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.

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.