Geoscience Reference
In-Depth Information
16.1 INTRODUCTION
Recent advances in the field of landscape ecology have included the development and application
of quantitative approaches to characterize landscape condition and processes based on landscape
patterns (Turner et al., 2001). Central to these approaches is the increasing availability of spatial
data characterizing landscape constituents and patterns, which are commonly derived using various
remote sensor data (i.e., aerial photography or multispectral imagery). Spatial pattern metrics
provide quantitative descriptions of the spatial composition and configurations of habitat or land-
cover (LC) types that can be applied to provide useful indicators of the habitat quality, ecosystem
function, and the flow of energy and materials within a landscape. Landscape metrics have been
used to compare ecological quality across landscapes (Riitters et al., 1995) and across scales (Frohn,
1997) and to track changes in landscape patterns through time (Henebry and Goodin, 2002). These
comparisons can often provide quantitative statements of the relative quality of landscapes with
respect to some spatial pattern concept (e.g., habitat fragmentation).
Uncertainty associated with landscape metrics has several components, including (1) accuracy
(how well the calculated values match the actual values), (2) precision (how closely repeated
measurements get to the same value), and (3) meaning (how comparisons between metric values
should be interpreted). In practical terms, accuracy, precision, and the meaning of metric values
are affected by several factors that include the definitions of categories on the landscape map, map
accuracy, and validity and uniqueness of the metric of interest. Standard methods for assessing LC
map accuracy provide useful information but are inadequate as indicators of the spatial metric
accuracy because they lack information concerning spatial patterns of uncertainty and the corre-
spondence between the map category definitions and landscape concepts of interest. Further, direct
estimation of the accuracy of landscape metric values is problematic. Unlike LC maps, standard
procedures are currently not available to support landscape metric accuracy assessment. Also, the
scale dependence of landscape metric values complicates comparisons between field observations
and map-based calculations.
As a transformation process, in which mapped landscape classes are transformed into landscape
measurements describing the composition and configuration of that landscape, landscape metrics
can be evaluated using precision and meaning diagnostics (Figure 16.1). The primary objective is
to acquire a metric with a known and relatively high degree of accuracy and precision that is
interpretable with respect to the landscape characteristic(s) of interest. The research presented in
this chapter addresses the following issues: (1) precision estimates associated with various landscape
metrics derived from satellite images, (2) sensitivity of landscape metrics relative to differences in
landscape class definitions, and (3) sensitivities of landscape metrics to landscape pattern concepts
of interest (e.g., ecotone abruptness or forest fragmentation) vs. potential confounding concepts
(e.g., patchiness or amount of forest).
Landscape
Metric
Output
Value
Input Map
f( Precision
I
,
∆ Precision )
=
Precision
O
f( Meaning
I
,
∆ Meaning )
=
Meaning
O
Figure 16.1
Illustration of the issues affecting the quality and utility affecting landscape pattern metric values
derived from landscape class maps. The precision and meaning of output values from landscape
metrics are functions of the precision and meaning of the input landscape maps and the effect of
the metric transformation.
