DOI: 10.25573/data.24782016.v1
title: Barro Colorado Island 50-ha plot aerial photogrammetry orthomosaics and digital surface models for 2018-2023: Globally and locally aligned time series.

description: <b>Data Citation</b>Please cite this dataset as follows:Vásquez, Vicente, Milton García, Melvin Hernández, and Helene C. Muller-Landau. 2023. Barro Colorado Island 50-ha plot aerial photogrammetry orthomosaics and digital surface models for 2018-2023: Globally and locally aligned time series. Smithsonian Tropical Research Institute. Smithsonian Figshare. https://doi.org/10.25573/data.24782016This data is licensed as CC BY 4.0 and is thus freely available for reuse with proper citation. We ask that data users share any resulting publications, preprints, associated analysis code, and derived data products with us by emailing mullerh@si.edu. We are open to contributing our expert knowledge of the study site and datasets to projects that use these data; please direct queries regarding potential collaboration to Vicente Vasquez, vasquezv@si.edu, and Helene Muller-Landau, mullerh@si.edu.Note that this dataset is part of a collection of Panama UAV data on Smithsonian Figshare, which can be viewed at https://smithsonian.figshare.com/projects/Panama_Forest_Landscapes_UAV/115572Additional information about this research can be found at the Muller-Landau lab web site at https://hmullerlandau.com/All code is available at https://github.com/P-polycephalum/ForestLandscapes/tree/main/LandscapeScripts and can be cited as:Vicente Vasquez. (2023). P-polycephalum/ForestLandscapes: segmentwise (v0.0.2-beta). Zenodo. https://doi.org/10.5281/zenodo.10380517<b>Data Description</b>This 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 90 flight dates between April 4, 2018, and October 24, 2023, 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 data products included here are 4-band orthomosaics rasters, one per flight date, with standard RGB as the first three bands and the digital surface model as the fourth band.The original drone imagery was first processed independently for each date using Agisoft Metashape Pro 2.0 Python API (Agisoft LLC), employing a standardized workflow (Vasquez 2023). Key parameters for this processing included highest setting for photo alignment, medium setting for point cloud construction, and aggressive point filtering; for additional details, see Vasquez (2023). This processing resulted in initial point clouds, orthomosaics, and digital surface models. For each flight date, the orthomosaics and digital surface model were combined into a single raster for subsequent alignment.These were then 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 distortions 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 alignment time series is free of such distortions, but because of accumulating errors in 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 4<sup>th</sup> 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 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. We first computed a grid of co-registration point using arosics.COREG_LOCAL function and used the highest reliability point position to globally align to the closest date orthomosaics. 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. The process of computing co-registration grids led to the emergence of a subproduct - the locally aligned timeseries - showcasing remarkable alignment precision at the level of individual tree crowns. 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.<b>Metadata</b>:All aligned orthomosaics have the following raster metadata:• N-S extension: 12696 pixels• E-W extension: 23424 pixels• Bands: 4• Data type: Unsigned integer 8 bit• Cell size x: 0.04506838077213615 m• Cell size y: 0.04506838077213615 m• Format: GeoTIFF• Coordinate reference system: UTM 17 N, EPSG:32617• No data value: None• Bottom left corner coordinate: 625753.8483345169, 1012295.5974669948Drone: DJI Phantom 4 ProDrone data collection: Milton Garcia, Melvin Hernandez, and David DeFilippisSensor: FC6310Sensor resolution: 5472 x 3648"<b>File naming scheme</b>We provide with a zip file for each of the timeseries following the file naming convention: MacroSite_plot_timeseries_type_aligment (BCI_50ha_timeseries_global_alignment) Inside each timeseries we can find the raster files following the convention: Macrosite_plot_year_month_day_typeAligment<b>Author contributions</b>VV wrote the code for standardized processing and alignment and processed the drone imagery. MG and MH led the drone imagery collection. HCM conceived the study, wrote the grant proposals to obtain funding, and supervised the research.<b>Acknowledgments</b>Milton Solano assisted with drone data management. Pablo Ramos, Paulino Villareal, David DeFillipps, Luisa Gomez-Correa, Conny Hernandez, as well as additional interns in the Muller-Landau lab assisted with drone data collection. Funding and/or in-kind support was provided by the Smithsonian Institution Scholarly Studies grant program (HCM), the Smithsonian Institution Equipment fund (HCM), Smithsonian ForestGEO, the Smithsonian Tropical Research Institute, and Stephanie Bohlman’s lab at the University of Florida.<b>References</b>Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Biogeosciences, 18: 6517-6531. https://doi.org/10.5194/bg-18-6517-2021Garcia, M., J. P. Dandois, R. F. Araujo, S. Grubinger, and H. C. Muller-Landau. 2021a. Color orthomosaics of the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. In Smithsonian Figshare, edited by S. T. R. Institute. https://doi.org/10.25573/data.16869259.v2Garcia, M., J. P. Dandois, R. F. Araujo, S. Grubinger, and H. C. Muller-Landau. 2021b. Surface elevation models and associated canopy height change models for the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. . In Smithsonian Figshare, edited by S. T. R. Institute. https://doi.org/10.25573/data.14417933Agisoft LLC. (n.d.). Metashape Python Reference Version (release 2.0.4). <i>agisoft.com</i>. Agisoft LLC. https://www.agisoft.com/pdf/metashape_python_api_2_0_4.pdf.Vicente Vasquez. (2023). P-polycephalum/ForestLandscapes: segmentwise (v0.0.2-beta). Zenodo. https://doi.org/10.5281/zenodo.10380517Scheffler D, Hollstein A, Diedrich H, Segl K, Hostert P. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sensing. 2017; 9(7):676.