Data are available for download at: https://smithsonian.dataone.org/datasets/Panama_Forest_Drone_DatasetMetadata of the products comes as comma separated value file named metadata_all.csv , variables_description.csv and a README.txtThe Panama Forest Drone Dataset is a dataset part of an initiative focused on monitoring and understanding forest ecosystems in Panama through the use of drone technology. This project is led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute, with the primary goal of capturing high-resolution imagery and comprehensive data across various forest study plots located in central Panama. The dataset is a part of an ongoing effort to document and analyze changes in forest conditions, contributing valuable insights into tropical forest dynamics.This extensive dataset covers nine macrosites, including well-known areas such as the Agua Salud Project, Parque Natural Metropolitano, Bosque Protector de Arraiján, Barro Colorado Natural Monument, Parque Nacional Soberanía, Parque Nacional Camino de Cruces, Gamboa, San Lorenzo, and Santa Rita Colón. Within these macrosites, the dataset encompasses 38 individual plots, each representing distinct areas of interest. Data collection has been conducted over multiple dates, with a total of 84 drone flights contributing to the dataset, capturing critical information across these diverse landscapes.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 dataset primarily consists of RGB imagery, though flights in 2024 utilized a DJI Mavic 3 Enterprise Multispectral sensor, offering multispectral data alongside the standard RGB products.The drones used in this project include a variety of platforms, each equipped with specific sensors suited to the data collection needs. The DJI Phantom 4 Pro, denoted as P4P in the dataset, was frequently used, equipped with an FC6310 camera. Other platforms include the Solo 3DR, outfitted with a Peau Productions MAPIR sensor(SOLO) and a MAPIR Survey 2 sensor(SOLO2). The eBee SenseFly, which utilized both a S.O.D.A camera sensor and a SONY DSC-WX220 sensor, the latter referred to as EBEE2 in the dataset. The DJI Mavic 3 Enterprise (M3E), featured a M3M multispectral sensor. The Hobbyest drones doneted as DR1 carried a Canon PowerShot ELPH 520 HS.AcknoledgementsWe extend our sincere gratitude to Jonathan Dandois, Ryan Nolin, and Marino Ramirez for their exceptional work as drone pilots during the early flights. We also deeply appreciate the invaluable field support provided by Paulino Villarreal and Pablo Ramos. A special thanks goes to Milton Solano for his support as a GIS specialist.

Data are available for download at: https://smithsonian.dataone.org/datasets/Panama_Forest_Drone_DatasetMetadata of the products comes as comma separated value file named metadata_all.csv , variables_description.csv and a README.txtThe Panama Forest Drone Dataset is a dataset part of an initiative focused on monitoring and understanding forest ecosystems in Panama through the use of drone technology. This project is led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute, with the primary goal of capturing high-resolution imagery and comprehensive data across various forest study plots located in central Panama. The dataset is a part of an ongoing effort to document and analyze changes in forest conditions, contributing valuable insights into tropical forest dynamics.This extensive dataset covers nine macrosites, including well-known areas such as the Agua Salud Project, Parque Natural Metropolitano, Bosque Protector de Arraiján, Barro Colorado Natural Monument, Parque Nacional Soberanía, Parque Nacional Camino de Cruces, Gamboa, San Lorenzo, and Santa Rita Colón. Within these macrosites, the dataset encompasses 38 individual plots, each representing distinct areas of interest. Data collection has been conducted over multiple dates, with a total of 84 drone flights contributing to the dataset, capturing critical information across these diverse landscapes.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 dataset primarily consists of RGB imagery, though flights in 2024 utilized a DJI Mavic 3 Enterprise Multispectral sensor, offering multispectral data alongside the standard RGB products.The drones used in this project include a variety of platforms, each equipped with specific sensors suited to the data collection needs. The DJI Phantom 4 Pro, denoted as P4P in the dataset, was frequently used, equipped with an FC6310 camera. Other platforms include the Solo 3DR, outfitted with a Peau Productions MAPIR sensor(SOLO) and a MAPIR Survey 2 sensor(SOLO2). The eBee SenseFly, which utilized both a S.O.D.A camera sensor and a SONY DSC-WX220 sensor, the latter referred to as EBEE2 in the dataset. The DJI Mavic 3 Enterprise (M3E), featured a M3M multispectral sensor. The Hobbyest drones doneted as DR1 carried a Canon PowerShot ELPH 520 HS.AcknoledgementsWe extend our sincere gratitude to Jonathan Dandois, Ryan Nolin, and Marino Ramirez for their exceptional work as drone pilots during the early flights. We also deeply appreciate the invaluable field support provided by Paulino Villarreal and Pablo Ramos. A special thanks goes to Milton Solano for his support as a GIS specialist.

