As climate-change-driven extremes potentially make coastal areas more vulnerable, mangroves can help sustainably protect the coasts. There is a substantial understanding of both mangrove dynamics and hydro-morphodynamic processes. However, the knowledge of complex eco-geomorphic interactions with physical-environmental stressors remains lacking. We introduce a novel coupled modelling approach consisting of an individual-based mangrove (mesoFON) and a process-based hydromorphodynamic model (Delft3D-FM). This coupled model is unique because it resolves spatiotemporal processes, including tidal, seasonal, and decadal environmental changes (water level, flow, sediment availability, and salinity) with full life-stages (propagule, seedling, sapling, mature) mangrove interaction. It allows us to mechanistically simulate forest expansion, retreat, and colonisation influenced by and with feedback on physical-environmental drivers. The model is applied in a schematized mixed fluvial-tidal deltaic mangrove forest in dominantly muddy sediment inspired by the prograding delta of Porong, Indonesia. Model results successfully reproduce observed mangrove extent development, age-height relationship, and morphodynamic delta features.

This article presents a novel approach to explore mangrove dynamics on a prograding delta by integrating unmanned aerial vehicle (UAV) and satellite imagery. The Porong Delta in Indonesia has a unique geographical setting with rapid delta development and expansion of the mangrove belt. This is due to an unprecedented mud load from the LUSI mud volcanic eruption. The mangrove dynamics analysis combines UAV-based Structure from Motion (SfM) photogrammetry and 11 years (2009–2019) satellite imagery cloud computing analysis by Google Earth Engine (GEE). Our analysis shows unique, high-spatiotemporal-resolution mangrove extent maps. The SfM pho-togrammetry analysis leads to a 3D representation of the mangrove canopy and an estimate of mangrove biophysical properties with accurate height and individual position of the mangroves stand. GEE derived vegetation indices resulted in high (three-monthly) resolution mangrove coverage dynamics over 11 years (2009–2019), yielding a value of more than 98% for the overall, producer and consumer accuracy. Combining the satellite-derived age maps and the UAV-derived spatial tree structure allowed us to monitor the mangrove dynamics on a rapidly prograding delta along with its structural attributes. This analysis is of essential value to ecologists, coastal managers, and poli-cymakers.