## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Ground Truth Data for Enhanced Coastal Monitoring
& sand spit & \\ \hline
[6] & Indian Sundarbans & Field observations (1975–2017) & Landsat series + & DSAS (shoreline), field data & DSAS (shoreline), NDVI ( vegetation) & Widespread shoreline & & \\  & &  
[PERSON] et al. [2] analyzed remote sensing data and statistical methods to study decadal shoreline dynamics along India's Purba Medinipur-Balasore coastal stretch. The results of his study indicated a continuous erosion pattern from 1990 to 2020, with some portions showing accretion due to land reclamation. The Kalman Filter projection of the probable positions of the shoreline in 2031 and 2041 indicates urgent management needs along the coasts to protect against erosion impact and damage to communities that will be exposed. [PERSON] et al. [3] investigated Nijhum Dwip, Bangladesh, where land cover and shoreline changes are associated with a 20-year data timeline. The researchers observed that the primary changes in mangrove loss are driven by rapid settlement expansion due to population pressure and economic development. Shoreline accretion outpaced erosion, adding about 1153 hectares of land. This study underlines how rising sea levels coupled with anthropogenic activities require a well-managed strategy so that development can occur alongside the protection of the environment. [PERSON] et al. [15] presented an effective way of quantifying decadal shoreline changes on a large scale using remote sensing and geospatial analysis. The authors also hint at the possibility of urgent automated coastline monitoring, thus reducing the need to involve human labor. This is a very effective methodology that investigates long-term shoreline variations, which are of essence for making informed decisions in the management of the coastal zone. [PERSON] et al. [34] explored coastal erosion in Langue de Barbarie, Senegal, through predictive models based on environmental factors like wave action and sediment transport. Real-time observations combined with predictive modeling provided valuable insights into the various shoreline movements likely to happen in the future. This work also helps underline the value of these tools for improving coastal risk management strategies. [PERSON] et al. [6] examined shoreline changes and their impacts on mangrove ecosystems using the NDVI index in the Sundarbans, India. Through remote sensing, they documented rising sea levels and human activities resulting in unprecedented mangrove loss and shoreline retreat. The study emphasized the urgent need for sustainable shoreline protection, mainly to protect the biodiversity of this region and the people depending on it. Regarding the validation process, GTD play a very relevant role in enhancing the effectiveness of shoreline change detection since these are real-world, precise points used for the calibration and validation of remote sensing methodologies. For example, [PERSON] et al. [40] showed how ground truth data from RTK-GPS measurements increased shoreline position precision derived from Sentinel-2 imagery. In fact, by aligning outputs from satellite-based methods with ground-based observations, the study achieved a value of less than 5 m for RMSE using GTD corrections instead of more than 15. Similarly, [PERSON] et al. [9] discussed the potential of using GTD to reduce noise due to cover from clouds and water turbidity as opposed to what was seen on Landsat imagery to achieve consistent temporal datasets for shoreline position accuracies. The integration of GTD should be especially effective in such an environment where tidal effects are complex. [PERSON] et al. [57] used GTD acquired during high- and low-tide periods to correct the tidal effects in satellite-derived shorelines, reducing misclassifications by 25% in a coastal area like Chongming Island. [PERSON] et al. [67] further reiterated this using GTD to separate the natural shoreline from the anthropogenic features in the urbanized area. This approach resulted in a higher-than-15% increase in the delineation accuracy of shorelines.