PE&RS June 2015 - page 514

geomorphological feature of interest. In this research, eleva-
tion data with 1 m grid cells was used as main source. Ap-
plying rule sets of Eisank et al. (2010) on 1 m resolution data
will not produce accurate results, as curvature is measured at
a different scale (1 m) than the geomorphological feature of
interest (10 m). This promotes an interesting discussion for
future research on the optimal spatial resolution for specific
geomorphological features, and applying multi-resolution
data sets for whole landscape classifications.
In terms of the transferability of segmentation parameters,
there is likely less concern, as long as objects are not too large
and the data sets used have a similar standard deviation.
Slightly under-segmented features compensate for potential
segmentation errors (Dragut et al, 2014) as long as the same
features in different areas are not too different from each other
in terms of size and shape (Anders, 2013).
Expert knowledge remains a crucial step in the design of
the rule set and the assessment of the transferability to other
areas. We therefore encourage experts from different geoscien-
tific disciplines to translate detailed field knowledge into arith-
metic or relational concepts, which in turn can be translated as
classification rules. In that way, common patterns can be used
to optimize existing classification rules or designing more ge-
neric rules for the classification of true morphogenetic features.
Conclusions
The focus of this paper was to create and test the transferabili-
ty of an object-based rule set for the semi-automated extraction
of cirque components in Vorarlberg using airborne lidar data
and CIR imagery. The rule set successfully extracted cirque di-
vides, cirque headwalls, cirque floors, and the subcomponent
cirque lake with an overall accuracy of 81 percent in the train-
ing area. In addition, the presented rule set was transferable to
nearby areas that shared a common geological and geomorpho-
logical history if crucial classification thresholds were adapted
to local topographic conditions. However, this failed when
geological differences, pre-glacial topography or post-glacial
geomorphological processes significantly changed or disguised
topographic signatures of cirque components. These findings
hinder straightforward upscaling by application of rule sets to
larger research areas. Expert knowledge remains a crucial step
in the design of the rule set, size of the study area, and assess-
ment of the transferability to other areas.
Acknowledgments
This project was partly funded by Interreg Project Smart
Inspectors (Project number: I-1-03=176). In addition, financial
support was provided by the Virtual Lab for e-Science (vl-e)
project and internal funds of the Computational Geo-Ecology
department of the University of Amsterdam. We are grateful
to the “Land Vorarlberg” (www.vorarlberg.at)in Austria for
free use of the lidar data. Robin Gabriner is thanked for his
work on cirque extraction. Mike Smith is thanked for provid-
ing useful comments to the manuscript.
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514 June 2015 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING