Innovative Geospatial Monitoring Strategies for Climate Change Coastline Environments

Oceans and seas are crucial components of the climate system and valuable assets for human activities. Natural and anthropogenic forcing are primary drivers of marine pollution, leading to water quality degradation and posing direct threats to ecosystems. In recent decades, in line with the European Marine Strategy Framework Directive (MSFD), researchers, along with public and private institutions, have increasingly focused on developing innovative monitoring strategies for coastal environments to promote sustainable use. In fact, the synoptic-scale variability and dynamic nature of these environments require alternatives to traditional in-situ data sampling approaches. Developed at DIGISKY S.R.L., this thesis aims to illustrate the pivotal role of various remote sensing technologies in supporting coastal monitoring programs, processing satellite and aerial photogrammetric data, and delivering relevant georeferenced products. A brief overview of electromagnetic principles and the spectral properties of water bodies addresses the question: Is it possible to correlate optical and thermal measurements with water quality parameters? Additionally, an examination of different acquisition platforms highlights their respective advantages and limitations, emphasizing the importance of a multilevel monitoring strategy. To demonstrate this approach, a case study on the Genoa coastline is conducted. Satellite data from the Landsat 8-9 Thermal Infrared Sensor and aerial thermal imagery are processed using commercial software (ENVI and Agisoft Metashape) and compared with in-situ sampling campaigns conducted by the Regional Agency for the Environmental Protection (ArpaL). Analysis in an open-source GIS environment (QGIS) assesses the accuracy and reliability of sea surface temperature maps derived from remote sensing data in reflecting actual water temperature. Results underscore the importance of coordinating spatially and temporally overlapping surveys to produce calibrated aerial thermography at 20 cm/px resolution. Following this methodology, potential correlations between additional water quality parameters, such as chlorophyll-a and turbidity, and remote sensing data are explored. Developing statistically significant regression models provides a means of estimating these parameters without frequent point-specific surveys. From a commercial perspective, validating this workflow offers a more efficient coastal monitoring solution, overcoming spatial, temporal, and financial limitations.