## 5 Conclusions  
The LUSI mud volcano eruption, arguably the largest mud eruption in the world, certainly affects the downstream landscape development. Particularly with the diversion operation that conveys a large amount of mudflow sediment into the river that eventually stimulates a rapid progradation of the delta compared to pre-LUSI conditions. This offers a unique opportunity to analyse mangrove dynamics on a rapidly prograding delta. We created time series of mangrove extent maps and mangrove biophysical structure with the following steps: (a) biophysical properties retrieval using UAV-based SfM photogrammetry; (b) three-monthly classification of mangrove areas using L7, L8, S1, and S2 in Google Earth Engine; and (c) derivation of mangrove age maps based on the satellite imagery. This improvement enables us to capture the highly dynamic setting in the study area. Moreover, the off-the-shelf UAV offers an efficient yet accurate technique to retrieve the important structural attributes, such as individual tree location and canopy height. When combined with satellite imagery analyses, the information can be used to characterise the mangrove forest and assess the effect of excessive mudflow discharge on the delta and mangrove development. The proposed approaches allowed us to monitor the dynamics of mangrove extent and structural attributes in a rapidly prograding delta. The random forest supervised classification demonstrated a high accuracy (OA > 99.39%, kappa value 0.99, PA > 97.83%, and CA > 98.18%). The highest accuracy was obtained in the classification from the combined Sentinel 1 and 2, while the lowest resulted from the Landsat 7. The 11 years of mangrove extent mapping provided evidence of an overall positive trend in mangrove extent overlaid by seasonal variation. The receding mangrove area is detected during the transition from dry to wet season and regrow during the wet to dry season. The individual trees height and position derived from UAV showed the different distribution of the north and south delta lobes that is likely related to the mangrove planting. The method enabled us to retrieve rapid and accurate information on mangrove biophysical properties with an off-the-shelf drone. Moreover, in combination with GEE based cloud computing, it is possible to derive a high spatiotemporal resolution mangrove extent map. The combination of UAV-derived spatial tree structure and the satellite-derived maps are needed to support fast and frequent mangrove monitoring that will be valuable for the ecologists, coastal managers, or policymakers. The following are available online at [[https://www.mdpi.com/2072-429/13/6/1084/s1](https://www.mdpi.com/2072-429/13/6/1084/s1)]([https://www.mdpi.com/2072-429/13/6/1084/s1](https://www.mdpi.com/2072-429/13/6/1084/s1)), Animation S1: Time series animation of Porong mangrove extent, Supplement S1 Mangrove trees density on the northern and southern delta lobes. Conceptualisation, S.M.B., M.v.d.W., U.G., and D.R. ; methodology, S.M.B. ; software, S.M.B. ; validation, S.M.B. ; formal analysis, S.M.B. ; data curation, S.M.B. ; writing--original draft preparation, S.M.B. ; writing--review and editing, S.M.B., M.v.d.W., D.R., U.G., J.D., and J.R.; visualisation, S.M.B. ; supervision, M.v.d.W. and D.R. ; project administration, S.M.B. ; funding acquisition, S.M.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is funded by the Indonesia Endowment Fund for Education (LPDP) with grant number 201710220111567. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** [PERSON] gratefully acknowledges LPDD within their framework of BUDI-LN for its financial support. [PERSON] would like to extend his gratitude to Agisoft LLC for their support by providing five months Agisoft Metashape Professional license for this research.