239
Each of these data sets comes with its own uncertainties (e.g., observation errors)
and user challenges. For instance, citizen science data require detailed metadata on
the sampling process to ensure that citizen scientists are able to reduce error and
bias as they collect data and to enable those analyzing the data to model potential
uncertainty (Bird et al. 2014). Despite these sources of uncertainty and logistical
hurdles, these data provide a useful starting point for understanding the relationship
between biodiversity and geodiversity.
10.5 A Case Study Linking RS of Geodiversity to Tree
Diversity in the Eastern United States
To motivate explorations of the relationship between biodiversity and geodiversity
with remotely sensed data, we provide an example using biodiversity data from the
FIA program of the US Forest Service (O’Connell et al. 2017) and geodiversity data
on elevation from SRTM. We selected elevation as a covariate because patterns of
tree diversity often vary with elevation (Körner 2012). While some studies promote
the use of many geodiversity components (Serrano et al. 2009; Hjort and Luoto
2010; Bailey et al. 2017; Tukiainen et al. 2017), a great deal of the variation in geodiversity is captured by the standard deviation in elevation (Hjort and Luoto 2012),
which is used in this analysis.
The FIA program uses a two-phase protocol to characterize the nation’s forest
resources. In phase one, all land in the United States is categorized as either “forested” or “not forested” using remotely sensed data. In phase two, in every 2428 ha
of land classified as forested, one permanent FIA plot is placed for in-situ sampling.
Each FIA plot consists of four 7.2-m-fixed-radius subplots wherein all trees
>12.7 cm diameter at breast height are measured. FIA plot measurements began in
the 1940s, but a consistent nationwide sampling protocol was not implemented until
2001. In the analysis presented, we used data from the most recent full plot FIA
inventory from 2012–2016; the SRTM data were collected in 2009. Although there
is not perfect temporal overlap in the geodiversity and biodiversity data used in this
example, we do not expect that topography at a spatial resolution of 50 km would
have changed much over the time period encompassed by both data sets for this part
of the world.
We fixed the spatial extent of the analysis to the contiguous United States east
of 100°W longitude (n = 90,250 plots total) and selected a grain size of 50 km for
calculating alpha (within site), beta (turnover between sites), and gamma (total
across all sites) diversities within a radius centered on each FIA plot. All biodiversity metrics were based on species abundances as quantified by the total basal area
of each tree species in each plot. Alpha diversity was calculated as the median
abundance- weighted effective species number of all plots falling within a 50 km
radius of the focal FIA plot, including the focal plot. Beta diversity was calculated
as the mean abundance-weighted pairwise Sørensen dissimilarity of all pairs of
plots within a 50 km radius of the focal plot, including the focal plot. Gamma
10 Remote Sensing of Geodiversity as a Link to Biodiversity
Each of these data sets comes with its own uncertainties (e.g., observation errors)
and user challenges. For instance, citizen science data require detailed metadata on
the sampling process to ensure that citizen scientists are able to reduce error and
bias as they collect data and to enable those analyzing the data to model potential
uncertainty (Bird et al. 2014). Despite these sources of uncertainty and logistical
hurdles, these data provide a useful starting point for understanding the relationship
between biodiversity and geodiversity.
10.5 A Case Study Linking RS of Geodiversity to Tree
Diversity in the Eastern United States
To motivate explorations of the relationship between biodiversity and geodiversity
with remotely sensed data, we provide an example using biodiversity data from the
FIA program of the US Forest Service (O’Connell et al. 2017) and geodiversity data
on elevation from SRTM. We selected elevation as a covariate because patterns of
tree diversity often vary with elevation (Körner 2012). While some studies promote
the use of many geodiversity components (Serrano et al. 2009; Hjort and Luoto
2010; Bailey et al. 2017; Tukiainen et al. 2017), a great deal of the variation in geodiversity is captured by the standard deviation in elevation (Hjort and Luoto 2012),
which is used in this analysis.
The FIA program uses a two-phase protocol to characterize the nation’s forest
resources. In phase one, all land in the United States is categorized as either “forested” or “not forested” using remotely sensed data. In phase two, in every 2428 ha
of land classified as forested, one permanent FIA plot is placed for in-situ sampling.
Each FIA plot consists of four 7.2-m-fixed-radius subplots wherein all trees
>12.7 cm diameter at breast height are measured. FIA plot measurements began in
the 1940s, but a consistent nationwide sampling protocol was not implemented until
2001. In the analysis presented, we used data from the most recent full plot FIA
inventory from 2012–2016; the SRTM data were collected in 2009. Although there
is not perfect temporal overlap in the geodiversity and biodiversity data used in this
example, we do not expect that topography at a spatial resolution of 50 km would
have changed much over the time period encompassed by both data sets for this part
of the world.
We fixed the spatial extent of the analysis to the contiguous United States east
of 100°W longitude (n = 90,250 plots total) and selected a grain size of 50 km for
calculating alpha (within site), beta (turnover between sites), and gamma (total
across all sites) diversities within a radius centered on each FIA plot. All biodiversity metrics were based on species abundances as quantified by the total basal area
of each tree species in each plot. Alpha diversity was calculated as the median
abundance- weighted effective species number of all plots falling within a 50 km
radius of the focal FIA plot, including the focal plot. Beta diversity was calculated
as the mean abundance-weighted pairwise Sørensen dissimilarity of all pairs of
plots within a 50 km radius of the focal plot, including the focal plot. Gamma
10 Remote Sensing of Geodiversity as a Link to Biodiversity