99
richness maps. The ridge and the associated steep slopes affect many environmental
variables, which could act as filter for niche space and thus diversity. Higher altitude
is linked with decreased temperature, whereas the slope is contributing to lesser soil
depths and water availability and increased incoming radiation, at least for the part
south of the ridge. Thus, the environmental conditions are harsher close to the ridge,
which might explain the decrease of functional richness we observe in this context.
In the lower regions, morphological and physiological richness exhibit differing
spatial patterns. We assume that changes of the morphological richness in lower
parts parallel to ridge are caused by the different stand management regimes and
associated stand ages and structures. For the physiological richness, differences in
species composition seem to be a dominant effect, with the conifer-dominated
stands having a lower functional richness and the old-growth mixed stands being
functionally richer.
4.6 Conclusion and Outlook
Modern RS technologies increasingly face a validation paradox—i.e., it is very difficult to provide ground-based validation data that match the spatial (resolution and
extent), temporal, and thematic characteristics of modern EO data sets. As an example, ALS-derived tree height is assumed to be more accurate than field measurements, but it cannot be proved using field data alone. By using laser scanning-derived
3-D structure together with LOPs in an RT model approach, we have shown a way
to overcome such mismatches and provide a framework that could be established
across a range of sites around the globe to prototype and validate EO-based data and
products in the future. Such a forward validation will as well pave the way for products that are not measurable in the field, but still might be relevant in the context of
ecosystem function and diversity. The RTM approach provides a physical and
mechanistic way to learn about the information content of EO data, and a combination of this approach with recent developments in the machine learning domain
could provide interesting perspectives.
With the trait-based functional richness assessment, we demonstrated how a spatially extended monitoring using the complementary technologies imaging spectroscopy and lidar would work and what kind of insights into ecosystem functioning
it could generate. In addition, the trait maps and the derived functional richness
could be used for spatiotemporal gap filling of in-situ observational networks such
as the global forest biodiversity initiative, complementing the diversity information
that these provide.
In the future, these data streams in conjunction with the EBV concept (Fernández
et al., Chap. 18) will give policy-makers around the world useful tools to assess and
report on the biodiversity. To speed up this process, the European Space Agency
funded the GlobDiversity project starting in 2017 in the tradition of similar projects
for some of the essential climate variables. The project’s goal is to demonstrate the
capability and utility of producing a set of selected RS-EBV data sets in different
4 The Laegeren Site: An Augmented Forest Laboratory
richness maps. The ridge and the associated steep slopes affect many environmental
variables, which could act as filter for niche space and thus diversity. Higher altitude
is linked with decreased temperature, whereas the slope is contributing to lesser soil
depths and water availability and increased incoming radiation, at least for the part
south of the ridge. Thus, the environmental conditions are harsher close to the ridge,
which might explain the decrease of functional richness we observe in this context.
In the lower regions, morphological and physiological richness exhibit differing
spatial patterns. We assume that changes of the morphological richness in lower
parts parallel to ridge are caused by the different stand management regimes and
associated stand ages and structures. For the physiological richness, differences in
species composition seem to be a dominant effect, with the conifer-dominated
stands having a lower functional richness and the old-growth mixed stands being
functionally richer.
4.6 Conclusion and Outlook
Modern RS technologies increasingly face a validation paradox—i.e., it is very difficult to provide ground-based validation data that match the spatial (resolution and
extent), temporal, and thematic characteristics of modern EO data sets. As an example, ALS-derived tree height is assumed to be more accurate than field measurements, but it cannot be proved using field data alone. By using laser scanning-derived
3-D structure together with LOPs in an RT model approach, we have shown a way
to overcome such mismatches and provide a framework that could be established
across a range of sites around the globe to prototype and validate EO-based data and
products in the future. Such a forward validation will as well pave the way for products that are not measurable in the field, but still might be relevant in the context of
ecosystem function and diversity. The RTM approach provides a physical and
mechanistic way to learn about the information content of EO data, and a combination of this approach with recent developments in the machine learning domain
could provide interesting perspectives.
With the trait-based functional richness assessment, we demonstrated how a spatially extended monitoring using the complementary technologies imaging spectroscopy and lidar would work and what kind of insights into ecosystem functioning
it could generate. In addition, the trait maps and the derived functional richness
could be used for spatiotemporal gap filling of in-situ observational networks such
as the global forest biodiversity initiative, complementing the diversity information
that these provide.
In the future, these data streams in conjunction with the EBV concept (Fernández
et al., Chap. 18) will give policy-makers around the world useful tools to assess and
report on the biodiversity. To speed up this process, the European Space Agency
funded the GlobDiversity project starting in 2017 in the tradition of similar projects
for some of the essential climate variables. The project’s goal is to demonstrate the
capability and utility of producing a set of selected RS-EBV data sets in different
4 The Laegeren Site: An Augmented Forest Laboratory