Multibeam echosounding is indispensable for underwater monitoring, with backscatter and bathymetry data enabling Acoustic Seafloor Classification (ASC) and Change Detection (ACD). ASC is a maturing discipline, whilst ACD has remained virtually unexplored. To further develop techniques for the spatio-temporal quantification of seafloor status and dynamics, state-of-the-art hydroacoustic and ground-truth data were acquired in the Belgian Part of the North Sea and were integrated via automated classification routines. ASC research found variable predictive performance between supervised machine learning and unsupervised clustering classification. 300 kHz backscatter discrimination potential is weaker for heterogenous substrates, constraining the spatial structure and information content of the classification scheme. ACD methodologies were developed allowing the acoustic observation of signals of change and quantified the measurement’s sensitivity to environmental cyclicity, advancing the phenomenological and acoustical understanding of the dynamic environment: sources and magnitudes that are paramount for the establishment of ACD in environmental monitoring. Multi-parameter sampling datasets need collecting to fine-tune ASC, better interpret field backscatter measurements, and improve classification schemes. Novel datatypes, classifiers and predictors need further investigation, which together with knowledge of the system and emerging technologies, ranging from robotics to ecosystem modelling, paves the way for more innovative monitoring of the marine environment.