229
difference, squared, between the elevation in a focal cell and all other cells in a
sample unit could be used as a measure of topographic heterogeneity. The coefficient of variation is a similar measure of heterogeneity, though it is standardized to
the mean elevation of the sample unit. Pairwise site differences in multiple geographical features can be used as predictors in matrix regression such as generalized
dissimilarity models (Ferrier et al. 2007) or more generally a Mantel test (Tuomisto
et al. 2003; Legendre et al. 2005), though mechanistic interpretation is limited when
geographical features are combined in this way.
Additional approaches include a geodiversity atlas that classifies areas as having
very high, high, moderate, low, and very low geodiversity (Kozlowski 1999), quantifying geodiversity in terms of total component resource potential (i.e., energy,
water, space, and nutrients; Parks and Mulligan 2010), and the geodiversity index
(Gd) that relates the variety of physical elements (i.e., geomorphological, hydrological, soils) with the roughness and surface of the previously established geomorphological units according to the formula:
Gd
EgR
lnS
=
(10.1)
where Eg is the number of different physical elements, R is the coefficient of roughness of the unit, and S is the surface of the unit (km
2
). The Gd is a semiquantitative
scale that permits the establishment of five values of geodiversity, from very low to
very high for each homogeneous unit. It is argued that use of Gd would allow easier
comparison of units and aid suitable management of protected areas (Serrano et al.
2009; Hjort and Luoto 2010; Tukiainen et al. 2017).
With continuously measured remotely sensed geographical features, the sample
unit (i.e., grain size) can be modified to examine within site and total site (and thus
between sites) geodiversity. Additionally, RS data can uniquely address how relationships between geodiversity and biodiversity change across scales. Various combinations of changing grain and extent (change grain maintain extent, change extent
maintain grain, change grain and extent) could be examined to explore scaling relationships (Barton et al. 2013).
10.3 Remote Sensing of Geodiversity
In the following sections, we describe the different components of geodiversity
(Table 10.1), some of the ways they can be quantified, and the current state of technologies available to measure them remotely via airborne or satellite observations
(Table 10.2). To match current interests in global biodiversity databases (e.g., the
Global Biodiversity Information Facility, gbif.org), and because of the importance
of scaling from local to much larger extents, we focus here on globally available
data; however, we also mention some local scale RS applications. In particular,
given that more and more remotely sensed data have been made publically available,
we highlight open access remotely sensed geodiversity data.
10 Remote Sensing of Geodiversity as a Link to Biodiversity
difference, squared, between the elevation in a focal cell and all other cells in a
sample unit could be used as a measure of topographic heterogeneity. The coefficient of variation is a similar measure of heterogeneity, though it is standardized to
the mean elevation of the sample unit. Pairwise site differences in multiple geographical features can be used as predictors in matrix regression such as generalized
dissimilarity models (Ferrier et al. 2007) or more generally a Mantel test (Tuomisto
et al. 2003; Legendre et al. 2005), though mechanistic interpretation is limited when
geographical features are combined in this way.
Additional approaches include a geodiversity atlas that classifies areas as having
very high, high, moderate, low, and very low geodiversity (Kozlowski 1999), quantifying geodiversity in terms of total component resource potential (i.e., energy,
water, space, and nutrients; Parks and Mulligan 2010), and the geodiversity index
(Gd) that relates the variety of physical elements (i.e., geomorphological, hydrological, soils) with the roughness and surface of the previously established geomorphological units according to the formula:
Gd
EgR
lnS
=
(10.1)
where Eg is the number of different physical elements, R is the coefficient of roughness of the unit, and S is the surface of the unit (km
2
). The Gd is a semiquantitative
scale that permits the establishment of five values of geodiversity, from very low to
very high for each homogeneous unit. It is argued that use of Gd would allow easier
comparison of units and aid suitable management of protected areas (Serrano et al.
2009; Hjort and Luoto 2010; Tukiainen et al. 2017).
With continuously measured remotely sensed geographical features, the sample
unit (i.e., grain size) can be modified to examine within site and total site (and thus
between sites) geodiversity. Additionally, RS data can uniquely address how relationships between geodiversity and biodiversity change across scales. Various combinations of changing grain and extent (change grain maintain extent, change extent
maintain grain, change grain and extent) could be examined to explore scaling relationships (Barton et al. 2013).
10.3 Remote Sensing of Geodiversity
In the following sections, we describe the different components of geodiversity
(Table 10.1), some of the ways they can be quantified, and the current state of technologies available to measure them remotely via airborne or satellite observations
(Table 10.2). To match current interests in global biodiversity databases (e.g., the
Global Biodiversity Information Facility, gbif.org), and because of the importance
of scaling from local to much larger extents, we focus here on globally available
data; however, we also mention some local scale RS applications. In particular,
given that more and more remotely sensed data have been made publically available,
we highlight open access remotely sensed geodiversity data.
10 Remote Sensing of Geodiversity as a Link to Biodiversity