In gebieden met een slappe veen- en/of kleigrond, die op grote schaal voorkomen in West- en Noord-Nederland, leiden verschillende bodemprocessen tot seizoensgebonden verticale bodembeweging en lange-termijn bodemdaling. Het bepalen hiervan is belangrijk om (1) in kaart te brengen waar, wanneer en in welke mate bodemdaling optreedt en daarmee risicogebieden te kunnen identificeren, (2) het proces en onderliggende sturende factoren beter te begrijpen, (3) deze kennis te gebruiken om effectieve maatregelen te ontwikkelen en toe te passen om bodemdaling en de negatieve effecten ervan te verminderen, en (4) bodemdaling beter te kunnen voorspellen. In deze Deltafact worden meetmethoden om ondiep veroorzaakte bodembeweging en -daling te bepalen toegelicht.

Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for the conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by the machine learning algorithms which can create categorical predictions about the relative ambiguity level based on the readily available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum gradient phase unwrapping techniques.