85
While they were better at modeling directional effects than 1-D models, they still
lacked multiple scattering and did not have full energy balance closure of incoming
and outgoing radiation across all spectral domains. More advanced models use
Monte Carlo ray tracing (MCRT) to add multiple scattering and provide a sound
physical representation of the photon’s interaction with vegetation canopies (Disney
et al. 2006). While the inclusion of more physical processes (e.g., multiple scattering) certainly improves MCRT-type models over simpler approaches, their parameterization and benchmarking remains an issue. A large effort in testing RT models
was undertaken in the course of the radiation transfer modeling intercomparison
(RAMI) exercise, where different models were tested using a set of artificial scenes
of different complexity, including 3-D scenes, to see if the models produced comparable results (Widlowski et al. 2008, 2015). However, this benchmarking remained
relative (i.e., representing actual forest patches that could be validated with realworld Earth observation (EO) data acquired over the same area was not an aim of
the RAMI exercise). One reason, among others, for this was the lack of suitable
technologies and methods to capture and represent the 3-D vegetation structure at
small scales (e.g., branches, leaves, and/or shoots).
Today, laser scanning is an established tool for retrieving quantitative measures
of canopy structure (Nelson 1997; Lefsky et al. 1999; Næsset 2002; Morsdorf et al.
2004; Popescu et al. 2002; Morsdorf et al. 2006, 2010; Nelson 2013; Wulder et al.
2012). Airborne (ALS)-, terrestrial (TLS)-, and unmanned aerial vehicle (UAV)based laser scanning (Morsdorf et al. 2017) provide a direct means to assess vegetation structure by combining the known position and orientation of the sensor with
the time of flight of a laser pulse to produce a point cloud of exact 3-D coordinates.
Measurements can be made across scales (e.g., stand, tree, branch, and leaf level)
with finer scales often captured by close-range laser scanning (Morsdorf et al.
2018). The amount of structural detail contained in the point cloud can be overwhelming, and the extraction of meaningful information remains a challenge
(Wulder et al. 2013; Morsdorf et al. 2018). Due to large data sets, automated methods for the extraction of either semantic information, such as single-tree detection
based on ALS (Hyyppa et al. 2001; Morsdorf et al. 2004; Kaartinen et al. 2012;
Wang et al. 2016) or tree geometry reconstruction from TLS (Cote et al. 2009;
Raumonen et al. 2013) or the derivation of biophysical variables such as LAI
(Morsdorf et al. 2006), are preferable over manual and/or empirical approaches.
Figure 4.1 shows an example of a single-tree-based 3-D reconstruction using
ALS- and TLS-derived information. Using the 3-D information derived by ALS and
TLS, one can reconstruct a virtual representation of the forest that will be used by
the RT model to simulate the radiative regime of the canopy. Such an approach can
be utilized to upscale measurements of leaf biochemistry to the canopy scale and to
validate imaging spectroscopy-derived RS-EBVs across larger regions. In addition,
this approach uses a set of three physiological and tree morphological functional
traits, derived from imaging spectroscopy and laser scanning, respectively, to showcase the potential of these technologies to map the functional diversity of forests
and to provide relevant information for RS-enabled EBVs.
Here we describe how we (i) designed and implemented an observational scheme
to gather in-situ and structural data across several scales to simulate the 3-D radiative
4 The Laegeren Site: An Augmented Forest Laboratory
While they were better at modeling directional effects than 1-D models, they still
lacked multiple scattering and did not have full energy balance closure of incoming
and outgoing radiation across all spectral domains. More advanced models use
Monte Carlo ray tracing (MCRT) to add multiple scattering and provide a sound
physical representation of the photon’s interaction with vegetation canopies (Disney
et al. 2006). While the inclusion of more physical processes (e.g., multiple scattering) certainly improves MCRT-type models over simpler approaches, their parameterization and benchmarking remains an issue. A large effort in testing RT models
was undertaken in the course of the radiation transfer modeling intercomparison
(RAMI) exercise, where different models were tested using a set of artificial scenes
of different complexity, including 3-D scenes, to see if the models produced comparable results (Widlowski et al. 2008, 2015). However, this benchmarking remained
relative (i.e., representing actual forest patches that could be validated with realworld Earth observation (EO) data acquired over the same area was not an aim of
the RAMI exercise). One reason, among others, for this was the lack of suitable
technologies and methods to capture and represent the 3-D vegetation structure at
small scales (e.g., branches, leaves, and/or shoots).
Today, laser scanning is an established tool for retrieving quantitative measures
of canopy structure (Nelson 1997; Lefsky et al. 1999; Næsset 2002; Morsdorf et al.
2004; Popescu et al. 2002; Morsdorf et al. 2006, 2010; Nelson 2013; Wulder et al.
2012). Airborne (ALS)-, terrestrial (TLS)-, and unmanned aerial vehicle (UAV)based laser scanning (Morsdorf et al. 2017) provide a direct means to assess vegetation structure by combining the known position and orientation of the sensor with
the time of flight of a laser pulse to produce a point cloud of exact 3-D coordinates.
Measurements can be made across scales (e.g., stand, tree, branch, and leaf level)
with finer scales often captured by close-range laser scanning (Morsdorf et al.
2018). The amount of structural detail contained in the point cloud can be overwhelming, and the extraction of meaningful information remains a challenge
(Wulder et al. 2013; Morsdorf et al. 2018). Due to large data sets, automated methods for the extraction of either semantic information, such as single-tree detection
based on ALS (Hyyppa et al. 2001; Morsdorf et al. 2004; Kaartinen et al. 2012;
Wang et al. 2016) or tree geometry reconstruction from TLS (Cote et al. 2009;
Raumonen et al. 2013) or the derivation of biophysical variables such as LAI
(Morsdorf et al. 2006), are preferable over manual and/or empirical approaches.
Figure 4.1 shows an example of a single-tree-based 3-D reconstruction using
ALS- and TLS-derived information. Using the 3-D information derived by ALS and
TLS, one can reconstruct a virtual representation of the forest that will be used by
the RT model to simulate the radiative regime of the canopy. Such an approach can
be utilized to upscale measurements of leaf biochemistry to the canopy scale and to
validate imaging spectroscopy-derived RS-EBVs across larger regions. In addition,
this approach uses a set of three physiological and tree morphological functional
traits, derived from imaging spectroscopy and laser scanning, respectively, to showcase the potential of these technologies to map the functional diversity of forests
and to provide relevant information for RS-enabled EBVs.
Here we describe how we (i) designed and implemented an observational scheme
to gather in-situ and structural data across several scales to simulate the 3-D radiative
4 The Laegeren Site: An Augmented Forest Laboratory