Data are available for download at: https://smithsonian.dataone.org/datasets/Panama_Forest_Drone_DatasetMetadata of the products comes as comma separated value file named metadata_all.csv , variables_description.csv and a README.txtThe Panama Forest Drone Dataset is a dataset part of an initiative focused on monitoring and understanding forest ecosystems in Panama through the use of drone technology. This project is led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute, with the primary goal of capturing high-resolution imagery and comprehensive data across various forest study plots located in central Panama. The dataset is a part of an ongoing effort to document and analyze changes in forest conditions, contributing valuable insights into tropical forest dynamics.This extensive dataset covers nine macrosites, including well-known areas such as the Agua Salud Project, Parque Natural Metropolitano, Bosque Protector de Arraiján, Barro Colorado Natural Monument, Parque Nacional Soberanía, Parque Nacional Camino de Cruces, Gamboa, San Lorenzo, and Santa Rita Colón. Within these macrosites, the dataset encompasses 38 individual plots, each representing distinct areas of interest. Data collection has been conducted over multiple dates, with a total of 84 drone flights contributing to the dataset, capturing critical information across these diverse landscapes.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 dataset primarily consists of RGB imagery, though flights in 2024 utilized a DJI Mavic 3 Enterprise Multispectral sensor, offering multispectral data alongside the standard RGB products.The drones used in this project include a variety of platforms, each equipped with specific sensors suited to the data collection needs. The DJI Phantom 4 Pro, denoted as P4P in the dataset, was frequently used, equipped with an FC6310 camera. Other platforms include the Solo 3DR, outfitted with a Peau Productions MAPIR sensor(SOLO) and a MAPIR Survey 2 sensor(SOLO2). The eBee SenseFly, which utilized both a S.O.D.A camera sensor and a SONY DSC-WX220 sensor, the latter referred to as EBEE2 in the dataset. The DJI Mavic 3 Enterprise (M3E), featured a M3M multispectral sensor. The Hobbyest drones doneted as DR1 carried a Canon PowerShot ELPH 520 HS.AcknoledgementsWe extend our sincere gratitude to Jonathan Dandois, Ryan Nolin, and Marino Ramirez for their exceptional work as drone pilots during the early flights. We also deeply appreciate the invaluable field support provided by Paulino Villarreal and Pablo Ramos. A special thanks goes to Milton Solano for his support as a GIS specialist.

Data are available for download at: https://smithsonian.dataone.org/datasets/BCI_ava_drone_productsMetadata are available in metadata_all.csv , variables_description.csv and README.txtThis dataset of repeat drone imagery of the AVA plot and surrounding areas on Barro Colorado Island (BCI), Panama is part of a research program investigating spatial and temporal variation in forest structure, dynamics, and composition of forest ecosystems in Panama using uncrewed aerial vehicles (UAVs) or drones to capture high-resolution data. This research is led by Dr. Helene C. Muller-Landau at the Smithsonian Tropical Research Institute.The current dataset encompasses flights on 80 dates between November 26, 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 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 processing produced the initial point clouds, orthomosaics, and digital surface models, which are provided as-is.Eighty flights were conducted centered on the AVA plot, a 6-ha forest area around the AVA tower, which is equipped with physical monitoring equipment including microclimate sensors, an eddy covariance system, and a phenocam. The coverage area also partially includes sections of the 25-ha, 10-ha, and 50-ha plots on BCI. The total covered area was 26 hectares, with the drone operating 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.

Data are available for download at:https://smithsonian.dataone.org/datasets/Panama_Forest_Drone_DatasetMetadata is available as comma separated value files named metadata_all.csv and variables_description.csv, as well as a README.txtThis dataset is part of a research program investigating spatial and temporal variation in forest structure, dynamics, and composition of forest ecosystems in Panama using uncrewed aerial vehicles (UAVs) or drones to capture high-resolution data. This research is led by Dr. Helene C. Muller-Landau at the Smithsonian Tropical Research Institute.This extensive dataset is organized into nine sites, hereafter referred to as macrosites. These are the Agua Salud Project, Parque Natural Metropolitano, Bosque Protector de Arraiján, Barro Colorado Natural Monument, Parque Nacional Soberanía, Parque Nacional Camino de Cruces, Gamboa (this area is also part of Parque Nacional Soberanía), San Lorenzo, and Santa Rita Colón. Within these macrosites, the dataset encompasses 38 individual plots or zones, each representing distinct areas of interest. Data collection was conducted over multiple dates, and includes a total of 84 drone flights.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 dataset is based primarily on RGB imagery, though flights in 2024 utilized a DJI Mavic 3 Enterprise Multispectral sensor, offering multispectral data alongside the standard RGB products.This dataset includes data collected by multiple different drone platforms and sensors, as specified in the metadata. The most frequently used was the DJI Phantom 4 Pro (denoted P4P in the dataset), which was equipped with a FC6310 camera. The other platforms were the Solo 3DR, outfitted with a Peau Productions MAPIR sensor (SOLO) and a MAPIR Survey 2 sensor (SOLO2); the eBee SenseFly, which utilized both a S.O.D.A camera sensor and a SONY DSC-WX220 sensor, the latter referred to as EBEE2 in the dataset; the DJI Mavic 3 Enterprise (M3E), featuring a M3M multispectral sensor; and a hobbyist drone (denoted DR1) with a Canon PowerShot ELPH 520 HS.