Time-series of plant area density (PAD) collected in fragmented Amazonian forests

Time-series of plant area density (PAD) collected in fragmented Amazonian forests

Description

These data consist of a time-series of Plant Area Density (PAD) extracted from Terrestrial Laser Scanning (TLS) point clouds. The measurements were made in 11 different dates, between 26th April and 16th October 2019, using a RIEGL VZ-400i. The data were collected in three transects of Central Amazonian forests, located at the Biological Dynamics of Forest Fragments (BDFF) Project. The location of the transects is provided in shapefiles located in the zipped folder "Transects_location_shapefiles.zip". The TLS point clouds were processed using AMAPVox software (Vincent et al., 2021), where PAD values were estimated in voxels of 1m^3. Data structure: x = Horizontal x coordinate of the voxel in meters based on a local coordinate x = 0 y = Horizontal y coordinate of the voxel in meters based on a local coordinate y = 0 z = Vertical z coordinate of the voxel in metersbased on a local coordinate z = 0 gdist = Distance from the ground in meters. Differs from z because the terrain elevation is not flat PAD = Plant Area Density derived from TLS measurements (m2 m-3) Repeat = Measurement i of PAD during the repeat TLS campaign i, where i varies between 1 and 11 in the transects "South" and "North" and between 1 and 10 in the transect "Center". Date = Date of the TLS measurement Dist_edge = Distance from the closest forest fragment margin in meters Transect = Name of the transect. It contains "North", "South" and "Center" References: VINCENT, Gregoire; PIMONT, François; VERLEY, Philippe, 2021, "A note on PAD/LAD estimators implemented in AMAPVox 1.7", https://doi.org/10.23708/1AJNMP, DataSuds, V1
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Year of publication

2021

Authors

Matheus Nunes - Publisher, Rights holder, Creator

Eduardo Maeda - Rights holder, Creator

Other information

Fields of science

Geosciences; Environmental sciences; Ecology, evolutionary biology

Open access

Embargo

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

remote sensing, Amazon, Phenology, Terrestrial LiDAR
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