Kamps, M.

Rutzinger, M.

Seijmonsbergen, A.C.

Zieher, T.

English

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Assessment of the interaction of land-cover change on shallow landslide occurrence using an
automated object-based approach
Kamps, M.; Seijmonsbergen, A.C.; Rutzinger, M.; Zieher, T.
Published in:
GEOBIA 2016 : Solutions and synergies
DOI:
10.3990/2.433
Link to publication
Citation for published version (APA):
Kamps, M., Seijmonsbergen, A. C., Rutzinger, M., & Zieher, T. (2016). Assessment of the interaction of land-
cover change on shallow landslide occurrence using an automated object-based approach. In N. Kerle, M.
Gerke, & S. Lefevre (Eds.), GEOBIA 2016 : Solutions and synergies: 14 September 2016-16 September 2016,
University of Twente, Faculty of Geo-Information and Earth Observation (ITC) : proceedings University of
Twente. https://doi.org/10.3990/2.433
General rights
It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s),
other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons).
Disclaimer/Complaints regulations
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating
your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask
the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,
The Netherlands. You will be contacted as soon as possible.
Download date: 09 Feb 2021
Assessment of the interaction of land-cover change on shallow landslide occurrence using an 
automated object-based approach 
 
 
M. Kamps a, A.C. Seijmonsbergen a, M. Rutzingerbc and T. Zieherbc 
 
aUniversity of Amsterdam – Institute for Biodiversity and Ecosystem Dynamics (IBED) - martijntkamps@gmail.com – 
a.c.seijmonsbergen@uva.nl 
b University Innsbruck - Institute for Geography 
cAustrian Academy of Sciences - Institute of Interdisciplinary Mountain Research (IGF) - martin.rutzinger@oeaw.ac.at - 
thomas.zieher@uibk.ac.at 
 
 
KEY WORDS: Data Synergy, OBIA, Landslide, LiDAR, Orthophotos, Land Cover Change 
 
ABSTRACT: Land-cover change could considerably lower landslide triggering rainfall thresholds allowing precipitation events 
with shorter recurrence intervals to initiate shallow landslides. This research focusses on developing an automated, robust and up-
scalable workflow to quantitatively assess the effect land-cover change has on initiating rainfall induced shallow landslides in the 
Laternser Valley. Land-cover is classified using four sets of high resolution orthophotos (198x, 2001, 2006, 2009; 0.25 m spatial 
resolution) by applying an object-based approach with eCognition software. The correlation between land-cover change and 
landslide occurrence was assessed by analyzing land-cover change trends in the vicinity (< 25 meters) of mapped shallow landslides. 
The obtained classification accuracy ranges from 76% for 198x to 88% for 2009. The relative area undergoing land-cover change is 
18% in the whole Laternser valley and 34% in the vicinity of landslides. Overall land-cover change trends indicate a shift from 
grassland to forest in the whole Laternser valley. However, in the vicinity of landslides the opposite is observed, namely a shift from 
forest to grassland and grassland to bare soil. Even though a general vegetation reduction is detected in the vicinity of landslides no 
correlation between LCC and landslide occurrence could be established yet.  
 
 
1. INTRODUCTION 
Shallow landslides are geomorphological processes occurring in 
mountainous regions. Shallow landslide susceptibility depends 
on several factors such as topography, changing land cover and 
characteristics of subsurface material. Detailed knowledge of 
fine-scale past and present land cover change (LCC) is a 
prerequisite for modelling and analysing landslide 
susceptibility. Classification of high resolution orthophotos - as 
a transparent and transferable method - is well suited to 
efficiently and accurately document LCC and to investigate the 
relation with known landslide-triggering rainfall events. The 
aims of this research are:   
 
(1). To develop a workflow for automated object-based LCC 
detection using high resolution orthophotos in the Laternser 
valley, Austria. 
(2). To analyse the correlation between LCC and the temporal 
occurrence of rainfall triggered shallow landslides. 
 
2. STUDY AREA  
The Laternser valley is located in the Eastern Alps in 
Vorarlberg, the westernmost province of Austria. Vorarlberg is 
a mountainous province characterized by large topographic 
differences and heterogeneous land-cover. The Laternser valley 
is situated between 47.23-47.31°N and 9.66-9.84°E and 
comprises of an area of 52.1 km² (Figure 1). Approximately 
68% of the total area has an inclination between 20 and 50 
degrees making it generally susceptible to landslides. The 
geological setting of Vorarlberg was formed by succession of 
calcareous, dolomitic and pelitic sediments originating from the 
Mesozoic era (Friebe, 2007). Vorarlberg has a typical Alpine 
geomorphology characterized by subglacial till, moraine ridges,
  
  
 
Figure 1. Location of the Laternser Valley in Vorarlberg, 
western Austria. 
 
 
ice-marginal landform deposits and cirques (Seijmonsbergen et 
al., 2014). The Laternser valley is dominated by quaternary, 
penninic and helvetic nappes (Friebe, 2007) and is drained by a 
tributary of the Rhine, the river Frutz.  
 
3. METHODS 
3.1 Data 
Table 1 shows the meta data of the multi-temporal orthophotos 
utilized in this research.  
 
Orthophoto 
series 
Photo 
scale 
GSM 
[m] 
Radiometric 
resolution 
Bands 
198x 10.000-
41.000 
0.2 8 bit CIR 
2001 10.000- 
15.000  
0.25 8 bit CIR 
2006 10.000-
12.000 
0.125 8 bit RGB 
2009 10.000-
15.000 
0.125 8 bit RGB 
 
 
 
Table 1. Meta data of the high resolution orthophoto series. 
GSD: ground sampling distance, CIR: color infrared imagery, 
RGB: Red, green, blue; true color imagery. Adopted from 
Zieher et al., 2016. 
 
A well-documented field- and orthophoto-based inventory of 
shallow landslides is available for the Laternser Valley in 
Vorarlberg, developed as part of the project Climate Induced 
System Status Changes at Slopes and Their Impact on Shallow 
Landslide Susceptibility (C3S-ISLS). Most of those registered 
landslides relate to the 1999 and 2005 rainfall events. Figure 2 
shows the mapped landslides in three time periods that were 
analysed in this research. This multi-annual landslide inventory 
with a 1:400 nominal scale is used as reference dataset for the 
location of shallow landslides. 
 
 
 
Figure 2. Mapped shallow landslides in the Laternser valley.  
 
 
3.2   Workflow  
Figure 3 shows a schematic overview of the general workflow 
of this research. Four steps are included: pre-processing, 
analysis, accuracy assessment and the deliverables to work 
towards the two aims: land-cover change maps and assessing 
the correlation between LCC and shallow landslide occurrence.
   
 
 
 
 
Figure 3. Schematic overview of the workflow. 
 
 
3.3      Pre-processing and software 
 
Pre-processing of the data was performed using ArcGIS 10.3.1 
and QGIS 2.4.0. eCognition Developer 9.1.2 software was used 
to develop the LCC maps. Pre-processing consists of 
resampling to 0.25x0.25 m and tiling the orthophotos to tiles of 
2750 m edge length including an overlap area of 10% to ensure 
minimization of object segmentation edge effects. 
 
3.4     Land Cover Change Analysis 
 
Our workflow in eCognition Developer for LCC combines 
segmentation and object-based image analysis of four sets of 
high resolution orthophotos. Subsequently, post-classification 
change detection using image differencing was utilized. 
 
Multiscale multiresolution segmentation was utilized to obtain 
image objects of every land-cover class throughout the whole 
study area. Scale parameters for multiresolution segmentation 
were determined with the estimation of scale parameter tool 
(ESP2) developed by Drăguţ et al. (2014). 
 
The class hierarchy used in this research is: forest, grassland, 
shadow, bare soil and infrastructure. 
To classify the land-cover classes, supervised feature space 
optimization (FSO) is used to determine the parameter 
combinations with the highest separation distance (Platt & 
Rapoza, 2008). 250 representative (based on expert knowledge) 
samples of every land-cover class are used by the FSO 
algorithm on 48 distinct features. 
 
Classification consisted of two steps: (1) stratified membership 
classification and (2) contextual improvements. The 
classification workflow was developed and optimized in one tile 
and subsequently upscaled to the whole of Laternser valley for 
analysis and validation. A minimum mapping unit of 3.125 m² 
(50 pixels) was utilized. 
 
The stratified membership classification was based on the 
feature thresholds derived from the FSO. The stratified 
membership classification workflow was designed to be 
conservative in order to minimize false positives. The 
contextual improvements were used to classify remaining false 
negatives. 
 
The contextual improvement rationale was developed to 
account for landscape homogeneity, variability and overlap in 
the feature space. The contextual improvements were based on 
expert interpretation of false positive and false negatives in 
combination with the original feature space parameters of the 
objects. Every rule in the workflow only removed a small 
portion of omission and commission errors. 
 
The land-cover change analysis is performed using post-
classification image differencing by automatic comparison of 
image sub-object hierarchies (Gutierrez et al., 2012; Zhou et al., 
2008). 
 
Derivation of LCC trends is utilized for interpretation of LCC in 
the vicinity (<25m) of shallow landslides to quantitatively 
assess the effect of LCC on rainfall induced landslide 
occurrence (Begueria, 2006). An automated ArcGIS model is 
used to calculate the area per land-cover change class using a 25 
[m] buffer around the scarpoints derived from the C3S-ISLS 
project. 
 
 
3.5  Accuracy Assessment 
 
The accuracy assessment consists of two parts: (1) standard 
confusion matrix, (2) classification stability.   
  
 
A stratified sampling scheme was used to obtain 150 samples 
per period based on the relative occurrence of land-cover 
classes and the entire range of membership probabilities to 
obtain a representative sample. Additionally, 150 samples in the 
vicinity (<25 m) of landslides were specified to compare the 
standard overall accuracy with the landslide specific 
application. 
 
In this study the classification stability was used to differentiate 
between the segments correctly classified by the initial 
membership-based classification and the contextual 
improvements. 
 
 
4. RESULTS 
4.1 Land Cover Change Analysis 
Classification accuracies range from 76% (198x) to 88% for the 
2009 orthophotos. Kappa statistic falls between 64% (198x) to 
81% in 2009. The context based classification ruleset improved 
52% of all accurately classified samples in 198x and decreases 
over time to 39% in 2009 due to better image quality and 
reduced overlap in the feature space in the more recent years. 
This research shows the effectiveness of utilizing context to 
improve classification accuracy after initial membership-based 
classification in an object-based workflow to deal with overlap 
in the feature space. 
 
Figure 4 shows the results of the LCC between 198x and 2009.  
 
Figure 4. LCC results 198x-2009. 
 
Figure 5 shows the relative area change for every land-cover 
class from 198x to 2009 in the whole Laternser Valley. A 
general increase in forest is observed while grassland decreases. 
Approximately 18% of the total area undergoes LCC between 
198x and 2009. The biggest LCC is the shift from grassland to 
forest attributing to 10.5% of the area and 58% of total LCC. 
This LCC shift occurs mainly at open areas within the canopy 
and at the edges of the tree-line. 
 
 
Figure 5. Relative land cover change for the four classes per 
orthophoto series in the whole Laternser valley. Error bars are 
derived from the average user/producer accuracy per land-cover 
class. 
 
 
4.2    Change Detection and Landslides 
 
Land-cover changes that occur in the vicinity (<25 m) of 
landslides are shown in figure 6. Land-cover changes account 
for 33.9% (198x-2009) within the 25 m buffer area. The land-
cover changes that occur the most are: a shift from grassland to 
bare soil / infrastructure, and a shift from forest to grassland. 
 
 
Figure 6. Relative land cover change for the four classes per 
orthophoto series in the vicinity of landslides. Error bars are 
derived from the average user/producer accuracy per land-cover 
class. 
 
5. CONCLUSION 
In the Laternser valley 18% of the total area undergoes LCC 
while in the vicinity of landslides LCC occurs on 34%. 
Although overall LCC trends indicate a shift from grassland to 
forest for the Laternser valley, the opposite is observed near 
shallow landslides. So far, a clear correlation between LCC and 
landslide occurrence could not be established. The question 
remains whether the observed land-cover change actually 
caused the landslides or was the eventual effect of the 
landslides. Further analysis will incorporate topographic 
LiDAR data to optimize segmentation and classification and 
integrate additional explanatory variables for shallow landslide 
susceptibility such as slope angle, aspect and curvature. 
Additional ancillary data such as lithology would contribute to 
strengthening the analysis. 
 
 
ACKNOWLEDGEMENTS 
We thank the Federal State of Vorarlberg for providing the 
necessary data. This work has been conducted within the project 
C3S-ISLS, which is funded by the Austrian Climate and Energy 
Fund, 5th ACRP Program. 
 
REFERENCES 
Aguirre-Gutiérrez, J., Seijmonsbergen, A. C., & 
Duivenvoorden, J. F. (2012). Optimizing land cover 
classification accuracy for change detection, a combined pixel-
based and object-based approach in a mountainous area in 
Mexico. Applied Geography, 34, 29-37. 
 
Beguería, S. (2006). Changes in land cover and shallow 
landslide activity: a case study in the Spanish 
Pyrenees. Geomorphology, 74(1), 196-206. 
 
Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). 
Automated parameterisation for multi-scale image segmentation 
on multiple layers. ISPRS Journal of Photogrammetry and 
Remote Sensing, 88, 119-127. 
 
Friebe, J. (2007). Geologie der österreichischen bundesländer 
vorarlberg. Geologische Bundesanstalt, Wien, 154. 
 
Platt, R. V., & Rapoza, L. (2008). An evaluation of an object-
oriented paradigm for land Use/Land cover classification. The 
Professional Geographer, 60(1), 87-100. 
 
Seijmonsbergen, A. C., De Jong, M. G., de Graaff, L. W., & 
Anders, N. S. (2014). Geodiversität von vorarlberg und 
liechtenstein-geodiversity of vorarlberg and liechtenstein. 
Haupt Verlag AG. 
 
Zieher, T.; Perzl, F.; Rössel, M.; Rutzinger, M.; Meißl, G.; 
Markart, G. & Geitner, C. (2016), 'A multi-annual landslide 
inventory for the assessment of shallow landslide susceptibility 
- Two test cases in Vorarlberg, Austria', Geomorphology  259, 
40 - 54. 
 
Zhou, W., Troy, A., & Grove, M. (2008). Object-based land 
cover classification and change analysis in the baltimore 
metropolitan area using multitemporal high resolution remote 
sensing data. Sensors, 8(3), 1613-1636. 


Assessment of the interaction of land-cover change on shallow landslide occurrence using an automated object-based approach

M. Kamps

A.C. Seijmonsbergen

Martin Rutzinger

T. Zieher

Crossref

Assessing of the interaction of land-cover change on shallow landslide occurrence using an automated object-based approach

Land-cover change could considerably lower landslide triggering rainfall thresholds allowing precipitation events with shorter recurrence intervals to initiate shallow landslides. This research focusses on developing an automated, robust and up-scalable workflow to quantitatively assess the effect land-cover change has on initiating rainfall induced shallow landslides in the Laternser Valley. Land-cover is classified using four sets of high resolution orthophotos (198x, 2001, 2006, 2009; 0.25 m spatial resolution) by applying an object-based approach with eCognition software. The correlation between land-cover change and landslide occurrence was assessed by analyzing land-cover change trends in the vicinity (&lt; 25 meters) of mapped shallow landslides. The obtained classification accuracy ranges from 76% for 198x to 88% for 2009. The relative area undergoing land-cover change is 18% in the whole Laternser valley and 34% in the vicinity of landslides. Overall land-cover change trends indicate a shift from grassland to forest in the whole Laternser valley. However, in the vicinity of landslides the opposite is observed, namely a shift from forest to grassland and grassland to bare soil. Even though a general vegetation reduction is detected in the vicinity of landslides no correlation between LCC and landslide occurrence could be established yet

