Safe nautical charts require a carefully designed bathymetric survey policy, especially in shallow sandy seas that potentially have dynamic sea floor patterns. Bathymetric resurveying at sea is a costly process with limited resources, though. A pattern on the sea floor known as tidal sand waves is clearly present in bathymetric surveys, endangering navigation in the Southern North Sea because of the potential dynamics of this pattern. An important factor in an efficient resurvey policy is the type and size of sea floor dynamics. The uncertainties of measurement and interpolation associated with the depth values enable the statistical processing of a time series of surveys, using deformation analysis. Currently, there is no procedure available that satisfies the Royal Netherlands Navy requirements. Therefore, a deformation analysis procedure is designed, implemented and tested in such a way that the procedure works on bathymetric data and satisfies the Royal Netherlands Navy requirements. Also, it is necessary to develop a procedure that translates the results into changes of the resurvey policy, taking into account their confidence intervals. To describe the sea floor statistically, we assume the sea floor to consist of a spatial trend function (or characterization) and a residual function (or dispersion). Such a description is called a representation. The covariances between positions are expressed in a covariance function, based on the residual function. The covariance function is used by Kriging, an interpolation procedure that propagates the variances and covariances of the data points to variances of the interpolated values. This approach is used widely for spatial analyses, like the interpolation of a bathymetric data set. The method that we propose uses Kriging to produce a time series of grids of depth values and their variances. Subsequently, it uses deformation analysis, a statistical procedure based on testing theory. Our application of deformation analysis is particularly aimed at the detection of dynamics in areas with tidal sand waves, resulting in parameter estimates for the sea floor dynamics, and their uncertainty. We apply the method to sea floor representations both with and without a sand wave pattern. A test scenario is set up, consisting of a survey of an existing area in the Southern North Sea, for which dynamics are simulated. The results show that the proposed method detects different types of sea floor dynamics well, leading to satisfactory estimates of the corresponding parameters. We show results for the anchorage area Maas West near the Port of Rotterdam, the Netherlands first. The area is divided into 18 subareas. The results show that a sand wave pattern is detected for most of the subareas, and a shore ward migration is detected for a majority of them. The estimated migration rates of the sand waves are up to 7.5 m/yr, with a 95% confidence interval that depends on the regularity of the pattern. The results are in confirmation with previously observed migration rates for the Southern North Sea, and with an idealized process-based model. Thereafter, we analyze several other areas for which a time series of surveys is available in the bathymetric archives of the Netherlands Hydrographic Service, to study the spatial variations in sea floor dynamics. We present results for several sand wave areas and a single flat area. In some of those areas, dredging takes place, to guarantee minimum depths. The results indicate sand wave migration in areas close to the coast, and bed level changes of the order of decimeters. The dominant wavelength of the sand waves varies. We compare our results to literature of the same sand wave areas, in which we find similar migration rates, and different wavelengths. By formulating four indicators, recommendations are made for the resurvey policy on the Belgian and Netherlands Continental Shelf. These indicators follow from the estimates for sea floor dynamics. We present a concept for the shallowest likely depth surface, on which we base two of the indicators. The other two indicators act as a warning: they quantify the potentially missed dynamics, which makes the procedure more robust in case of complicated morphology. We show clear differences in recommended resurvey frequency between the five analyzed regions. We conclude that the designed method is able to use a time series of bathymetric surveys for the estimation of sea floor dynamics in a satisfactory way. Those dynamics may be present on the scale of the sea floor, it may be a local effect, or it may be due to a tidal sand wave pattern. Also, the results are successfully reduced to a set of four indicators, used to improve a resurvey policy. Based on these conclusions, we formulate recommendations on the extrapolation of the results in space and time, on potential adaptations to the designed procedure, and on implementation of the procedure.