Technical applications of Remote Sensing with optical sensors for non-invasive diagnostic environmental analyses

This dissertation explores the role of optical remote sensing technologies, particularly multispectral and hyperspectral sensors, in environmental monitoring. It underscores how these platforms provide essential data for understanding Earth’s dynamics and human-environment interactions, aligning with global sustainability goals. By analyzing the spectral and spatial characteristics of multispectral and hyperspectral sensors, the study demonstrates their distinct advantages in monitoring diverse environmental variables such as land cover, vegetation health, and soil composition. While multispectral sensors excel in spatial resolution, hyperspectral sensors offer superior spectral detail, enabling more precise quantitative analysis. Two case studies are presented to illustrate the sensors’ applications: risk assessment in urban environments and the early detection of soil salinization. These case studies emphasize the importance of selecting the appropriate sensor based on the specific analysis context. The dissertation validates the continued evolution of remote sensing technologies, driven by advancements in sensor accuracy, atmospheric correction methods, and mission management strategies. The integration of multispectral and hyperspectral data is proposed as a future research direction, combining the strengths of both platforms for enhanced environmental diagnostics. Further advancements will focus on improving sensor resolution, reducing revisit times, and fostering inter-agency collaboration for broader data accessibility. Ultimately, this research contributes to the expanding role of remote sensing in environmental monitoring, highlighting its potential to advance our understanding of Earth’s processes and the impact of human activities.