## 3 Materials and Methods
### Remote Sensing Indices and Analytical Tools for Shoreline Monitoring  
Remote sensing core technologies, in combination with analytical tools, provide a solid approach to monitoring alterations and shoreline variability [72]. The most common  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **Sentinel-1** & **Sentinel-2** & ** Landsat** & **GIS** & **UAV/Drone** & \
\begin{tabular}{c} **Ground** \\ **Truth Data** \\ \end{tabular} &
\begin{tabular}{c} **AI/Machine** \\ **Learning/Neural** \\ **Network** \\ \end{tabular} \\ \hline No. of Articles (n = 58) & 3/58 & 21/58 & 53/58 & 55/58 & 1/58 & 21/58 & 9/58 \\ \hline Percentage (\%) & 5.17\% & 36.21\% & 91.38\% & 94.83\% & 1.72\% & 36.21\% & 15.52\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dominant techniques and methods used in reviewed studies on shoreline detection (2021–2024). The table presents a thematic classification of 58 reviewed articles based on the primary technologies applied—such as Sentinel-1/2, Landsat, GIS, UAV, Ground Truth Data (GTD), and AI/ML—highlighting the prevalence of each technique across the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mission** & **Sentinel-1** & **Sentinel-2** & ** Landsat-8/9** \\ \hline Agency & \begin{tabular}{c} ESA (European \\ Space Agency) \\ \end{tabular} & \begin{tabular}{c} ESA (European \\ Space Agency) \\ \end{tabular} & NASA/USGS \\ \hline First Launch Year & 2014 (Sentinel-1A) & 2015 (Sentinel-2A) & \begin{tabular}{c} 2013 (Landsat-8), 2021 \\ (Landsat-9) \\ \end{tabular} \\ \hline Revisit Time & 6 days (combined 1A \& 1B) & 5 days (combined 2A \& 2B) & 16 days each \\ \hline Spatial Resolution & 10 m (SAR) & \begin{tabular}{c} 10 m (VIS/NIR), 20 m \\ (RE/SWIR), 60 m \\ (Atmospheric) \\ \end{tabular} & \begin{tabular}{c} 15 m (panchromatic), 30 m \\ (multispectral), 100 m \\ (thermal) \\ \end{tabular} \\ \hline Spectral Bands & C-band SAR & 13 spectral bands & 11 spectral bands \\ \hline Swath Width & 250 km & 290 km & 185 km \\ \hline Temporal Coverage & 2014–present & 2015–present & 2013–present \\ \hline Primary Applications & \begin{tabular}{c} All-weather imaging, \\ terrain motion, \\ flood monitoring \\ \end{tabular} & \
\begin{tabular}{c} Land cover, vegetation, \\ water bodies, coastal zones \\ \end{tabular} &
\begin{tabular}{c} Land use/land cover \\ change, agriculture, \\ hydrology \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of key characteristics of Sentinel-1, Sentinel-2, and Landsat 8/9 satellite missions commonly used in shoreline detection and coastal erosion studies. The table summarizes each mission’s spatial, spectral, and temporal resolution, data type, and key applications relevant to coastal monitoring.  
analytical tools employed for addressing the purpose of shoreline analysis include the indices of the Normalized Difference Water Index (NDWI), as stated by [PERSON] et al. [73], and the Digital Shoreline Analysis System (DSAS). While NDVI is not typically used for direct shoreline extraction, it has been included in some studies [67] to monitor vegetation loss adjacent to shorelines, indirectly indicating erosion processes in sensitive coastal zones [9; 50]. In shoreline contexts, NDVI is useful for detecting vegetative dune systems or mangrove degradation [74], which serve as early indicators of erosion-prone zones.  
Therefore, these specialized indices and methodologies will enable accurate monitoring results and data analysis, which are fundamentally required at the formulation and implementation levels of an effective coastal management strategy [21; 41; 50]. The selection of those specific remote sensing indices and GIS techniques for shoreline analysis that we refer to is based on the popularity of the reviewed articles.