mainly data gaps due to cloud cover, which is a relevant problem particularly in the
Alps. The overall accuracy of 87 % is high, taking into account the large number of
classes (12). However, while a remote sensing-based classification can hardly reach the
accuracy of a field-based survey it can compete with the widely used approach of the
photointerpretation of orthophotos. In particular the multi-temporal approach allows
classes to be separated based on phenological differences or differences in management
(mowing) that cannot be separated using a mono-temporal approach like an
orthotophoto. Furthermore, the higher number of spectral bands with a better radiometric resolution and robustness of satellite data compared to orthophotos allow a
semi-automatic classification which saves costs and labour. Key factors for a high
quality classification result are sufficient samples in terms of amount and quality, which
should be verified in the field. Moreover, the combination of automatic classification
approaches with expert classification rules, which are based on profound knowledge of
the habitats in the region, are required for a successful application of the proposed
method. The possibility to also analyse some aspects of the conservation status of
habitats adds further value to the approach. Regarding the potential impacts of climate
change, the most obvious impact, which is a shift in vegetation zones to higher altitudes
(shrubs, treeline, glacier foreland), can be effectively monitored with remote sensing.
7.5 General Conclusion and Discussion
In this chapter the possibility of using remote sensing information for monitoring
climate-induced impacts on habitats has been demonstrated for three test cases in
the Continental, Alpine, and Pannonian biogeographic regions. Moreover, habitats
from the land-cover types forest, wetland, and Alpine environment were evaluated
to assess the feasibility of supporting the monitoring of climate change impacts.
In those test cases with a validation of the classification results, the accuracy is
higher than 80 %. Given the complexity of the target classes, this result can be
accepted as a basis for the further derivation of the conservation status of classes.
Generally, comparison with future image acquisitions for the evaluation of changes
is possible. However, these changes might have causes other than pure (and often
very gradually occurring) climate change. Variations in market prices of timber or
crops may influence usage intensity, as may the subsidy schemes of the European
Union or the changing touristic utilisation of an area. It is not possible to distinguish
anthropogenic land-use changes from those induced by climate change by means of
the methods discussed.
The results were achieved using multi-temporal RapidEye imagery. At least two
scenes per year were available for the presented studies. The advantage of utilising
several pieces of information from the phenological cycle was stated in all studies,
as well as the necessity of working with very high spatial resolution imagery
(below 10 m).
7 Remote Sensing-Based Monitoring of Potential Climate-Induced Impacts on Habitats
109
Alps. The overall accuracy of 87 % is high, taking into account the large number of
classes (12). However, while a remote sensing-based classification can hardly reach the
accuracy of a field-based survey it can compete with the widely used approach of the
photointerpretation of orthophotos. In particular the multi-temporal approach allows
classes to be separated based on phenological differences or differences in management
(mowing) that cannot be separated using a mono-temporal approach like an
orthotophoto. Furthermore, the higher number of spectral bands with a better radiometric resolution and robustness of satellite data compared to orthophotos allow a
semi-automatic classification which saves costs and labour. Key factors for a high
quality classification result are sufficient samples in terms of amount and quality, which
should be verified in the field. Moreover, the combination of automatic classification
approaches with expert classification rules, which are based on profound knowledge of
the habitats in the region, are required for a successful application of the proposed
method. The possibility to also analyse some aspects of the conservation status of
habitats adds further value to the approach. Regarding the potential impacts of climate
change, the most obvious impact, which is a shift in vegetation zones to higher altitudes
(shrubs, treeline, glacier foreland), can be effectively monitored with remote sensing.
7.5 General Conclusion and Discussion
In this chapter the possibility of using remote sensing information for monitoring
climate-induced impacts on habitats has been demonstrated for three test cases in
the Continental, Alpine, and Pannonian biogeographic regions. Moreover, habitats
from the land-cover types forest, wetland, and Alpine environment were evaluated
to assess the feasibility of supporting the monitoring of climate change impacts.
In those test cases with a validation of the classification results, the accuracy is
higher than 80 %. Given the complexity of the target classes, this result can be
accepted as a basis for the further derivation of the conservation status of classes.
Generally, comparison with future image acquisitions for the evaluation of changes
is possible. However, these changes might have causes other than pure (and often
very gradually occurring) climate change. Variations in market prices of timber or
crops may influence usage intensity, as may the subsidy schemes of the European
Union or the changing touristic utilisation of an area. It is not possible to distinguish
anthropogenic land-use changes from those induced by climate change by means of
the methods discussed.
The results were achieved using multi-temporal RapidEye imagery. At least two
scenes per year were available for the presented studies. The advantage of utilising
several pieces of information from the phenological cycle was stated in all studies,
as well as the necessity of working with very high spatial resolution imagery
(below 10 m).
7 Remote Sensing-Based Monitoring of Potential Climate-Induced Impacts on Habitats
109