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modeling approaches are beyond our scope in this chapter, and readers should
refer to several other relevant publications (e.g. Agarwal et al.
2002
; Parker et al.
2003
; Verburg et al.
2004
;An
2012
).
While spatially explicit modeling approaches can help extend landscape anal-
ysis beyond quantitative pattern description and into the area of forecasting and
prediction, there is a need to accept the limitations of prediction (Sutherland
2006
).
Most of the modeling efforts are technically driven, the justification and verifi-
cation of ecological concepts and theories are not adequate. Some game-like
simulators consider only a few untested factors, which often perform poorly when
predicting for the future. On the other hand, some models tend to be too ambitious,
considering too many variables, which are not easily parameterized. Many sim-
ulators do not contain components of model calibration and verification, and hence
their results are generally not good enough for prediction and forecasting.
11.4 Concluding Remarks
In this chapter, we have reviewed the utilities of remote sensing and geospatial
analysis for landscape pattern characterization, a fundamental pursuit in landscape
ecology. While landscape ecologists were among the earliest groups benefiting
from the use of remote sensing and GIS techniques, few of them are fully aware of
the latest development in these technical areas. Essentially global coverage of
remote sensor data with individual pixels ranging from sub-meters to a few kilo-
meters can help make connections across various levels of landscape pattern
analysis. The development of advanced image classification techniques and GIS
software engineering has helped landscape ecologists to revolutionize the analysis
of landscape structure by using pattern metrics. Nevertheless, the statistical prop-
erties and behavior of landscape metrics across a range of classification schemes
and landscapes, as well as their sensitivity to changing landscape patterns, are still
not fully understood. While GIS-based spatial analysis and modeling techniques
can help examine patterns, relationships, emerging trends, and dynamics, landscape
ecologists should also pay attention on some outstanding issues that we have dis-
cussed in this chapter. And landscape ecologists should fully understand both the
strengths and weaknesses of remote sensing and geospatial techniques in order to
better utilize these techniques in their specific applications. Finally, there is an
increasing need to collaborate between the disciplines of landscape ecology and
geospatial science that would not only lead to landscape ecology being taken more
seriously but also help expand the inferential capabilities of geospatial research.
Acknowledgments This work has been partially supported by the CAS/SAFEA International
Partnership Program for Creative Research Teams of ''Ecosystem Processes and Services''. The
lead author would like to acknowledge the funding support from the U.S. Environmental Pro-
tection Agency Great Lakes and Estuarine research program and Florida State University for the
time release in conducting the research.
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