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UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Assessment of the interaction of land-cover change on shallow landslideoccurrence using an automated object-based approachKamps, M.; Seijmonsbergen, A.C.; Rutzinger, M.; Zieher, T.DOI10.3990/2.433Publication date2016Document VersionFinal published versionPublished inGEOBIA 2016 : Solutions and synergiesLicenseArticle 25fa Dutch Copyright ActLink to publicationCitation for published version (APA):Kamps, M., Seijmonsbergen, A. C., Rutzinger, M., & Zieher, T. (2016). Assessment of theinteraction of land-cover change on shallow landslide occurrence using an automated object-based approach. In N. Kerle, M. Gerke, & S. Lefevre (Eds.), GEOBIA 2016 : Solutions andsynergies: 14 September 2016-16 September 2016, University of Twente, Faculty of Geo-Information and Earth Observation (ITC) : proceedings University of Twente.https://doi.org/10.3990/2.433General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:11 Nov 2022Assessment of the interaction of land-cover change on shallow landslide occurrence using an automated object-based approach   M. Kamps a, A.C. Seijmonsbergen a, M. Rutzingerbc and T. Zieherbc  aUniversity of Amsterdam – Institute for Biodiversity and Ecosystem Dynamics (IBED) - martijntkamps@gmail.com – a.c.seijmonsbergen@uva.nl b University Innsbruck - Institute for Geography cAustrian Academy of Sciences - Institute of Interdisciplinary Mountain Research (IGF) - martin.rutzinger@oeaw.ac.at - thomas.zieher@uibk.ac.at   KEY WORDS: Data Synergy, OBIA, Landslide, LiDAR, Orthophotos, Land Cover Change  ABSTRACT: Land-cover change could considerably lower landslide triggering rainfall thresholds allowing precipitation events with shorter recurrence intervals to initiate shallow landslides. This research focusses on developing an automated, robust and up-scalable workflow to quantitatively assess the effect land-cover change has on initiating rainfall induced shallow landslides in the Laternser Valley. Land-cover is classified using four sets of high resolution orthophotos (198x, 2001, 2006, 2009; 0.25 m spatial resolution) by applying an object-based approach with eCognition software. The correlation between land-cover change and landslide occurrence was assessed by analyzing land-cover change trends in the vicinity (< 25 meters) of mapped shallow landslides. The obtained classification accuracy ranges from 76% for 198x to 88% for 2009. The relative area undergoing land-cover change is 18% in the whole Laternser valley and 34% in the vicinity of landslides. Overall land-cover change trends indicate a shift from grassland to forest in the whole Laternser valley. However, in the vicinity of landslides the opposite is observed, namely a shift from forest to grassland and grassland to bare soil. Even though a general vegetation reduction is detected in the vicinity of landslides no correlation between LCC and landslide occurrence could be established yet.    1. INTRODUCTION Shallow landslides are geomorphological processes occurring in mountainous regions. Shallow landslide susceptibility depends on several factors such as topography, changing land cover and characteristics of subsurface material. Detailed knowledge of fine-scale past and present land cover change (LCC) is a prerequisite for modelling and analysing landslide susceptibility. Classification of high resolution orthophotos - as a transparent and transferable method - is well suited to efficiently and accurately document LCC and to investigate the relation with known landslide-triggering rainfall events. The aims of this research are:    (1). To develop a workflow for automated object-based LCC detection using high resolution orthophotos in the Laternser valley, Austria. (2). To analyse the correlation between LCC and the temporal occurrence of rainfall triggered shallow landslides.  2. STUDY AREA  The Laternser valley is located in the Eastern Alps in Vorarlberg, the westernmost province of Austria. Vorarlberg is a mountainous province characterized by large topographic differences and heterogeneous land-cover. The Laternser valley is situated between 47.23-47.31°N and 9.66-9.84°E and comprises of an area of 52.1 km² (Figure 1). Approximately 68% of the total area has an inclination between 20 and 50 degrees making it generally susceptible to landslides. The geological setting of Vorarlberg was formed by succession of calcareous, dolomitic and pelitic sediments originating from the Mesozoic era (Friebe, 2007). Vorarlberg has a typical Alpine geomorphology characterized by subglacial till, moraine ridges,     Figure 1. Location of the Laternser Valley in Vorarlberg, western Austria.   ice-marginal landform deposits and cirques (Seijmonsbergen et al., 2014). The Laternser valley is dominated by quaternary, penninic and helvetic nappes (Friebe, 2007) and is drained by a tributary of the Rhine, the river Frutz.   3. METHODS 3.1 Data Table 1 shows the meta data of the multi-temporal orthophotos utilized in this research.   Orthophoto series Photo scale GSM [m] Radiometric resolution Bands 198x 10.000-41.000 0.2 8 bit CIR 2001 10.000- 15.000  0.25 8 bit CIR 2006 10.000-12.000 0.125 8 bit RGB 2009 10.000-15.000 0.125 8 bit RGB    Table 1. Meta data of the high resolution orthophoto series. GSD: ground sampling distance, CIR: color infrared imagery, RGB: Red, green, blue; true color imagery. Adopted from Zieher et al., 2016.  A well-documented field- and orthophoto-based inventory of shallow landslides is available for the Laternser Valley in Vorarlberg, developed as part of the project Climate Induced System Status Changes at Slopes and Their Impact on Shallow Landslide Susceptibility (C3S-ISLS). Most of those registered landslides relate to the 1999 and 2005 rainfall events. Figure 2 shows the mapped landslides in three time periods that were analysed in this research. This multi-annual landslide inventory with a 1:400 nominal scale is used as reference dataset for the location of shallow landslides.    Figure 2. Mapped shallow landslides in the Laternser valley.    3.2   Workflow  Figure 3 shows a schematic overview of the general workflow of this research. Four steps are included: pre-processing, analysis, accuracy assessment and the deliverables to work towards the two aims: land-cover change maps and assessing the correlation between LCC and shallow landslide occurrence.       Figure 3. Schematic overview of the workflow.   3.3      Pre-processing and software  Pre-processing of the data was performed using ArcGIS 10.3.1 and QGIS 2.4.0. eCognition Developer 9.1.2 software was used to develop the LCC maps. Pre-processing consists of resampling to 0.25x0.25 m and tiling the orthophotos to tiles of 2750 m edge length including an overlap area of 10% to ensure minimization of object segmentation edge effects.  3.4     Land Cover Change Analysis  Our workflow in eCognition Developer for LCC combines segmentation and object-based image analysis of four sets of high resolution orthophotos. Subsequently, post-classification change detection using image differencing was utilized.  