## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Ground Truth Data for Enhanced Coastal Monitoring  
Ground Truth Data (GTD) are instrumental in refining the accuracy and reliability of shoreline change detection inferred using satellite-based methodologies. High-precision field measurements like GNSS-RTK or GNSS-RTN surveys, Ground Control Points (GCPs) based on robotic total stations, terrestrial laser scanners (TLS), and RTK-corrected UAV-based photogrammetry are usually included in GTD [34; 67; 87]. These in situ measurements act as verification references for the corroboration of shoreline positions derived from remote sensing indicators like the NDWI and mNDWI and are frequently overlayed in the GIS space for the evaluation of spatial discrepancies using indices like RMSE or regression R\({}^{2}\)[9; 53; 57]. For instance, [PERSON] et al. [32] digitized 12,000 shoreline transects in order to calibrate extractions based on Landsat, whereas [PERSON] et al. [34] combined GTD with sediment transport modeling and real-time high-resolution video monitoring. In predictive modeling activities like those using the Kalman Filter, GTD are the key input necessary for affirming shoreline forecast reliability in dynamic settings [2; 15]. As tabulated in Table 6, GTD allow for the strong validation of outputs inferred from satellites, enhance the accuracy of indicators like EPR, LRR, and WLR, and indirectly support the reproducibility and credibility of coastal monitoring derived outcomes [56; 70]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**References** & **Geographical and Temporal Scope** & **GIS Usage** & **Satellite Data** & **Shoreline Change Metrics** & **Accuracy/Error** & **Key Findings** & **Unique Features** \\ \hline
[11] & Quang Nam & DSAS in ArcGIS & Sentinel-2 and for shoreline & Sentinel-2 and Landsat (TM, ATM, ATM, ATM) & EPR, LRR, mNDWI for shoreline detection, undertain analysis \(<1\) m; & 401 erosional and 414 directional transports & Focus on human and natural \\
[11] & Province, Vietnam, 1990-2019 & extraction and visualization & ETHAP, OOL/TIRS & shoreline detection analysis (R\({}^{2}\)-0.9) & Shoepline position uncertainty \(<1\) m; & 401 erosional and 414 directional transports & Focus on human and natural \\ \hline  & & & & DSAS regression R\({}^{2}\)-0.9; & correlation between & a tropical region, \\  & & & & & \(\pm\)0.02 m / yr rate error & EPR and LRR (R\({}^{2}\) = 0.96) & robust mNDWI \\ \hline  & & & & & (R\({}^{2}\) = 0.9) & application. \\ \hline  & & & & & & \\ \multirow{-2}{*}{[31]} & \multirow{-2}{*}{Joao Pessoa City,} & DSAS in ArcGIS, & Landsat 5-TM & EPR, LRR, mUL for long-term charge, cyclical and 8-OL from cryogenic earth & & & 351 transects showed & Added depth through CV and \\  & & & & & & & \\ \multirow{-2}{*}{[31]} & \multirow{-2}{*}{Jolo Pessoa City,} & DSAS in ArcGIS, & Landsat 5-TM & EPR, LRR, mUL for long-term charge, cyclical and 8-OL from cryogenic earth & & & 351 transects showed & Added depth through CV and \\  & & & & & & & \\ \multirow{-2}{*}{[31]} & \multirow{-2}{*}{Jolo Pessoa City,} & DSAS in ArcGIS, & Landsat 5-TM & EPR, LRR, shoreline & & & & \\ \multirow