## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### GIS for Shoreline Change Analysis and Coastal Monitoring
The study identified cyclical patterns of erosion and accretion within specific periods, indicating that various environmental and human activities and factors influence the coastal area's dynamics, highlighting that the GIS plays a significant role in environmental monitoring and demonstrates the importance of coastal changes. [PERSON] et al. [38] used 96 Landsat images to carry out a long-term shoreline change analysis for the black sea coastline. The coastline was divided into 28 subsections with 78,943 transects. Landsat (MSS, TM, ETMP, and OLI) images from 1972-2018 were used, with a total of 96 images. The spatial resolution of Landsat 1,3,5 (MSS) is a 60 m resolution, and for Landsat 5 (ETM),7,8, the spatial resolution is 30 m. For the large area of the 5000 km coastline covered by the research, the spatial resolution was appropriate. Once again, the NDWI method was employed to acquire the base image for shoreline delineation. The author indicated that stable performance is provided by the NDWI method. In any case, the validation of the study results via the georeferencing and digitization of aerial photos or in situ measures was unavailable. The images were referenced with WGS84, and digitization was limited by converting automated extraction into a semi-automated process. The shoreline change was computed using the DSAS, which applied the EPR and LRR approaches. LRR indicated a \(-\)0.17/m shoreline change, which is considered logical according to a large-scale basin's erosion and accretion balance. The research detailed other countries and coastline sections, indicating local hotspots. Transects in the methodology matched similar investigations, leading us to conclude the validation of this method. [PERSON] et al. [32] analyzed the dynamic shoreline of Bangladesh over the last thirty years (1989-2019) with a five-year interval using the dataset of multi-temporal Landsat images with a 30 m resolution and a comparison of tidal conditions collected from 1989-2019. Manual digitization of shorelines was performed. The DSAS was used to analyze the transects. There were 12,000 transects cast with 50 m intervals, resulting in shoreline retreat. Supplementary analysis of polygonal was carried out in ArcGIS, where the net area for shoreline extraction was determined for the entire study period. The LRR method for the shoreline movement NSM was analyzed. Thus, net erosion and accretion for the whole area were carried out, tracing the spatial changes in the area. [PERSON] et al. [12] analyzed the short-term and long-term changes in the shoreline of Ganjam district, Odisha, India, between 1990 and 2019. Multiresolution and temporal datasets of Landsat sensor, Thematic Mapper (TM), Enhanced Thematic Mapper (ETM),and Operational Land Imager (OLI) datasets were used for the shoreline change analysis. The DSA was used in the GIS. Land and water discrimination methods, such as the NDWI, mNDWI, EWI (Enhanced Water Index), and WRI (Water Ratio Index), were employed to extract the shoreline. The authors continued with projections of the coastline's future line for the 2030-2040 period and the potential impact on the area. The analysis used the EPR, WLR, and trigonometric functions to examine shoreline changes from 1990 to 2019 and predict future conditions for 2030 and 2040. The short- to long-term shoreline assessments identified erosion. GIS-based techniques, enabled through the DSA, have been found to be invaluable for the analysis of long-term shoreline changes in varied coastal environments. Using Landsat and Sentinel-2 imagery, erosion and accretion patterns have been mapped successfully, with parameters such as the NDWI, mNDWI, and WLR improving shoreline extraction accuracy. The main findings and geospatial methodologies utilized in the studies under review are presented in Table 5, showing how the DSA allows for effective multi-temporal analysis of shoreline changes.