Coastal wetlands represent particularly valuable natural resources, characterized by the interaction between their geomorphological and biological components. Their adaptation to the changing conditions depends on the rate and extent of spatial and temporal processes and their response is still not fully understood. This work aims at detecting and improving the understanding of the transition dynamics on eco-geomorphological structures in a coastal wetland ecosystem. The approach could support sustainable habitat management improving the detection and optimizing the offer of Earth Observation (EO) products for coastal system monitoring. Such course of action will strengthen evidence-based policy making, surface biophysical data sovereignty and the Space Data downstream sector through remote sensing techniques thanks to the capability of investigating larger scale and short-to-long-term dynamics. The selected case study is the Lido basin (Venice Lagoon, Italy). Our methodology offers a support in the framework of nature-based solutions, allowing the identification of ecosystem-level indicators of the surface biophysical properties influencing stability and evolution of intertidal flats on which a conceptual model is implemented. Landsat satellite imagery is used to delineate the spatial and temporal variability of the main vegetation and sediment typologies in 1990–2011. Within this period, specific anthropic activities were carried out for morphological restoration and flood protection interventions. Specifically, the lower saltmarsh shows its more fragmented part in the Baccan islet, a residual sandy spit in front of the Lido inlet. The area covered by Sarcocornia-Limonium, that triggers sediment deposition, has fluctuated yearly, from a minimum coverage of 13% to a maximum of 50%. The second decade (2001–2009) is identified as the period with major changes of halophytic and Algae-Biofilm cover typologies distribution. The power law and related thresholds, representing the patch size frequency distribution, is an indicator of the ecosystem state transition dynamics. The approach, based on multi-temporal and spatial EO analysis, is scalable elsewhere, from regional to local-to-global scale, considering the variability of climate data and anthropogenic activities. The present research also supports sustainable habitat management, improving the detection, and optimizing the offer of EO products for coastal system monitoring. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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