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 photogrammetry 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 policymakers.

Mangrove forests’ restoration has gained traction as a sustainable solution to mitigate the effects of greenhouse gas emissions and to provide ecosystem services, such as coastal protection. Restoration projects are often informed by expert judgment rather than a quantitative understanding and have a high failure rate. Monitoring mangrove restoration performance may take decades and has a strong case study dependency. To optimise restoration strategies, we developed an individual-based mangrove and process-based hydro-morphodynamic model to simulate multi-species mangrove forest trajectories, including the physical environment’s feedback. We find a significant impact of planting zonation on the mudflat behaviour, with seaward erosion and in-forest-landward deposition. Planting mangroves close to mean sea level decreases carbon storage potential due to increased mudflat erosion. Configuring planting in multiple patches proves beneficial to mangrove biomass development, expansion, and sediment accumulation. Combined with sound monitoring, the developed tool can potentially optimize planned mangrove restoration strategies.

To date, the potential development of 3D laser scanning has enabled the capture of high-quality and high-precision reality-based datasets for both research and industry. In particular, Terrestrial Laser Scanning (TLS) technology has played a key role in the documentation of cultural heritage. In the existing literature, the geometric properties of point clouds are still the main focus for 3D reconstruction, while the surface performance of the dataset is of less interest due to the partial and limited analysis performed by certain disciplines. As a consequence, geometric defects on surface datasets are often identified when visible through physical inspection. In response to that, this study presents an integrated approach for investigating the materials behavior of heritage building surfaces by making use of attribute point cloud information (i.e., XYZ, RGB, reflection intensity). To do so, fracture surface analysis and material properties are computed to identify vulnerable structures on the existing dataset. This is essential for architects or conservators so that they can assess and prepare preventive measures to minimize microclimatic impacts on the buildings.

Mangrove ecosystems are widely recognized for having highly valued multiple ecosystem services. These services, however, are often overlooked because of the lack of understanding of mangrove's species-specific and associated eco-geomorphological dynamics. Therefore, it will lead to a limited quantification and valuation of mangrove's functional and structural attributes. A mangrove ecosystem model capable of mechanistically simulating the feedback loop between mangrove stands and physical–environmental drivers is essentially important, specifically in the strategy of integrating mangroves as nature-based solutions for climate change adaptation and mitigation. The main objectives of this chapter are to gain a better insight into complex mangrove ecosystem and eco-geomorphic interactions to predict their trajectories, the possibility of modelling those utilizing a process-based model, and explore the interactions of mangrove, mudflat, and physical–environmental drivers. Following that, this chapter introduces a new hybrid model, so-called DFMFON, achieved by coupling mangrove individual-based and landscape-scale hydro-morphodynamic models which is capable of reproducing mangrove forest dynamics and morphodynamic delta features. To conclude, the application, limitations, and future development of process-based mangrove modelling for nature-based solutions are discussed.

The Jeneberang Watershed is one of the river basins in South Sulawesi Province that is highly vulnerable to flood disasters due to land use change and extreme rainfall. This study aims to analyze the rainfall-runoff transformation in the upper Jeneberang Watershed using the HEC-HMS hydrological model through calibration and validation processes. The data used include daily rainfall data from five rainfall stations and daily inflow discharge data from the Bili-Bili Dam for the period 2017–2022. The HEC-HMS model was constructed using the SCS Curve Number method for water loss estimation, the SCS Unit Hydrograph for transformation, the Recession Method for baseflow, and the Lag Method for flow routing. Calibration was conducted for four flood events (2017, 2018, 2020, 2021) and validation for two independent events (2019, 2022). Model performance was evaluated using NSE, R², PBIAS, and RSR parameters. Calibration results showed an average NSE=0.76, R²=0.84, PBIAS=−10.50%, and RSR=0.48, classified as Satisfactory to Very Good, while validation yielded NSE=0.63, R²=0.73, PBIAS=−2.48%, and RSR=0.60, with overall performance classified as Satisfactory to Good and PBIAS specifically classified as Very Good. The consistency between calibration and validation indicates the model possesses adequate generalization capability and is declared suitable and reliable for flood discharge simulation in the upper Jeneberang Watershed.