## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### GIS for Shoreline Change Analysis and Coastal Monitoring  
The Geographic Information System (GIS) has become an indispensable tool in contemporary shoreline monitoring, facilitating the precise spatial analysis and visualization of coastal change [67]. Coupled with tools such as the Digital Shoreline Analysis System (DSAS), the GIS can facilitate the processing of historical positions of the shoreline, compute erosion and accretion rates, and visualize long-term coastal evolution. This facilitates large-scale geospatial data management, predictive modeling, and decision-making in coastal management. Recent studies have utilized GIS to efficiently analyze shoreline dynamics and long-term coastal changes. With the aid of the Digital Shoreline Analysis System (DSAS) [59], used as an add-on tool in ArcGIS, researchers could undertake the extraction, quantification, and prediction of shoreline changes with high accuracy. GIS plays a critical role in understanding erosion and accretion patterns and the impacts of natural and human activities on coastal environments. It enables the creation of better and more knowledgeable coastal management and protection policies. Table 2 (GIS-based approaches to shoreline change detection) reveals concentrated information highlighting the main findings with the core technologies Sentinel-2 and Landsat aided by GIS. [PERSON] et al. [11] investigated the long-term shoreline evolution in Quang Nam Province, Vietnam, with a temporal resolution of 1990 to 2019. Employing the DSA, he analyzed satellite imagery erosion and accretion patterns, indicating significant coastal changes caused by natural processes and human activities. The methodology attempted to extract historical coastline positions from all available data scenes of Sentinel-2 and Landsat satellite series, i.e., Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETMP), and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS), over the period of 1990-2019. Satellite images with a percentage below 20% land cloud cover were used for the analysis. Then, all available data were collected and utilized to detect the coastline dynamic. The estimated change rate in Quang Nam province, employing the modified Normalized Difference Water Index (mNDWI) for shoreline detection, was used to classify water and land regions on satellite images with stable performances compared to the NDWI, which contains built-up land noises. The resultsfrom the changes using the ArcGIS add-in toolkit DSAS showed that the EPR and LRR indices had a strong correlation (R\({}^{2}\) = 0.96). The results identified 401 erosional and 414 accretional transects in a coastline length of 81.5 km, emphasizing the dynamic nature of the coastline, with significant erosion observed in the north of the Cua Dai estuary and notable accretion in its southern segment. [PERSON] et al. [31] conducted research on the changes in the shoreline variability of Joao Pessoa City (Brazil) over the last 34 years (1985-2019), using multitemporal remote sensing data from Landsat 5-TM and 8-OLI satellites as a dataset, which were downloaded from the Google Earth Engine (GEE). Statistical techniques were performed, such as EPR for short-term analysis and LRR and Weighted Linear Regression (WLR) for long-term analysis provided by the DSAS. The technique was used to analyze 351 transects across 4 distinct zones, involving a detailed examination of the factors influencing the shoreline changes, including data such as the sea level, tidal range, wave height, beach morphology, and ocean currents. The statistical methods used included Standard Deviation (SD) and the Coefficient of Variance (CV), which are capable of interpreting the spatiotemporal changes along coastline transects. In one case for Zone 1 during the period of 2014-2019, the CV (2064.2) and SD (3.89) had high values, which implied inconsistency among the data, validating the need to use statistical methods for correct data interpretation.