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Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model
The common practice adopted in previous attempts on Satellite-Derived Bathymetry (SDB) has been to calibrate a single set of coefficients using global regression model. In this study we propose an Adaptive-Geographically Weighted Regression (A-GWR) model that takes into account local factors in determining the regression coefficients. A-GWR model is examined as an effective solution for addressing heterogeneity and could provide better water depth estimates in near-shore region. The study has been carried out for a 30-km stretch and covers 160 km 2 of a complex near-shore coastal region of Puerto Rico, Northeastern Caribbean Sea. Medium-resolution (Landsat-8) and high-resolution (RapidEye) images were used to estimate water depth. Results demonstrate that the A-GWR model performs well in estimating bathymetry for shallow water depths (1-20 m), showing the correlation coefficient (R) of 0.98 and 0.99, determination coefficient (R 2 ) of 0.95 and 0.99 and Root Mean Square Error (RMSE) of 1.14 and 0.4 m for Landsat-8 and RapidEye, respectively. The data-processing workflow has been entirely implemented in an Open-Source GIS environment and can be easily adopted in other areas.
Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model
The common practice adopted in previous attempts on Satellite-Derived Bathymetry (SDB) has been to calibrate a single set of coefficients using global regression model. In this study we propose an Adaptive-Geographically Weighted Regression (A-GWR) model that takes into account local factors in determining the regression coefficients. A-GWR model is examined as an effective solution for addressing heterogeneity and could provide better water depth estimates in near-shore region. The study has been carried out for a 30-km stretch and covers 160 km 2 of a complex near-shore coastal region of Puerto Rico, Northeastern Caribbean Sea. Medium-resolution (Landsat-8) and high-resolution (RapidEye) images were used to estimate water depth. Results demonstrate that the A-GWR model performs well in estimating bathymetry for shallow water depths (1-20 m), showing the correlation coefficient (R) of 0.98 and 0.99, determination coefficient (R 2 ) of 0.95 and 0.99 and Root Mean Square Error (RMSE) of 1.14 and 0.4 m for Landsat-8 and RapidEye, respectively. The data-processing workflow has been entirely implemented in an Open-Source GIS environment and can be easily adopted in other areas.
Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model
Vinayaraj, Poliyapram (author) / Raghavan, Venkatesh / Masumoto, Shinji
Marine geodesy ; 39
2016
Article (Journal)
English
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