It is still a challenge to understand the mangrove dynamics and their response to the environmental forces. The assessment of the mangrove dynamics can be obtained by observing the development of its biophysical properties. This observation will provide insights into the processes at the plot level and landscape level. In this research, the assessment has been conducted by integrating Unmanned Aerial Vehicle (UAV) photogrammetry with the Structure from Motion (SfM) method and multiple satellite imagery sources. The objectives are to retrieve the mangrove biophysical properties based on two periods of UAV observation (2019 and 2021) and estimate the extent and the age-height relationship of the mangrove forests for twelve years. The analysis resulted in an accurate individual tree structure and mangrove age distribution. We integrated UAV-based very high-resolution 3D point clouds and the classified mangrove extent based on the combinations of satellite imagery from four satellites (Landsat 7-8 and Sentinel 1-2). The point clouds were processed by noise removal, ground classification, height normalisation, and generating the Canopy Height Model (CHM) to detect the individual tree height and location. Google Earth Engine has been used to perform the mangrove classification by way of four vegetation indices, i.e., Normalised Difference Index, Normalised Difference Moisture Index, Enhanced Vegetation Index, and Soil-Adjusted Vegetation Index. The off-the-shelve UAV-based surface model had a total error of 0.06m compared to the ground control points, and the root mean square error of the individual tree was 0.23m. GEE’s mangrove classification resulted in the three-monthly mangrove extent map —an advantage over the commonly annual mangrove extent map. The UAV-derived height information and satellite-based mangrove age class were integrated to retrieve the relationship of mangrove height dependent on the stand age. We observed the seasonal pattern of mangrove expansion. The mangroves area receded during the transition from dry to wet season and regrow during the wet to dry season. The general trend is the expansion of the mangroves with the high-low seasonal signal that is likely related to the mangrove’s response to sediment deposition and freshwater.

Determination of biophysical properties of mangroves, e.g. tree height and diameter at breast height, is necessary for assessing mangrove ecosystem dynamics and growth. However, traditional surveying methods (e.g. diameter at breast height, location with GPS, and tree’s height with hypsometer) are time consuming and expensive. In this research we aim to assess mangrove properties by Unmanned Aerial Vehicles (or: UAV/drone) based photogrammetry. The additional benefit of this methodology is that mangrove environments which are often difficult to access can be reached. We focus on Porong Delta, Indonesia. In this area rapid delta progradation has taken place since 2006 due to an extreme mud volcano eruption. The regional climate conditions and added nutrient flux to the new delta have provided a suitable environment for growth of different mangrove species, such as Avicennia spp., Rhizophora spp., and Sonneratia spp. We used an off-the-shelve drone to generate a high-resolution spatial canopy height model (CHM) for the mangroves by using structure-from-motion (SfM) photogrammetry with an achieved DEM resolution of 5.2cm/pixel. This approach is used to determine the structural characteristics of mangrove stands and is validated with ground-truthing on two deltas. The dense point cloud derived from SfM photogrammetry is processed in LAStools and bare earth extraction with a Cloth Simulation Filter method. The CHM is generated from the processed point clouds. The structural information of mangroves is retrieved by using the lidR package in R. Our analysis lead to a median tree height on north and south delta of 4.2m and 3.5m, respectively. Analysis of photogrammetry data shows that drone-based observations of the mangrove canopy height are a useful tool to provide trustworthy data of mangroves characteristics.