## 2 Materials and Methods
### UAV Data Collection and Processing
#### 2.2.1 Data Acquisition  
Several studies demonstrated that consumer-grade UAV could achieve vertical accuracies in the order of a few centimetres to a few decimetres in coastal topographical surveying [38; 39; 40]. In comparison with other low-cost platforms (e.g., kite, pole, and fixed-wing), a consumer (rotary) drone system with its integrated positioning system, inertial measurement unit, and stabilised camera offers flexibility and efficiency while attaining the accuracy in coastal area application [39; 41; 42].  
In this study, we used consumer-grade UAV DJI Mavic Pro (DJI, Shenzen, China) during the field campaign. The drone has four propellers and a built-in true colour camera. The camera is equipped with 1/2.3\({}^{\prime\prime}\) CMOS sensor with total effective pixels of 12.35 M, which produces a 4000 \(\times\) 3000 image resolution and equipped with an electronic shutter. Information on shutter type is important since it will affect the setting on the camera calibration to compensate the rolling shutter issues as in the electronic shutter-type cameras. The overall flight time in optimal condition is 21 min with 15% remaining battery level [43].  
The DroneDeploy web app (DroneDeploy, San Fransisco, CA, USA) was used to define the flight path [40]. It was planned with a flight altitude of 60 m, an overlay of 80% front overlap and 75% side overlap. Each flight was designed to cover 0.02 km\({}^{2}\) area with 15 min flight time to limit one battery per flight. An enhanced 3D mode was activated to improve 3D structures quality which will capture an oblique image of the objects, facing toward the inner centre of the target by carefully not to include horizon in the shots [44]. We added a buffer zone approximately 20 m apart from the edge of the low tide limit that was cropped during the processing later on to prevent interference with the SfM Photogrammetry processing [15]. With these settings, we were able to build 15 grids covering around 0.3 km\({}^{2}\). All the grids created in the web app then can be synced into the mobile app to manage the flight and photo acquisition on-field automatically. The flights were conducted between 09:00 and 12:00 to get optimum natural sunlight, primarily limit the appearance of shadows on the photos. Additionally, this short time window was chosen to avoid high variation in sun intensity [45] and it covered the low-tide period. Before the flight, we placed the custom made printed red cross-shape tarpaulin as a ground control point (GCP). The GCP size is 1 m \(\times\) 1 m rectangle in the inner side and 1 m diagonal cross in the outer side to make it identifiable from the 60 m altitude. We placed GCPs over the mudflat and in the middle of the delta, which has low vegetation density and registered them with DGPS in RTK mode.  
In total, the properties and location of 69 mangrove trees were measured concurrently with the mudflat topography. We recorded diameter at breast height (dbh), height, and location for each tree. The dbh was recorded using a measuring tape at 130 cm above the ground. Tree height was recorded with laser rangefinder by measuring horizontal distance and hypotenuse from surveyor location to the tree stem and treetop, respectively. For the trees that were located in the proximity of the mangrove edge we recorded the position with DGPS. Trees that were located more in the centre of the forest were recorded with GPS (horizontal accuracy \(\pm\) 3.5 m) because it is difficult to walk inside the forest with heavy equipment. Mudflat topography was measured with DGPS in RTK mode. The mangrove trees dataset was used as control points and groundtruthing for the created Digital Surface Model (DSM).