217
Finally, the example presented here is meant to spur further theoretical, methodological, and empirical research aimed at developing a Global Biodiversity
Observatory (Geller et al., Chap. 20). Explicit incorporation of biotic information
into species-environment modeling may turn our focus away from the use only of
climatic information toward the “complete” evaluation of the drivers that determine
the species distributions (Fig. 9.1).
9.4.2 Enabling Large-Scale Biodiversity Change Detection
Since the last millennium, rising human population and activity have been major
drivers of environmental change on Earth, with consequences for the distribution
and abundance of biodiversity and associated ecosystem functioning (Tilman 1997;
Tylianakis et al. 2008). Thus, improving large-scale biodiversity change detection is
crucial to the development of effective policies that advance conservation and management of species and communities.
Such efforts are critical to enhancing efforts to develop a Global Biodiversity
Observatory (Geller et al., Chap. 20; Jetz et al. 2016). Research interest in using
S-RS has increased in recent years given its high potential for monitoring global
biodiversity and detecting change (Turner 2014; Jetz et al. 2016). For example, it is
possible to identify shifts in vegetation structure or to monitor the dynamics of the
growing season of an entire region, or within a specific species geographical range
(Fig. 9.4) using time series S-RS products such as LAI—half of the total green leaf
area per unit of horizontal ground surface area (Xiao et al. 2014)—which has a
temporal resolution of 8 days. This is particularly important given that ENM/SDM
theory assumes that species’ niches are stable across time and space and that species
and their environments are at pseudo-equilibrium, suggesting that species are occupying all suitable areas (Guisan and Thuiller 2005). However, the environment is
dynamic and can change even at small scales; species ranges can thus expand and
retract across time, varying within species lifetimes as well as over evolutionary
timescales encompassing many generations. Long-term series of S-RS data products (i.e., spatial and temporal) supply remarkable opportunities for assessing and
monitoring the state of the Earth’s surface and, combining with species- environment
relationship modeling, provide new frontiers for the prediction of species distributions and species monitoring across time and space (Randin et al. 2020). Indeed,
using biophysical variables derived from high-resolution S-RS products (i.e., LAI)
allows the identification of geographic areas where species actually occur (Fig. 9.7)
and thus has the potential for enhancing the predictions of a set of species that could
occur in an area—species pool—that is used for species assignments from direct RS
detection using hyperspectral data (see simulation in Fig. 7.8, Section 9.4.2 in
Meireles et al., Chap. 7).
In addition, enhancing predictive models of the species expected to be present in
a given geographic region can be coupled with other means of detecting which species are present based on spectroscopic imaging (Serbin et al. 2015; Bolch et al.,
9 Using Remote Sensing for Modeling and Monitoring Species Distributions
Finally, the example presented here is meant to spur further theoretical, methodological, and empirical research aimed at developing a Global Biodiversity
Observatory (Geller et al., Chap. 20). Explicit incorporation of biotic information
into species-environment modeling may turn our focus away from the use only of
climatic information toward the “complete” evaluation of the drivers that determine
the species distributions (Fig. 9.1).
9.4.2 Enabling Large-Scale Biodiversity Change Detection
Since the last millennium, rising human population and activity have been major
drivers of environmental change on Earth, with consequences for the distribution
and abundance of biodiversity and associated ecosystem functioning (Tilman 1997;
Tylianakis et al. 2008). Thus, improving large-scale biodiversity change detection is
crucial to the development of effective policies that advance conservation and management of species and communities.
Such efforts are critical to enhancing efforts to develop a Global Biodiversity
Observatory (Geller et al., Chap. 20; Jetz et al. 2016). Research interest in using
S-RS has increased in recent years given its high potential for monitoring global
biodiversity and detecting change (Turner 2014; Jetz et al. 2016). For example, it is
possible to identify shifts in vegetation structure or to monitor the dynamics of the
growing season of an entire region, or within a specific species geographical range
(Fig. 9.4) using time series S-RS products such as LAI—half of the total green leaf
area per unit of horizontal ground surface area (Xiao et al. 2014)—which has a
temporal resolution of 8 days. This is particularly important given that ENM/SDM
theory assumes that species’ niches are stable across time and space and that species
and their environments are at pseudo-equilibrium, suggesting that species are occupying all suitable areas (Guisan and Thuiller 2005). However, the environment is
dynamic and can change even at small scales; species ranges can thus expand and
retract across time, varying within species lifetimes as well as over evolutionary
timescales encompassing many generations. Long-term series of S-RS data products (i.e., spatial and temporal) supply remarkable opportunities for assessing and
monitoring the state of the Earth’s surface and, combining with species- environment
relationship modeling, provide new frontiers for the prediction of species distributions and species monitoring across time and space (Randin et al. 2020). Indeed,
using biophysical variables derived from high-resolution S-RS products (i.e., LAI)
allows the identification of geographic areas where species actually occur (Fig. 9.7)
and thus has the potential for enhancing the predictions of a set of species that could
occur in an area—species pool—that is used for species assignments from direct RS
detection using hyperspectral data (see simulation in Fig. 7.8, Section 9.4.2 in
Meireles et al., Chap. 7).
In addition, enhancing predictive models of the species expected to be present in
a given geographic region can be coupled with other means of detecting which species are present based on spectroscopic imaging (Serbin et al. 2015; Bolch et al.,
9 Using Remote Sensing for Modeling and Monitoring Species Distributions