DOI: 10.60635/c3kw2x
title: Barro Colorado Island 50-ha Plot Aerial Photogrammetry (2018-2024): Orthomosaics, Digital Surface Models, Point Clouds, Raw Images, and Globally/Locally Aligned Timeseries

description: Data are available for download at: https://smithsonian.dataone.org/datasets/BCI_50ha_drone_productsMetadata of the products comes as comma separated value file named metadata_all.csv , variables_description.csv and a README.txtThis dataset is part of a larger initiative monitoring forests in Panama using drones (unoccupied aerial vehicles), an initiative led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute. As part of this initiative, we have been collecting repeat imagery of the 50-ha forest dynamics plot on Barro Colorado Island (BCI), Panama, since October 2014 (see Garcia et al. 2021a, b for data products for 2014-2019).The current dataset encompasses a total of 121 flight dates between February 2, 2018, and March 18, 2024, all conducted using a DJI Phantom 4 Pro drone and a FC6310 camera. The flight interval was near monthly through January 2023, and near weekly thereafter.The drone operated at a flight altitude of 180 meters above the take-off point. The imagery was captured with a front and side overlap of 79%. The drone maintained a speed of 5.5 meters per second, with the camera set to a shutter interval of 7 seconds.The data products available within this dataset include orthomosaics, digital surface models (DSMs), point clouds, raw images, and detailed processing reports. All these products are provided in the UTM Zone 17N coordinate reference system (EPSG: 32617).Orthomosaics and DSMs are available in GeoTIFF format, while point clouds are provided as LAS files in versions 1.2 and 1.4. The original drone imagery was independently processed for each date using the Agisoft Metashape Pro 2.0 Python API (Agisoft LLC) following a standardized workflow. Key processing parameters included the highest setting for photo alignment, a medium setting for point cloud construction, and aggressive point filtering. For more details, you can refer to the functions in the script available at this Github link: https://github.com/VasquezVicente/ForestLandscapes/blob/main/LandscapeScripts/UAV_photogrametry.py. This workflow produced the initial point clouds,orthomosaics, and digital surface models, which are provided as-is.Globally and locally aligned timeseries is a selected group of 106 orthomosaics with the DSM in the fourth band. These were horizontally and vertically aligned in two alternative ways, producing two time series: one based on local alignment, and one based on global alignment.(No fixed ground control points were available: the focal area is entirely forested and in a protected area, with no possibility of establishing permanent clearings.) Depending on the application, the globally or locally aligned products may be preferred. The local alignment algorithms at times produced warping which have the potential to remove some of the signals of interest (e.g., obscuring tree crown growth or damage, as the edges of crowns are aligned across dates). The global aligned time series is free of such warping but due to photo alignment and matching during photogrammetry processing, it can display noticeable systematic alignment errors towards the edges of the plot (the center tends to be well-aligned). The workflow performs several tasks such as combining digital surface models into the 4th band and cropping to the area of interest. Horizontal alignment was based on the arosics.CoReg module (Scheffler, 2017) applied to the 4-band raster (including RGB and the digital surface model). This was followed by global vertical alignment, implemented simply by subtracting the median elevation difference. We took advantage of airborne LiDAR data(doi:10.60635/C34W2W) and associated RGB photogrammetry collected on May 26, 2023, as a basis for the alignment. The drone-acquired raster for the closest date (May 23, 2023) served as the main reference. Subsequently, an iterative procedure was implemented in which each next closest date was aligned to the preceding one, creating a chain of alignments. This process continued backward and forward in time, creating a series of aligned datasets in which each date served as the reference for the next and previous dates. We provide with the grids used for the local alignment in the form of comma separated value files, it contains the x and y position in the form of spatial points. Careful attention was maintained throughout these processes to uphold the integrity and quality of the data. For a comprehensive understanding of the alignment methodology, please refer to the GitHub repository housing the complete code: https://github.com/VasquezVicente/ForestLandscapes/blob/main/LandscapeScripts/50ha_aligment_v2.py