Multiscale multiresolution segmentation was utilized to obtain image objects of every land-cover class throughout the whole study area. Scale parameters for multiresolution segmentation were determined with the estimation of scale parameter tool (ESP2) developed by Drăguţ et al. (2014).  The class hierarchy used in this research is: forest, grassland, shadow, bare soil and infrastructure. To classify the land-cover classes, supervised feature space optimization (FSO) is used to determine the parameter combinations with the highest separation distance (Platt & Rapoza, 2008). 250 representative (based on expert knowledge) samples of every land-cover class are used by the FSO algorithm on 48 distinct features.  Classification consisted of two steps: (1) stratified membership classification and (2) contextual improvements. The classification workflow was developed and optimized in one tile and subsequently upscaled to the whole of Laternser valley for analysis and validation. A minimum mapping unit of 3.125 m² (50 pixels) was utilized.  The stratified membership classification was based on the feature thresholds derived from the FSO. The stratified membership classification workflow was designed to be conservative in order to minimize false positives. The contextual improvements were used to classify remaining false negatives.  The contextual improvement rationale was developed to account for landscape homogeneity, variability and overlap in the feature space. The contextual improvements were based on expert interpretation of false positive and false negatives in combination with the original feature space parameters of the objects. Every rule in the workflow only removed a small portion of omission and commission errors.  The land-cover change analysis is performed using post-classification image differencing by automatic comparison of image sub-object hierarchies (Gutierrez et al., 2012; Zhou et al., 2008).  Derivation of LCC trends is utilized for interpretation of LCC in the vicinity (<25m) of shallow landslides to quantitatively assess the effect of LCC on rainfall induced landslide occurrence (Begueria, 2006). An automated ArcGIS model is used to calculate the area per land-cover change class using a 25 [m] buffer around the scarpoints derived from the C3S-ISLS project.   3.5  Accuracy Assessment  The accuracy assessment consists of two parts: (1) standard confusion matrix, (2) classification stability.      A stratified sampling scheme was used to obtain 150 samples per period based on the relative occurrence of land-cover classes and the entire range of membership probabilities to obtain a representative sample. Additionally, 150 samples in the vicinity (<25 m) of landslides were specified to compare the standard overall accuracy with the landslide specific application.  In this study the classification stability was used to differentiate between the segments correctly classified by the initial membership-based classification and the contextual improvements.   4. RESULTS 4.1 Land Cover Change Analysis Classification accuracies range from 76% (198x) to 88% for the 2009 orthophotos. Kappa statistic falls between 64% (198x) to 81% in 2009. The context based classification ruleset improved 52% of all accurately classified samples in 198x and decreases over time to 39% in 2009 due to better image quality and reduced overlap in the feature space in the more recent years. This research shows the effectiveness of utilizing context to improve classification accuracy after initial membership-based classification in an object-based workflow to deal with overlap in the feature space.  Figure 4 shows the results of the LCC between 198x and 2009.   Figure 4. LCC results 198x-2009.  Figure 5 shows the relative area change for every land-cover class from 198x to 2009 in the whole Laternser Valley. A general increase in forest is observed while grassland decreases. Approximately 18% of the total area undergoes LCC between 198x and 2009. The biggest LCC is the shift from grassland to forest attributing to 10.5% of the area and 58% of total LCC. This LCC shift occurs mainly at open areas within the canopy and at the edges of the tree-line.   Figure 5. Relative land cover change for the four classes per orthophoto series in the whole Laternser valley. Error bars are derived from the average user/producer accuracy per land-cover class.   4.2    Change Detection and Landslides  Land-cover changes that occur in the vicinity (<25 m) of landslides are shown in figure 6. Land-cover changes account for 33.9% (198x-2009) within the 25 m buffer area. The land-cover changes that occur the most are: a shift from grassland to bare soil / infrastructure, and a shift from forest to grassland.   Figure 6. Relative land cover change for the four classes per orthophoto series in the vicinity of landslides. Error bars are derived from the average user/producer accuracy per land-cover class.  5. CONCLUSION In the Laternser valley 18% of the total area undergoes LCC while in the vicinity of landslides LCC occurs on 34%. Although overall LCC trends indicate a shift from grassland to forest for the Laternser valley, the opposite is observed near shallow landslides. So far, a clear correlation between LCC and landslide occurrence could not be established. The question remains whether the observed land-cover change actually caused the landslides or was the eventual effect of the landslides. Further analysis will incorporate topographic LiDAR data to optimize segmentation and classification and integrate additional explanatory variables for shallow landslide susceptibility such as slope angle, aspect and curvature. Additional ancillary data such as lithology would contribute to strengthening the analysis.   ACKNOWLEDGEMENTS We thank the Federal State of Vorarlberg for providing the necessary data. This work has been conducted within the project C3S-ISLS, which is funded by the Austrian Climate and Energy Fund, 5th ACRP Program.  REFERENCES Aguirre-Gutiérrez, J., Seijmonsbergen, A. C., & Duivenvoorden, J. F. (2012). Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography, 34, 29-37.  Beguería, S. (2006). Changes in land cover and shallow landslide activity: a case study in the Spanish Pyrenees. Geomorphology, 74(1), 196-206.  Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 119-127.  Friebe, J. (2007). Geologie der österreichischen bundesländer vorarlberg. Geologische Bundesanstalt, Wien, 154.  Platt, R. V., & Rapoza, L. (2008). An evaluation of an object-oriented paradigm for land Use/Land cover classification. The Professional Geographer, 60(1), 87-100.  Seijmonsbergen, A. C., De Jong, M. G., de Graaff, L. W., & Anders, N. S. (2014). Geodiversität von vorarlberg und liechtenstein-geodiversity of vorarlberg and liechtenstein. Haupt Verlag AG.  Zieher, T.; Perzl, F.; Rössel, M.; Rutzinger, M.; Meißl, G.; Markart, G. & Geitner, C. (2016), 'A multi-annual landslide inventory for the assessment of shallow landslide susceptibility - Two test cases in Vorarlberg, Austria', Geomorphology  259, 40 - 54.  Zhou, W., Troy, A., & Grove, M. (2008). Object-based land cover classification and change analysis in the baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8(3), 1613-1636. 

