462
The second is that there is a growing need that conservation moves beyond protected areas to protected landscapes, which include livelihoods. Remotely sensed
supporting services include habitat, nutrient cycling, terrestrial and aquatic primary
production, soil formation, and provision of biological refugia, but there is an acute
lack of RS applications in the study of cultural ES with the exception of cultural
heritage and recreation (Andrew et al. 2014; de Araujo Barbosa et al. 2015; Cord
et al. 2017). Similar to the concept of essential biodiversity variables, indicators
have been developed to allow fast and efficient assessment of ES across large spatial
scales. One of these indicators is the recently proposed Rapid Ecosystem Function
Assessment (REFA, Meyer et al. 2015). REFA builds on a suite of core variables
such as aboveground primary productivity, soil fertility, decomposition, and pollination. While many of these could theoretically be assessed using RS technology,
the concept was developed for in-situ measured data. On the other hand, Cerreta and
Poli (2017) propose a GIS-based framework with scalable and transferable methodology to rapidly assess multiple ecosystem functional features of a landscape using
a multi-criteria spatial decision support system. While none of these approaches has
been implemented for the use of RS technology at large spatial extents, there is
potential for setting up near-real-time systems of fast ES assessment. One issue with
extending temporal coverage and amount of ESs covered, despite increasing
amounts and quality of remotely sensed data products, is the lack of ground data
necessary to validate satellite outputs (Jones and Vaughan 2010). Alternatives to
traditional validation approaches based on comparison with ground data are new
methods such as the application of process models to test the consistency of time
series of more complex satellite data products (Loew et al. 2017).
17.4 Data Availability and Issues
Large amounts of auxiliary data from RS and other sources are now freely available,
together with the models and technology necessary to process disparate data in
geospatial frameworks. To take advantage of the full range of aspects covered,
increase the reliability of different data sets, and account for data uncertainty as
much as possible, auxiliary data sources are often used in tandem with RS data. For
example, satellite and census land use data can be integrated to scale
administrative- level information to the globe (Ellis et al. 2013), and vegetation indices from satellites can be combined with local ecological knowledge to improve
assessments of ecosystem degradation (Eddy et al. 2017). We provide an overview
of the most frequently and widely integrated remotely sensed and auxiliary data
products using approaches discussed at the end of this chapter. We focus on data
sources that are useful in assessing plant diversity-related aspects at the landscape
scale. In a recent review, Englund et al. (2017) found RS-related publications to use
the term landscape rather loosely as describing studies at anything between 24 and
122 million ha. Here, we define landscape as referring to studies going beyond
local, plot-level scales and generally not past country-level scales, although many
F. Schrodt et al.
The second is that there is a growing need that conservation moves beyond protected areas to protected landscapes, which include livelihoods. Remotely sensed
supporting services include habitat, nutrient cycling, terrestrial and aquatic primary
production, soil formation, and provision of biological refugia, but there is an acute
lack of RS applications in the study of cultural ES with the exception of cultural
heritage and recreation (Andrew et al. 2014; de Araujo Barbosa et al. 2015; Cord
et al. 2017). Similar to the concept of essential biodiversity variables, indicators
have been developed to allow fast and efficient assessment of ES across large spatial
scales. One of these indicators is the recently proposed Rapid Ecosystem Function
Assessment (REFA, Meyer et al. 2015). REFA builds on a suite of core variables
such as aboveground primary productivity, soil fertility, decomposition, and pollination. While many of these could theoretically be assessed using RS technology,
the concept was developed for in-situ measured data. On the other hand, Cerreta and
Poli (2017) propose a GIS-based framework with scalable and transferable methodology to rapidly assess multiple ecosystem functional features of a landscape using
a multi-criteria spatial decision support system. While none of these approaches has
been implemented for the use of RS technology at large spatial extents, there is
potential for setting up near-real-time systems of fast ES assessment. One issue with
extending temporal coverage and amount of ESs covered, despite increasing
amounts and quality of remotely sensed data products, is the lack of ground data
necessary to validate satellite outputs (Jones and Vaughan 2010). Alternatives to
traditional validation approaches based on comparison with ground data are new
methods such as the application of process models to test the consistency of time
series of more complex satellite data products (Loew et al. 2017).
17.4 Data Availability and Issues
Large amounts of auxiliary data from RS and other sources are now freely available,
together with the models and technology necessary to process disparate data in
geospatial frameworks. To take advantage of the full range of aspects covered,
increase the reliability of different data sets, and account for data uncertainty as
much as possible, auxiliary data sources are often used in tandem with RS data. For
example, satellite and census land use data can be integrated to scale
administrative- level information to the globe (Ellis et al. 2013), and vegetation indices from satellites can be combined with local ecological knowledge to improve
assessments of ecosystem degradation (Eddy et al. 2017). We provide an overview
of the most frequently and widely integrated remotely sensed and auxiliary data
products using approaches discussed at the end of this chapter. We focus on data
sources that are useful in assessing plant diversity-related aspects at the landscape
scale. In a recent review, Englund et al. (2017) found RS-related publications to use
the term landscape rather loosely as describing studies at anything between 24 and
122 million ha. Here, we define landscape as referring to studies going beyond
local, plot-level scales and generally not past country-level scales, although many
F. Schrodt et al.