Mangroves are recognised for the ecosystem services they provide, yet practitioners lack guidance for quantifying these services over time. To overcome this knowledge gap, this study developed a numerical tool, the mangrove Lifecycle Ecosystem Analysis and Forecasting (LEAF) model, that simulates the growth and mortality of mangroves across all lifecycle stages (seedling to senescence). To test model functionality, the LEAF model (version 1.0, dated January 31, 2025) was coupled to Delft3D Flexible Mesh, where individual mangrove size, impacts of extreme events, biomass, and coastal protection parameters were monitored. Cross-shore mangrove distribution was successfully predicted in four estuary typologies over temporal domains of 5–12 years. Sensitivity analyses revealed the timing and duration of the fruiting window, inundation free period, and inundation depth as critical to forest development. Results highlight the need for field data acquisition to target these thresholds, further validate mangrove growth, and expand the model to other species and locations worldwide.

Mangroves are increasingly recognised for their ecosystem services, including their capacity to store carbon and adapt to climate pressures by stabilising shorelines and acting as storm barriers. To quantify these services, relevant parameters such as mangrove biomass and drag coefficients have been calculated using allometric equations fitted to field measurements of physical mangrove attributes. However, previous research to quantify mangrove attributes has involved time-consuming hand measurements and long processing times associated with terrestrial laser scanning (TLS). To more efficiently capture and process mangrove attributes, such as the density, diameter, height, and projected area of stems and roots, a novel method for collecting mangrove field data using TLS was developed. Recorded TLS data were compared to field measurements conducted in 12 Avicennia marina forests across 10 estuaries and 4 unique estuary typologies. The results demonstrated the reliable estimation of mangrove attributes using TLS and revealed a link between these attributes and estuarine geomorphology. Mangrove stems were accurately identified in all estuary typologies, with attribute estimations more accurate for forests in Drowned River Valleys (DRVs). A sensitivity analysis revealed that 10–20 trees for DRVs and 35–45 trees for barrier estuaries require point cloud processing to characterise a forest area of 400–1300 m² and to achieve convergent stem diameter and tree height results. The method presented herein offers an efficient way to quantify aboveground stem and root attributes and the surface area of mangrove trees. This data can be used to characterise mangrove forests worldwide and provide fundamental attributes for quantifying ecosystem services.