Topic: Investigating, Algorithm to Estimate Shallow Water Depth Data from Multi-temporal Satellite Imageries and Implementing with GRASS GIS.
Presenter : Vinayaraj poliyapram
Open source geospatial software’s have been successfully using for several remote sensing application. Here, this paper made an effort to estimate multi-temporal coastal bathymetry changes from remotely sensed images using GRASS GIS software. Several tools (r.mapcalc, r.regression.line, r.regression.multi, r.contour etc.) from GRASSGIS have been used to estimate bathymetry. Depth data is especially important for near coastal lines, in harbours, and near shoals and banks, where changes can occur rapidly as sedimentation, erosion and scouring of channels alters underwater topography. Here, a simple method for estimating water depths from single spectral band is described and is applied to multi-temporal and multi-source passive remote sensing data such as Landsat 7, Landsat 8 and ASTER data. The proposed empirical method is a combination of physical and statistical model. Two different methods; Single Band Algorithm (SBA) and Radiance Based Estimation (RBE) were applied to estimate depth data from Near Infra Red (NIR) band of multi-temporal images. The RBE, basically proposed in this study can be applied in the absence of in-situ depth data. Both methods assume a constant attenuation coefficient and homogenous bottom type all over the study area. Lyzenga’ noise correction method has been used to remove atmospheric and water column reflectance. The accuracy of the depth algorithm is determined by comparison with ground-truth measurements. The correlation coefficient of least square fit and RMSE detect the good quality of the depth data derived by the proposed method. GRASS GIS was feasible and efficient for bathymetry estimation from remotely sensed data.