326
the standard procedure for waveform decomposition—additional echo attributes,
such as amplitude and intensity of the return signal, can be provided, which can
support the classification process. As a result, full-waveform data provide a much
more detailed characterization of the vertical vegetation structure. In this way,
important indicators for vegetation structure and biodiversity, e.g., vegetation height
and cover of the different vegetation layers, can be estimated with a lower bias and
higher consistency (Reitberger et al. 2008).
Nowadays, laser-based instruments are mounted on all kinds of RS platforms,
including stationary or mobile scanners and terrestrial-, drone-, and aircraft-based
platforms (e.g., the well-established airborne LiDAR scanning). To record a variety
of structural parameters, it is possible to combine information from LiDAR sensors
with optical, thermal, or radar RS sensors (Joshi et al. 2015, 2016). Li et al. (2014)
provide an overview of 3-D imaging techniques for describing plant phenotyping of
vegetation. Rosell and Sanz (2012) review methods and applications of 3-D imaging techniques for the geometric characterization of tree crops in agricultural systems. Wulder et al. (2012) provide a review of LiDAR sampling for characterizing
landscapes.
The LiDAR systems used for ecological applications generally have a beam footprint of less than 1 m diameter on the ground. These so-called small-footprint systems are preferred because they provide a good link between the LiDAR beam and
the structural vegetation attributes that could subtly change as a consequence of
stress or damage, sometimes within individual trees. By comparison, large-footprint
systems have beam diameters of up to scores of meters on the ground; e.g., the
Geoscience Laser Altimeter System (GLAS) instrument mounted on the Ice, Cloud,
and land Elevation Satellite (ICESat) platform has a footprint of 38 m (Schutz et al.
2005). Such systems can be used to model and map broad vegetation structural
attributes and are well suited for detecting structural vegetation characteristics
across large areas.
The most important environmental application of LiDAR is the precise mapping
of terrain and surface elevations. Such digital terrain models (DTMs) or digital surface models (DSMs) can be useful in determining topographic information important for plant growth and monitoring of vegetation structure and biodiversity, e.g.,
changes in vegetation height or density resulting from succession or natural disturbance (Heurich 2008). Many filtering methods have been developed to extract terrain elevation from point clouds, which produces DTMs with high spatial resolution
and root mean square errors (RMSEs) of 0.15–0.35 m (Andersen et al. 2005;
Heurich 2008; Sithole and Vosselman 2004). No other RS technique has the ability
to deliver DTMs of similar quality within dense vegetation. Recent studies show
that it is even possible for LiDAR to detect objects located on the ground surface.
Coarse woody debris, as an example, is an important indicator of past disturbances
that might influence biodiversity because it provides habitat to a multitude of plant
and animal species and plays an important role in the forest carbon cycle.
Because of its characteristics, LiDAR is well suited for measuring biophysical
parameters of vegetation, such as tree dimensions and canopy properties. Two main
approaches have been developed over recent years. The area-based approach is a
A. Lausch et al.
the standard procedure for waveform decomposition—additional echo attributes,
such as amplitude and intensity of the return signal, can be provided, which can
support the classification process. As a result, full-waveform data provide a much
more detailed characterization of the vertical vegetation structure. In this way,
important indicators for vegetation structure and biodiversity, e.g., vegetation height
and cover of the different vegetation layers, can be estimated with a lower bias and
higher consistency (Reitberger et al. 2008).
Nowadays, laser-based instruments are mounted on all kinds of RS platforms,
including stationary or mobile scanners and terrestrial-, drone-, and aircraft-based
platforms (e.g., the well-established airborne LiDAR scanning). To record a variety
of structural parameters, it is possible to combine information from LiDAR sensors
with optical, thermal, or radar RS sensors (Joshi et al. 2015, 2016). Li et al. (2014)
provide an overview of 3-D imaging techniques for describing plant phenotyping of
vegetation. Rosell and Sanz (2012) review methods and applications of 3-D imaging techniques for the geometric characterization of tree crops in agricultural systems. Wulder et al. (2012) provide a review of LiDAR sampling for characterizing
landscapes.
The LiDAR systems used for ecological applications generally have a beam footprint of less than 1 m diameter on the ground. These so-called small-footprint systems are preferred because they provide a good link between the LiDAR beam and
the structural vegetation attributes that could subtly change as a consequence of
stress or damage, sometimes within individual trees. By comparison, large-footprint
systems have beam diameters of up to scores of meters on the ground; e.g., the
Geoscience Laser Altimeter System (GLAS) instrument mounted on the Ice, Cloud,
and land Elevation Satellite (ICESat) platform has a footprint of 38 m (Schutz et al.
2005). Such systems can be used to model and map broad vegetation structural
attributes and are well suited for detecting structural vegetation characteristics
across large areas.
The most important environmental application of LiDAR is the precise mapping
of terrain and surface elevations. Such digital terrain models (DTMs) or digital surface models (DSMs) can be useful in determining topographic information important for plant growth and monitoring of vegetation structure and biodiversity, e.g.,
changes in vegetation height or density resulting from succession or natural disturbance (Heurich 2008). Many filtering methods have been developed to extract terrain elevation from point clouds, which produces DTMs with high spatial resolution
and root mean square errors (RMSEs) of 0.15–0.35 m (Andersen et al. 2005;
Heurich 2008; Sithole and Vosselman 2004). No other RS technique has the ability
to deliver DTMs of similar quality within dense vegetation. Recent studies show
that it is even possible for LiDAR to detect objects located on the ground surface.
Coarse woody debris, as an example, is an important indicator of past disturbances
that might influence biodiversity because it provides habitat to a multitude of plant
and animal species and plays an important role in the forest carbon cycle.
Because of its characteristics, LiDAR is well suited for measuring biophysical
parameters of vegetation, such as tree dimensions and canopy properties. Two main
approaches have been developed over recent years. The area-based approach is a
A. Lausch et al.