The largest pressure on habitats in Alpine areas is land-use. This is true despite
land-use activities being limited within the Nature Park due to conservation
restrictions. Pressures arise mostly from extensive forestry, agriculture (grasslands with livestock breeding and pasture farming), tourism, and traffic. In this
study we investigated the following potential impacts for the study area
Rieserferner Ahrn:
• increase in dwarf shrub cover,
• change in tree line,
• new vegetation on rocks and the glacier forefields,
• changes in water regime and intra-annual and inter-annual dynamics,
• changes in phenology and its intra-annual and inter-annual dynamics (Zebisch
et al. 2010).
7.4.3 Data and Methods
For the study area four RapidEye images (Level 1B) with the acquisition dates
22-07-2009, 29-07-2009, 03-10-2009 and 31-07-2010 were available. The following auxiliary data sets were used:
• a colour aerial orthophoto acquired in 2006 with a spatial resolution of 0.5 m,
• a Digital Elevation Model (DEM) with a spatial resolution of 2.5 m,
• solar radiation layers – from RapidEye images using metadata and DEM,
• texture layers: texture features (Haralick et al. 1973) such as mean, variance,
homogeneity, contrast, dissimilarity, entropy, second angular moment and correlation features were generated from the orthophoto,
• detailed habitat thematic map: field mapping 2006 as well as photointerpretation
and digitalisation of orthophotos by experts.
Initially the RapidEye images were orthorectified (Toutin 2003) and the pixel
values converted to reflectance at top of the atmosphere (TOA). In the latter step
only distance to the sun and the geometry of the incoming solar radiation was
considered. Next we masked out clouds and shadowed areas in the images using
object-based image analysis. Using Definiens eCognition software the images were
first segmented and classified into two levels to map clouds and shadows based on
object statistics, topological and shape object’s features. The mapping results of the
two classification levels were then merged. The classification was further improved
by modifications of the object’s shapes using appropriate features of classified
objects. Subsequently, training as well as validation samples of the different
vegetation types were derived following a random stratified sampling approach
based on thematically homogeneous areas. A minimum of 50 samples were taken
from twelve vegetation types present in the study area. The SVM classification
7 Remote Sensing-Based Monitoring of Potential Climate-Induced Impacts on Habitats
105
land-use activities being limited within the Nature Park due to conservation
restrictions. Pressures arise mostly from extensive forestry, agriculture (grasslands with livestock breeding and pasture farming), tourism, and traffic. In this
study we investigated the following potential impacts for the study area
Rieserferner Ahrn:
• increase in dwarf shrub cover,
• change in tree line,
• new vegetation on rocks and the glacier forefields,
• changes in water regime and intra-annual and inter-annual dynamics,
• changes in phenology and its intra-annual and inter-annual dynamics (Zebisch
et al. 2010).
7.4.3 Data and Methods
For the study area four RapidEye images (Level 1B) with the acquisition dates
22-07-2009, 29-07-2009, 03-10-2009 and 31-07-2010 were available. The following auxiliary data sets were used:
• a colour aerial orthophoto acquired in 2006 with a spatial resolution of 0.5 m,
• a Digital Elevation Model (DEM) with a spatial resolution of 2.5 m,
• solar radiation layers – from RapidEye images using metadata and DEM,
• texture layers: texture features (Haralick et al. 1973) such as mean, variance,
homogeneity, contrast, dissimilarity, entropy, second angular moment and correlation features were generated from the orthophoto,
• detailed habitat thematic map: field mapping 2006 as well as photointerpretation
and digitalisation of orthophotos by experts.
Initially the RapidEye images were orthorectified (Toutin 2003) and the pixel
values converted to reflectance at top of the atmosphere (TOA). In the latter step
only distance to the sun and the geometry of the incoming solar radiation was
considered. Next we masked out clouds and shadowed areas in the images using
object-based image analysis. Using Definiens eCognition software the images were
first segmented and classified into two levels to map clouds and shadows based on
object statistics, topological and shape object’s features. The mapping results of the
two classification levels were then merged. The classification was further improved
by modifications of the object’s shapes using appropriate features of classified
objects. Subsequently, training as well as validation samples of the different
vegetation types were derived following a random stratified sampling approach
based on thematically homogeneous areas. A minimum of 50 samples were taken
from twelve vegetation types present in the study area. The SVM classification
7 Remote Sensing-Based Monitoring of Potential Climate-Induced Impacts on Habitats
105