RGB (red blue green) averaged orthomosaic was created in Agisoft Metashape v. 2.0.1 using the following general steps (see Over and others, 2021):
1. The project was created and imagery (located in larger work citation 2023022FA_NLB_preHxLee_imagery.zip and 2023022FA_NLB_postHxLee_imagery.zip) were imported using the NAD83(2011)/UTM19N and NAVD88 datums.
2. Photos were aligned at a low accuracy so that GCPs could be automatically detected in the point cloud. Eight GCPs (2023022FA_NLB_preHxLee_AeroPoints.csv and 2023022FA_NLB_postHxLee_AeroPoints.csv located in the larger work citation) were found in the images and then added to the project in the reference systems NAD83(2011)/UTM Zone 19N and NAVD88. Accuracies for the GCPs were set 0.02 m. The photos were re-aligned with high accuracy (the pixels are not subsampled for increased processing speed) using a keypoint limit of 40,000 and unlimited tie points.
3. Then, Metashape software refined and optimized, using least squares, the camera positions and lens model using gradual selection and optimization parameters of: Ru = 20, Pa = 5, and Re = 0.3 to minimize the reprojection error or the distance between the measured points and the software created points.
4. The pre and post Hurricane Lee point clouds were separated into individual Metashape chunks. A dense point cloud was generated using the high-quality setting (images were not subsampled) and a low frequency filtering algorithm for the pre and post images. Each dense point cloud was then hand edited to remove noise before generating digital elevation models (the software calls all models elevation models, but a surface model includes vegetation/canopy returns, a terrain model would be only bare earth returns).
5. The digital surface model was used to build an RGB averaged orthomosaic, which was then exported from the software at a resolution of 5 cm and in NAD83(2011)/UTM Zone 19N (ESPG:6348).
6. The orthoss were then turned into cloud-optimized GeoTIFFs (COGs) using gdal_translate with the following command: for %i in (.\*.tif) do gdal_translate %i .\%~ni_cog.tif -of COG -stats -co BLOCKSIZE=256 -co COMPRESS=DEFLATE -co PREDICTOR=YES -co NUM_THREADS=ALL_CPUS -co BIGTIFF=YES (v. 3.1.4 accessed October 20, 2020
https://gdal.org/), where i is the name of each GeoTIFF section.