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Assessment of the interaction of land-cover change on shallow landslideoccurrence using an automated object-based approachKamps, M.; Seijmonsbergen, A.C.; Rutzinger, M.; Zieher, T.DOI10.3990/2.433Publication date2016Document VersionFinal published versionPublished inGEOBIA 2016 : Solutions and synergiesLicenseArticle 25fa Dutch Copyright Act (https://www.openaccess.nl/en/in-the-netherlands/you-share-we-take-care)Link to publicationCitation for published version (APA):Kamps, M., Seijmonsbergen, A. C., Rutzinger, M., & Zieher, T. (2016). Assessment of theinteraction of land-cover change on shallow landslide occurrence using an automated object-based approach. In N. Kerle, M. Gerke, & S. Lefevre (Eds.), GEOBIA 2016 : Solutions andsynergies: 14 September 2016-16 September 2016, University of Twente, Faculty of Geo-Information and Earth Observation (ITC) : proceedings University of Twente.https://doi.org/10.3990/2.433General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:31 Aug 2023Assessment of the interaction of land-cover change on shallow landslide occurrence using an automated object-based approach   M. Kamps a, A.C. Seijmonsbergen a, M. Rutzingerbc and T. Zieherbc  aUniversity of Amsterdam – Institute for Biodiversity and Ecosystem Dynamics (IBED) - martijntkamps@gmail.com – a.c.seijmonsbergen@uva.nl b University Innsbruck - Institute for Geography cAustrian Academy of Sciences - Institute of Interdisciplinary Mountain Research (IGF) - martin.rutzinger@oeaw.ac.at - thomas.zieher@uibk.ac.at   KEY WORDS: Data Synergy, OBIA, Landslide, LiDAR, Orthophotos, Land Cover Change  ABSTRACT: Land-cover change could considerably lower landslide triggering rainfall thresholds allowing precipitation events with shorter recurrence intervals to initiate shallow landslides. This research focusses on developing an automated, robust and up-scalable workflow to quantitatively assess the effect land-cover change has on initiating rainfall induced shallow landslides in the Laternser Valley. Land-cover is classified using four sets of high resolution orthophotos (198x, 2001, 2006, 2009; 0.25 m spatial resolution) by applying an object-based approach with eCognition software. The correlation between land-cover change and landslide occurrence was assessed by analyzing land-cover change trends in the vicinity (< 25 meters) of mapped shallow landslides. The obtained classification accuracy ranges from 76% for 198x to 88% for 2009. The relative area undergoing land-cover change is 18% in the whole Laternser valley and 34% in the vicinity of landslides. Overall land-cover change trends indicate a shift from grassland to forest in the whole Laternser valley. However, in the vicinity of landslides the opposite is observed, namely a shift from forest to grassland and grassland to bare soil. Even though a general vegetation reduction is detected in the vicinity of landslides no correlation between LCC and landslide occurrence could be established yet.    1. INTRODUCTION Shallow landslides are geomorphological processes occurring in mountainous regions. Shallow landslide susceptibility depends on several factors such as topography, changing land cover and characteristics of subsurface material. Detailed knowledge of fine-scale past and present land cover change (LCC) is a prerequisite for modelling and analysing landslide susceptibility. Classification of high resolution orthophotos - as a transparent and transferable method - is well suited to efficiently and accurately document LCC and to investigate the relation with known landslide-triggering rainfall events. The aims of this research are:    (1). To develop a workflow for automated object-based LCC detection using high resolution orthophotos in the Laternser valley, Austria. (2). To analyse the correlation between LCC and the temporal occurrence of rainfall triggered shallow landslides.  2. STUDY AREA  The Laternser valley is located in the Eastern Alps in Vorarlberg, the westernmost province of Austria. Vorarlberg is a mountainous province characterized by large topographic differences and heterogeneous land-cover. The Laternser valley is situated between 47.23-47.31°N and 9.66-9.84°E and comprises of an area of 52.1 km² (Figure 1). Approximately 68% of the total area has an inclination between 20 and 50 degrees making it generally susceptible to landslides. The geological setting of Vorarlberg was formed by succession of calcareous, dolomitic and pelitic sediments originating from the Mesozoic era (Friebe, 2007). Vorarlberg has a typical Alpine geomorphology characterized by subglacial till, moraine ridges,     Figure 1. Location of the Laternser Valley in Vorarlberg, western Austria.   ice-marginal landform deposits and cirques (Seijmonsbergen et al., 2014). The Laternser valley is dominated by quaternary, penninic and helvetic nappes (Friebe, 2007) and is drained by a tributary of the Rhine, the river Frutz.   3. METHODS 3.1 Data Table 1 shows the meta data of the multi-temporal orthophotos utilized in this research.   Orthophoto series Photo scale GSM [m] Radiometric resolution Bands 198x 10.000-41.000 0.2 8 bit CIR 2001 10.000- 15.000  0.25 8 bit CIR 2006 10.000-12.000 0.125 8 bit RGB 2009 10.000-15.000 0.125 8 bit RGB    Table 1. Meta data of the high resolution orthophoto series. GSD: ground sampling distance, CIR: color infrared imagery, RGB: Red, green, blue; true color imagery. Adopted from Zieher et al., 2016.  A well-documented field- and orthophoto-based inventory of shallow landslides is available for the Laternser Valley in Vorarlberg, developed as part of the project Climate Induced System Status Changes at Slopes and Their Impact on Shallow Landslide Susceptibility (C3S-ISLS). Most of those registered landslides relate to the 1999 and 2005 rainfall events. Figure 2 shows the mapped landslides in three time periods that were analysed in this research. This multi-annual landslide inventory with a 1:400 nominal scale is used as reference dataset for the location of shallow landslides.    Figure 2. Mapped shallow landslides in the Laternser valley.    3.2   Workflow  Figure 3 shows a schematic overview of the general workflow of this research. Four steps are included: pre-processing, analysis, accuracy assessment and the deliverables to work towards the two aims: land-cover change maps and assessing the correlation between LCC and shallow landslide occurrence.       Figure 3. Schematic overview of the workflow.   3.3      Pre-processing and software  Pre-processing of the data was performed using ArcGIS 10.3.1 and QGIS 2.4.0. eCognition Developer 9.1.2 software was used to develop the LCC maps. Pre-processing consists of resampling to 0.25x0.25 m and tiling the orthophotos to tiles of 2750 m edge length including an overlap area of 10% to ensure minimization of object segmentation edge effects.  3.4     Land Cover Change Analysis  Our workflow in eCognition Developer for LCC combines segmentation and object-based image analysis of four sets of high resolution orthophotos. Subsequently, post-classification change detection using image differencing was utilized.  