457
Beyond land cover classification, one of the most extensive uses of RS data is to
produce distribution maps of species, communities, and ecosystems (Kerr and
Ostrovsky 2003). Most RS studies of biodiversity focused on mapping species using
all kinds of information from optical, radar, and Light Detection and Ranging
(LiDAR) data. Nagendra (2001) reviewed the potential of RS data for assessing
biodiversity, namely, species mapping and species diversity and habitat mapping,
and concluded that at the time the most feasible application of RS would be to map
species distributions and habitat, the former at smaller spatial scales and the latter at
larger scales. RS data such as those from Landsat and other multispectral sensors
have been widely used to map species and vegetation communities (Xie et al. 2008).
There is now a growing literature on identifying species in many case studies.
However, use of imaging spectroscopy for species identification needs to be understood at a more fundamental level—especially the development of generalized
methodologies and rules for detection and mapping, which is an area of active
research today. Conceptually, we have yet to resolve how to identify unique spectral
signatures for the estimated 400,000 extant plant species or groups of species (i.e.,
functional types or optical types; Ustin and Gamon 2010). In contrast to geologic
minerals, which are often spectrally distinct, all land plants share a common basic
metabolism and biochemistry. This fundamental similarity makes identification of
plant species difficult. The interactions of a spectral signal with environmental conditions and shifts in spectral signatures through phenological stages contribute to
spectral variation, in addition to the characteristic properties of individual species
(Ustin and Jacquemoud, Chap. 14). In recent years, with the advent of hyperspectral
sensors and the fusion of these data sets with other auxiliary data, novel avenues to
map and monitor biodiversity have emerged. These new data sets make it possible
to directly discriminate species in terrestrial and freshwater ecosystems (Jones and
Vaughan 2010; Turak et al. 2017; Choa et al. 2012; Fassnacht et al. 2016) and assess
relationships between the diversity of spectra and the diversity of species and the
fundamentals of the spectral diversity hypothesis (i.e., that the diversity of spectral
profiles generally predicts diversity of species, Nagendra 2001), which are presented in other chapters of this book (see especially Schweiger, Chap. 15; CavenderBares et al., Chap. 2). A recent study highlights the relation of spectral diversity to
functional and phylogenetic components of biodiversity (Schweiger et al. 2018),
and this topic is covered in more detail in Meireles et al. (Chap. 7).
Another avenue in which RS can contribute to biodiversity monitoring is through
its use in species distribution models (SDMs) (see Pinto-Ledézma and CavenderBares, Chap. 9; Paz et al., Chap. 11). SDMs are empirical statistical approaches that
predict the spatial distribution of species (Guisan and Zimmermann 2000), and the
choice of environmental predictors is fundamental for SDM. RS measurements of
vegetation condition (Turner et al. 2003), ecosystem productivity (Running et al.
2004), and seasonality (Reed et al. 1994), among others, are now available over time
series (e.g., Landsat time series; Kennedy et al. 2014) and might be used in SDMs
(Bradley and Fleishman 2008; He et al. 2015) making it possible to predict species
distributions over time. Although the use of RS in SDMs is widely advocated and
applied, it has yet to be scaled to most species, especially non-plant taxa. Promising
progress toward the inclusion of RS products in SDMs includes responses to
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…
Beyond land cover classification, one of the most extensive uses of RS data is to
produce distribution maps of species, communities, and ecosystems (Kerr and
Ostrovsky 2003). Most RS studies of biodiversity focused on mapping species using
all kinds of information from optical, radar, and Light Detection and Ranging
(LiDAR) data. Nagendra (2001) reviewed the potential of RS data for assessing
biodiversity, namely, species mapping and species diversity and habitat mapping,
and concluded that at the time the most feasible application of RS would be to map
species distributions and habitat, the former at smaller spatial scales and the latter at
larger scales. RS data such as those from Landsat and other multispectral sensors
have been widely used to map species and vegetation communities (Xie et al. 2008).
There is now a growing literature on identifying species in many case studies.
However, use of imaging spectroscopy for species identification needs to be understood at a more fundamental level—especially the development of generalized
methodologies and rules for detection and mapping, which is an area of active
research today. Conceptually, we have yet to resolve how to identify unique spectral
signatures for the estimated 400,000 extant plant species or groups of species (i.e.,
functional types or optical types; Ustin and Gamon 2010). In contrast to geologic
minerals, which are often spectrally distinct, all land plants share a common basic
metabolism and biochemistry. This fundamental similarity makes identification of
plant species difficult. The interactions of a spectral signal with environmental conditions and shifts in spectral signatures through phenological stages contribute to
spectral variation, in addition to the characteristic properties of individual species
(Ustin and Jacquemoud, Chap. 14). In recent years, with the advent of hyperspectral
sensors and the fusion of these data sets with other auxiliary data, novel avenues to
map and monitor biodiversity have emerged. These new data sets make it possible
to directly discriminate species in terrestrial and freshwater ecosystems (Jones and
Vaughan 2010; Turak et al. 2017; Choa et al. 2012; Fassnacht et al. 2016) and assess
relationships between the diversity of spectra and the diversity of species and the
fundamentals of the spectral diversity hypothesis (i.e., that the diversity of spectral
profiles generally predicts diversity of species, Nagendra 2001), which are presented in other chapters of this book (see especially Schweiger, Chap. 15; CavenderBares et al., Chap. 2). A recent study highlights the relation of spectral diversity to
functional and phylogenetic components of biodiversity (Schweiger et al. 2018),
and this topic is covered in more detail in Meireles et al. (Chap. 7).
Another avenue in which RS can contribute to biodiversity monitoring is through
its use in species distribution models (SDMs) (see Pinto-Ledézma and CavenderBares, Chap. 9; Paz et al., Chap. 11). SDMs are empirical statistical approaches that
predict the spatial distribution of species (Guisan and Zimmermann 2000), and the
choice of environmental predictors is fundamental for SDM. RS measurements of
vegetation condition (Turner et al. 2003), ecosystem productivity (Running et al.
2004), and seasonality (Reed et al. 1994), among others, are now available over time
series (e.g., Landsat time series; Kennedy et al. 2014) and might be used in SDMs
(Bradley and Fleishman 2008; He et al. 2015) making it possible to predict species
distributions over time. Although the use of RS in SDMs is widely advocated and
applied, it has yet to be scaled to most species, especially non-plant taxa. Promising
progress toward the inclusion of RS products in SDMs includes responses to
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…