LAUSR

ABSTRACT A novel methodological approach is presented to estimate the shoreline change rate in complex coastal settings by using multi-source/multi-temporal shoreline data extracted from both orthoimages and digital elevation models. Several stages are covered to integrate the information available for the coastal area under study by means of a well-balanced and robust data fusion approach: i) determining the most accurate approach for extracting each shoreline from the different available sources, also estimating their corresponding accuracy, ii) finding out the best set of multi-temporal shorelines, within the available ones, to help avoid temporal oversampling and remove local severe shoreline change rate oscillations and, finally, iii) testing linear regression methods to include shoreline accuracy and automatic outlier removal in order to model the underlying general trend of shoreline evolution. A reweighted-weighted linear regression together with an evenly distributed shorelines dataset were chosen as the best approach to robustly integrate all the multisource remote sensing data available and determine which shorelines did not follow the general linear trend.