tools for environmental monitoring, urban planning, natural resource management, and disaster management. Remote sensing provides detailed and timely information about the Earth's surface through satellite or aerial imagery, while GIS enables the storage, manipulation, analysis, and visualization of spatial data. The integration of these two technologies enhances the capacity to model complex spatial phenomena and supports informed decision-making. Several methods exist for effectively combining RS and GIS, which can be broadly categorized into data-level integration, feature-level integration, and decision-level integration. Data-level integration involves the direct use of remotely sensed data within GIS platforms. This method starts with preprocessing of RS data, which includes radiometric and geometric corrections to ensure accuracy and consistency. Once preprocessed, the raster data from RS can be imported into a GIS environment, where it can be overlaid with existing vector data such as administrative boundaries, transportation networks, or land-use maps. Techniques such as image classification (supervised and unsupervised), change detection, and spectral indices like NDVI (Normalized Difference Vegetation Index) are commonly employed to extract meaningful information from the RS data. The results can then be spatially analyzed in GIS using tools like buffer analysis, overlay analysis, and spatial interpolation. For example, in land cover change modeling, classified satellite imagery can be combined