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Plant functional types are often used in Earth system models or dynamic global
vegetation models to simplify the surface representation of vegetation into functional classes. Plant functional types defined for models are usually physiognomic
vegetation types, defined by their structure, shape, stature, and leaf shape/habit,
e.g., graminoids, forbs, shrubs, broadleaf/conifer, and evergreen/deciduous.
Plant litter Dead plant material—leaves, stems, bark, etc.—on the ground that
can form the surface layer of the soil, sometimes called the “duff” layer.
Platform in remote sensing, a structure or system for mounting or supporting
a measuring instrument. Examples include unmanned aerial vehicle (UAV) or
system (UAS) (also called a drone), airplane, satellite, or tower.
Point cloud A set (cloud) of points representing the three-dimensional structure
of an object or set of objects (e.g., tree or forest canopy), typically derived from
LiDAR or structure from motion.
Polarization parameters used in radar remote sensing to represent polarizations
(e.g., horizontal, vertical, or other configurations) of the microwave energy emitted from the antenna and the signal backscattered from the target.
Polarimetric in radar remote sensing, horizontal and vertical phase information
from both transmit and receive data is recorded, usually reported as HH, HV,
VH, and VV.
Pollination The process that brings pollen, which produces the male gametes
(sperm), into contact with the female organs of a plant. This process often results
in the fertilization of an ovule by the sperm and the development of a seed.
Polyphenolics A large category of secondary metabolites found in plant tissues
and soil that contain many phenol (C 6 H 5 OH) groups.
Polytomy When more than two branches descend directly from the same ancestral node. It could mean that one species instantly became three or more (this is
sometimes called a hard polytomy) – more commonly, it represents uncertainty
in not knowing the relative order of branching of the descendants (called a soft
polytomy).
Post-processing in remote sensing, further modification of data beyond initial
processing steps to a standard output, often with a particular objective in mind,
e.g., filtering or smoothing.
Precision Refinement in a measurement, calculation, or specification, represented
by the number of significant digits used. Also, a measure of the measurement
repeatability, related to the variability in values or predictions resulting from
applying models or measurements multiple times, with low variability corresponding to high precision, or high reproducibility; not to be confused with accuracy, which is a measure of measurement error or measurement, or model bias,
with low bias corresponding to a high degree of correspondence between predictions and observations/measurements.
Principal components analysis A linear transformation method that maximizes
the variance of the data through transformation (rotation) of the data axes using
the covariance matrix. When applied to spectra, it produces a series of orthogonal components that correspond to linear combinations of the original bands
aligned to represent the main axes of variation within the original data.
Glossary
Plant functional types are often used in Earth system models or dynamic global
vegetation models to simplify the surface representation of vegetation into functional classes. Plant functional types defined for models are usually physiognomic
vegetation types, defined by their structure, shape, stature, and leaf shape/habit,
e.g., graminoids, forbs, shrubs, broadleaf/conifer, and evergreen/deciduous.
Plant litter Dead plant material—leaves, stems, bark, etc.—on the ground that
can form the surface layer of the soil, sometimes called the “duff” layer.
Platform in remote sensing, a structure or system for mounting or supporting
a measuring instrument. Examples include unmanned aerial vehicle (UAV) or
system (UAS) (also called a drone), airplane, satellite, or tower.
Point cloud A set (cloud) of points representing the three-dimensional structure
of an object or set of objects (e.g., tree or forest canopy), typically derived from
LiDAR or structure from motion.
Polarization parameters used in radar remote sensing to represent polarizations
(e.g., horizontal, vertical, or other configurations) of the microwave energy emitted from the antenna and the signal backscattered from the target.
Polarimetric in radar remote sensing, horizontal and vertical phase information
from both transmit and receive data is recorded, usually reported as HH, HV,
VH, and VV.
Pollination The process that brings pollen, which produces the male gametes
(sperm), into contact with the female organs of a plant. This process often results
in the fertilization of an ovule by the sperm and the development of a seed.
Polyphenolics A large category of secondary metabolites found in plant tissues
and soil that contain many phenol (C 6 H 5 OH) groups.
Polytomy When more than two branches descend directly from the same ancestral node. It could mean that one species instantly became three or more (this is
sometimes called a hard polytomy) – more commonly, it represents uncertainty
in not knowing the relative order of branching of the descendants (called a soft
polytomy).
Post-processing in remote sensing, further modification of data beyond initial
processing steps to a standard output, often with a particular objective in mind,
e.g., filtering or smoothing.
Precision Refinement in a measurement, calculation, or specification, represented
by the number of significant digits used. Also, a measure of the measurement
repeatability, related to the variability in values or predictions resulting from
applying models or measurements multiple times, with low variability corresponding to high precision, or high reproducibility; not to be confused with accuracy, which is a measure of measurement error or measurement, or model bias,
with low bias corresponding to a high degree of correspondence between predictions and observations/measurements.
Principal components analysis A linear transformation method that maximizes
the variance of the data through transformation (rotation) of the data axes using
the covariance matrix. When applied to spectra, it produces a series of orthogonal components that correspond to linear combinations of the original bands
aligned to represent the main axes of variation within the original data.
Glossary