###### Abstract  
This article presents a novel approach to explore mangrove dynamics on a programing 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 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. m 2021 10 March 2021 12 March 2  
difficult to carry out on the ground [5]. Mapping mangrove forests is one of the most challenging tasks in remote sensing since the forests are mostly quite large, located in a remote area, and persistent cloud cover in the tropical areas [2; 6; 7]. Several efforts have been made to monitor the spatial extent of mangrove forests from local to the global scales. Two global-scale mangrove extent maps were released for 2000, i.e., the World Atlas of Mangrove (WAM) [1] and the Global Mangrove Forest Distribution v1 (GMFD) [8]. Other current global products are the Continuous Global Mangrove Forest Cover for the 21 st Century (GCMFC-21) that showing annual global mangrove forest cover from 2000 to 2012 [9] and Global Mangrove Watch (GMW) v2.0 [10]. Information on important structural attributes of mangroves such as tree height, age, and diameter at breast height (dbh) is required to characterise the mangrove forest stands. Accurate information of tree height is essential, since there is a high correlation of the height and the dbh, basal area (BA), and above ground biomass (AGB) [5; 11]. Mangrove forests with their harsh physical conditions hinder surveyors from carrying out direct measurements in the field. In principle, a number of remote sensing approaches have been developed as supplement or substitute for ground-based inventory [12; 13; 14; 15]. A Canopy Height Model (CHM)--that is, a three-dimensional representation of the forest--can be derived by using airborne or terrestrial LiDAR, Synthetic Aperture Radar (SAR), and high-resolution optical imagery [16]. Global satellite datasets such as Shuttle Radar Topography Mission (SRTM) and ICESat/ GLAS [16; 17], and ICESat-2/ATLAS launched in 2018 [18] have proven to be useful in deriving canopy height, but they were released in a medium resolution of 30 m \(\times\) 30 m. TanDEM-X InSAR, a DEM product developed by the German Space Agency (2011-2015) with a ground resolution of 12.5 \(\times\) 12.5 m, has also been used to estimate canopy height [14]. Recently, Very High Resolution (VHR) CHM derived from Unmanned Aerial Vehicle (UAV)-based Structure from Motion (SfM) Photogrammetry has been deployed [15; 16] often at a local scale. The SRTM, ICESat, and TanDEM are the most commonly utilised methods to estimate CHM since they have global coverage, and the data are easily accessible [19]. However, the passive/optical sensors solely obtain surface canopy information, and radar has at least limited penetration capability [19]. LiDAR, on the other hand, is able to penetrate the canopy cover to some extent depending on the acquisition design and lidar system [20].