The development and widespread deployment of new-generation satellites for Earth Observation (EO) have dramatically enhanced the precision and scope of environmental and infrastructural monitoring. Equipped with advanced technologies such as SAR and multispectral sensors, these systems enable detailed assessments of infrastructure stability, provide high-resolution mapping of coastal erosion, and facilitate the evaluation of natural hazards, including floods and seismic events. The integration of multispectral data, capturing a broad range of frequency bands, introduces novel applications and deeper insights into dynamic environmental processes. Coastal environments and the land–sea interface present particularly significant challenges due to their inherent complexity and ecological sensitivity. These regions are crucial for the protection of valuable habitats and require the implementation of sustainable engineering principles to mitigate the associated risks. Processes such as coastal erosion, the pollution of fragile ecosystems, slope instability, and chemically aggressive conditions resulting from elevated chloride concentrations can severely affect transportation infrastructure in these areas, leading to potential damage or structural failure. Advanced satellite-based monitoring provides a transformative solution to address these challenges, enabling more effective sustainable management and enhancing the resilience of these critical regions. In this context, the computation of indices derived from multispectral data, including the Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Vegetation Index (NDVI), provides quantitative metrics for water body delineation, vegetation water content estimation, and land cover change detection. This study presents preliminary findings on the integration of multi-resolution datasets processed through spectral indices to identify and quantify variations in reflectance properties associated with transport infrastructure and its surrounding environments. These methodologies are designed to inform detailed on-site analyses, utilizing conventional inspection techniques such as total stations and drone-based surveys. An integrated analysis was conducted, utilizing historical time series data from spectral indices derived from the Sentinel-2 multispectral sensor. MT-InSAR was employed to detect and quantify displacements affecting transport infrastructure, while the NDWI and NDMI were applied to monitor temporal variations in coastal regions, facilitating the detection of land cover changes and enabling a comprehensive assessment of both infrastructure and coastal dynamics.