243
that ecological processes influencing the assembly of communities of organisms are
scale dependent (Levin 1992; McGill 2010). A spatially explicit framework for conceptualizing community assembly describes external filters (e.g., climate or soils)
that sort species from a regional pool at a spatial scale larger than the community
and internal filters that sort species into a community from a subset of the species
that make it through the external filter (e.g., microenvironmental heterogeneity,
biotic interactions; Violle et al. 2012; Fig. 10.5). These “assembly rules” about how
communities form remain a controversial paradigm with uncertainty about which
processes operate at which scales (McGill 2010; Belmaker et al. 2015). Observing
and quantifying relationships between geodiversity and biodiversity and how these
relationships change with scale, however, are essential for moving forward regardless of one’s position on these controversies. To most effectively use geodiversity to
help explain and predict patterns of biodiversity, we need a framework that addresses
the scaling relationship between biodiversity and geodiversity.
Furthermore, there are important disconnects in both scale and expertise between
biodiversity science and RS (Petorelli et al. 2014) that once addressed will aid in the
development of such a framework. Whereas the availability of remotely sensed geodiversity data products has increased, many of the scales are too coarse to reflect the
environmental and biological conditions that often drive more fine-scaled spatially
heterogeneous biodiversity patterns (Nadeau et al. 2017) and thus may require complex post-processing techniques unfamiliar to most biodiversity scientists before
they can be used appropriately in biodiversity models. Also, there are likely many
important aspects of geodiversity that at this time can only be derived through in-situ
measurements and cannot be remotely sensed. Determining how physical and biological drivers influence biodiversity across spatial and temporal scales is a central
focus of ecology. However, most models predicting future patterns of biodiversity
assume broad-scale climatic drivers—temperature and precipitation—are sole drivers and leave out important biological drivers (Zarnetske et al. 2012; Record et al.
2013). Biological drivers such as dispersal ability and biotic interactions (e.g., competition) are often mediated by the structure of the landscape, including geophysical
feature configuration, topographic complexity, and habitat patch arrangement
(Zarnetske et al. 2017). Yet a significant knowledge gap remains about how the
relationships between biodiversity and its geophysical and biological drivers change
with respect to space and time—perhaps owing to the scale mismatch between finescale point-level biodiversity data and many coarse-scale remotely sensed data
products.
Many ecological questions are addressed at scales much finer than the grain size
of MODIS or GPM, which makes statistical downscaling a necessity for remotely
sensed products to be used. Yet the landscape of options for statistical downscaling
is vast and complex (Pourmokhtarian et al. 2016). In addition, the increasing availability of airborne topographic data like LiDAR makes the possibility of finer-grain
analysis even more viable, yet these data also bring another dimension of complexity and a lack of standardization across platforms and methods.
Open access analytical tools and training will provide ways forward given data
downloading and processing challenges. The Application for Extracting and
10 Remote Sensing of Geodiversity as a Link to Biodiversity
that ecological processes influencing the assembly of communities of organisms are
scale dependent (Levin 1992; McGill 2010). A spatially explicit framework for conceptualizing community assembly describes external filters (e.g., climate or soils)
that sort species from a regional pool at a spatial scale larger than the community
and internal filters that sort species into a community from a subset of the species
that make it through the external filter (e.g., microenvironmental heterogeneity,
biotic interactions; Violle et al. 2012; Fig. 10.5). These “assembly rules” about how
communities form remain a controversial paradigm with uncertainty about which
processes operate at which scales (McGill 2010; Belmaker et al. 2015). Observing
and quantifying relationships between geodiversity and biodiversity and how these
relationships change with scale, however, are essential for moving forward regardless of one’s position on these controversies. To most effectively use geodiversity to
help explain and predict patterns of biodiversity, we need a framework that addresses
the scaling relationship between biodiversity and geodiversity.
Furthermore, there are important disconnects in both scale and expertise between
biodiversity science and RS (Petorelli et al. 2014) that once addressed will aid in the
development of such a framework. Whereas the availability of remotely sensed geodiversity data products has increased, many of the scales are too coarse to reflect the
environmental and biological conditions that often drive more fine-scaled spatially
heterogeneous biodiversity patterns (Nadeau et al. 2017) and thus may require complex post-processing techniques unfamiliar to most biodiversity scientists before
they can be used appropriately in biodiversity models. Also, there are likely many
important aspects of geodiversity that at this time can only be derived through in-situ
measurements and cannot be remotely sensed. Determining how physical and biological drivers influence biodiversity across spatial and temporal scales is a central
focus of ecology. However, most models predicting future patterns of biodiversity
assume broad-scale climatic drivers—temperature and precipitation—are sole drivers and leave out important biological drivers (Zarnetske et al. 2012; Record et al.
2013). Biological drivers such as dispersal ability and biotic interactions (e.g., competition) are often mediated by the structure of the landscape, including geophysical
feature configuration, topographic complexity, and habitat patch arrangement
(Zarnetske et al. 2017). Yet a significant knowledge gap remains about how the
relationships between biodiversity and its geophysical and biological drivers change
with respect to space and time—perhaps owing to the scale mismatch between finescale point-level biodiversity data and many coarse-scale remotely sensed data
products.
Many ecological questions are addressed at scales much finer than the grain size
of MODIS or GPM, which makes statistical downscaling a necessity for remotely
sensed products to be used. Yet the landscape of options for statistical downscaling
is vast and complex (Pourmokhtarian et al. 2016). In addition, the increasing availability of airborne topographic data like LiDAR makes the possibility of finer-grain
analysis even more viable, yet these data also bring another dimension of complexity and a lack of standardization across platforms and methods.
Open access analytical tools and training will provide ways forward given data
downloading and processing challenges. The Application for Extracting and
10 Remote Sensing of Geodiversity as a Link to Biodiversity