Multiscale multiresolution segmentation was utilized to obtain image objects of every land-cover class throughout the whole study area. Scale parameters for multiresolution segmentation were determined with the estimation of scale parameter tool (ESP2) developed by Drăguţ et al. (2014).  The class hierarchy used in this research is: forest, grassland, shadow, bare soil and infrastructure. To classify the land-cover classes, supervised feature space optimization (FSO) is used to determine the parameter combinations with the highest separation distance (Platt & Rapoza, 2008). 250 representative (based on expert knowledge) samples of every land-cover class are used by the FSO algorithm on 48 distinct features.  Classification consisted of two steps: (1) stratified membership classification and (2) contextual improvements. The classification workflow was developed and optimized in one tile and subsequently upscaled to the whole of Laternser valley for analysis and validation. A minimum mapping unit of 3.125 m² (50 pixels) was utilized.  The stratified membership classification was based on the feature thresholds derived from the FSO. The stratified membership classification workflow was designed to be conservative in order to minimize false positives. The contextual improvements were used to classify remaining false negatives.  The contextual improvement rationale was developed to account for landscape homogeneity, variability and overlap in the feature space. The contextual improvements were based on expert interpretation of false positive and false negatives in combination with the original feature space parameters of the objects. Every rule in the workflow only removed a small portion of omission and commission errors.  The land-cover change analysis is performed using post-classification image differencing by automatic comparison of image sub-object hierarchies (Gutierrez et al., 2012; Zhou et al., 2008).  Derivation of LCC trends is utilized for interpretation of LCC in the vicinity (<25m) of shallow landslides to quantitatively assess the effect of LCC on rainfall induced landslide occurrence (Begueria, 2006). An automated ArcGIS model is used to calculate the area per land-cover change class using a 25 [m] buffer around the scarpoints derived from the C3S-ISLS project.   3.5  Accuracy Assessment  The accuracy assessment consists of two parts: (1) standard confusion matrix, (2) classification stability.      A stratified sampling scheme was used to obtain 150 samples per period based on the relative occurrence of land-cover classes and the entire range of membership probabilities to obtain a representative sample. Additionally, 150 samples in the vicinity (<25 m) of landslides were specified to compare the standard overall accuracy with the landslide specific application.  In this study the classification stability was used to differentiate between the segments correctly classified by the initial membership-based classification and the contextual improvements.   4. RESULTS 4.1 Land Cover Change Analysis Classification accuracies range from 76% (198x) to 88% for the 2009 orthophotos. Kappa statistic falls between 64% (198x) to 81% in 2009. The context based classification ruleset improved 52% of all accurately classified samples in 198x and decreases over time to 39% in 2009 due to better image quality and reduced overlap in the feature space in the more recent years. This research shows the effectiveness of utilizing context to improve classification accuracy after initial membership-based classification in an object-based workflow to deal with overlap in the feature space.  Figure 4 shows the results of the LCC between 198x and 2009.   Figure 4. LCC results 198x-2009.  Figure 5 shows the relative area change for every land-cover class from 198x to 2009 in the whole Laternser Valley. A general increase in forest is observed while grassland decreases. Approximately 18% of the total area undergoes LCC between 198x and 2009. The biggest LCC is the shift from grassland to forest attributing to 10.5% of the area and 58% of total LCC. This LCC shift occurs mainly at open areas within the canopy and at the edges of the tree-line.   Figure 5. Relative land cover change for the four classes per orthophoto series in the whole Laternser valley. Error bars are derived from the average user/producer accuracy per land-cover class.   4.2    Change Detection and Landslides  Land-cover changes that occur in the vicinity (<25 m) of landslides are shown in figure 6. Land-cover changes account for 33.9% (198x-2009) within the 25 m buffer area. The land-cover changes that occur the most are: a shift from grassland to bare soil / infrastructure, and a shift from forest to grassland.   Figure 6. Relative land cover change for the four classes per orthophoto series in the vicinity of landslides. Error bars are derived from the average user/producer accuracy per land-cover class.  5. CONCLUSION In the Laternser valley 18% of the total area undergoes LCC while in the vicinity of landslides LCC occurs on 34%. Although overall LCC trends indicate a shift from grassland to forest for the Laternser valley, the opposite is observed near shallow landslides. So far, a clear correlation between LCC and landslide occurrence could not be established. The question remains whether the observed land-cover change actually caused the landslides or was the eventual effect of the landslides. Further analysis will incorporate topographic LiDAR data to optimize segmentation and classification and integrate additional explanatory variables for shallow landslide susceptibility such as slope angle, aspect and curvature. Additional ancillary data such as lithology would contribute to strengthening the analysis.   ACKNOWLEDGEMENTS We thank the Federal State of Vorarlberg for providing the necessary data. This work has been conducted within the project C3S-ISLS, which is funded by the Austrian Climate and Energy Fund, 5th ACRP Program.  REFERENCES Aguirre-Gutiérrez, J., Seijmonsbergen, A. C., & Duivenvoorden, J. F. (2012). Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography, 34, 29-37.  Beguería, S. (2006). Changes in land cover and shallow landslide activity: a case study in the Spanish Pyrenees. Geomorphology, 74(1), 196-206.  Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 119-127.  Friebe, J. (2007). Geologie der österreichischen bundesländer vorarlberg. Geologische Bundesanstalt, Wien, 154.  Platt, R. V., & Rapoza, L. (2008). An evaluation of an object-oriented paradigm for land Use/Land cover classification. The Professional Geographer, 60(1), 87-100.  Seijmonsbergen, A. C., De Jong, M. G., de Graaff, L. W., & Anders, N. S. (2014). Geodiversität von vorarlberg und liechtenstein-geodiversity of vorarlberg and liechtenstein. Haupt Verlag AG.  Zieher, T.; Perzl, F.; Rössel, M.; Rutzinger, M.; Meißl, G.; Markart, G. & Geitner, C. (2016), 'A multi-annual landslide inventory for the assessment of shallow landslide susceptibility - Two test cases in Vorarlberg, Austria', Geomorphology  259, 40 - 54.  Zhou, W., Troy, A., & Grove, M. (2008). Object-based land cover classification and change analysis in the baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors, 8(3), 1613-1636. 

UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl)
UvA-DARE (Digital Academic Repository)
Assessment of the interaction of land-cover change on shallow landslide occurrence using an
automated object-based approach
Kamps, M.; Seijmonsbergen, A.C.; Rutzinger, M.; Zieher, T.
Published in:
GEOBIA 2016 : Solutions and synergies
DOI:
10.3990/2.433
Link to publication
Citation for published version (APA):
Kamps, M., Seijmonsbergen, A. C., Rutzinger, M., & Zieher, T. (2016). Assessment of the interaction of land-
cover change on shallow landslide occurrence using an automated object-based approach. In N. Kerle, M.
Gerke, & S. Lefevre (Eds.), GEOBIA 2016 : Solutions and synergies: 14 September 2016-16 September 2016,
University of Twente, Faculty of Geo-Information and Earth Observation (ITC) : proceedings University of
Twente. https://doi.org/10.3990/2.433
General rights
It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s),
other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons).
Disclaimer/Complaints regulations
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating
your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask
the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,
The Netherlands. You will be contacted as soon as possible.
Download date: 05 Feb 2021
Assessment of the interaction of land-cover change on shallow landslide occurrence using an 
automated object-based approach 
 
 
M. Kamps a, A.C. Seijmonsbergen a, M. Rutzingerbc and T. Zieherbc 
 
aUniversity of Amsterdam – Institute for Biodiversity and Ecosystem Dynamics (IBED) - martijntkamps@gmail.com – 
a.c.seijmonsbergen@uva.nl 
b University Innsbruck - Institute for Geography 
cAustrian Academy of Sciences - Institute of Interdisciplinary Mountain Research (IGF) - martin.rutzinger@oeaw.ac.at - 
thomas.zieher@uibk.ac.at 
 
 
KEY WORDS: Data Synergy, OBIA, Landslide, LiDAR, Orthophotos, Land Cover Change 
 
ABSTRACT: Land-cover change could considerably lower landslide triggering rainfall thresholds allowing precipitation events 
with shorter recurrence intervals to initiate shallow landslides. This research focusses on developing an automated, robust and up-
scalable workflow to quantitatively assess the effect land-cover change has on initiating rainfall induced shallow landslides in the 
Laternser Valley. Land-cover is classified using four sets of high resolution orthophotos (198x, 2001, 2006, 2009; 0.25 m spatial 
resolution) by applying an object-based approach with eCognition software. The correlation between land-cover change and 
landslide occurrence was assessed by analyzing land-cover change trends in the vicinity (< 25 meters) of mapped shallow landslides. 
The obtained classification accuracy ranges from 76% for 198x to 88% for 2009. The relative area undergoing land-cover change is 
18% in the whole Laternser valley and 34% in the vicinity of landslides. Overall land-cover change trends indicate a shift from 
grassland to forest in the whole Laternser valley. However, in the vicinity of landslides the opposite is observed, namely a shift from 
forest to grassland and grassland to bare soil. Even though a general vegetation reduction is detected in the vicinity of landslides no 
correlation between LCC and landslide occurrence could be established yet.  
 
 
1. INTRODUCTION 
Shallow landslides are geomorphological processes occurring in 
mountainous regions. Shallow landslide susceptibility depends 
on several factors such as topography, changing land cover and 
characteristics of subsurface material. Detailed knowledge of 
fine-scale past and present land cover change (LCC) is a 
prerequisite for modelling and analysing landslide 
susceptibility. Classification of high resolution orthophotos - as 
a transparent and transferable method - is well suited to 
efficiently and accurately document LCC and to investigate the 
relation with known landslide-triggering rainfall events. The 
aims of this research are:   
 
(1). To develop a workflow for automated object-based LCC 
detection using high resolution orthophotos in the Laternser 
valley, Austria. 
(2). To analyse the correlation between LCC and the temporal 
occurrence of rainfall triggered shallow landslides. 
 
2. STUDY AREA  
The Laternser valley is located in the Eastern Alps in 
Vorarlberg, the westernmost province of Austria. Vorarlberg is 
a mountainous province characterized by large topographic 
differences and heterogeneous land-cover. The Laternser valley 
is situated between 47.23-47.31°N and 9.66-9.84°E and 
comprises of an area of 52.1 km² (Figure 1). Approximately 
68% of the total area has an inclination between 20 and 50 
degrees making it generally susceptible to landslides. The 
geological setting of Vorarlberg was formed by succession of 
calcareous, dolomitic and pelitic sediments originating from the 
Mesozoic era (Friebe, 2007). Vorarlberg has a typical Alpine 
geomorphology characterized by subglacial till, moraine ridges,
  
  
 
Figure 1. Location of the Laternser Valley in Vorarlberg, 
western Austria. 
 
 
ice-marginal landform deposits and cirques (Seijmonsbergen et 
al., 2014). The Laternser valley is dominated by quaternary, 
penninic and helvetic nappes (Friebe, 2007) and is drained by a 
tributary of the Rhine, the river Frutz.  
 
3. METHODS 
3.1 Data 
Table 1 shows the meta data of the multi-temporal orthophotos 
utilized in this research.  
 
Orthophoto 
series 
Photo 
scale 
GSM 
[m] 
Radiometric 
resolution 
Bands 
198x 10.000-
41.000 
0.2 8 bit CIR 
2001 10.000- 
15.000  
0.25 8 bit CIR 
2006 10.000-
12.000 
0.125 8 bit RGB 
2009 10.000-
15.000 
0.125 8 bit RGB 
 
 
 
Table 1. Meta data of the high resolution orthophoto series. 
GSD: ground sampling distance, CIR: color infrared imagery, 
RGB: Red, green, blue; true color imagery. Adopted from 
Zieher et al., 2016. 
 
A well-documented field- and orthophoto-based inventory of 
shallow landslides is available for the Laternser Valley in 
Vorarlberg, developed as part of the project Climate Induced 
System Status Changes at Slopes and Their Impact on Shallow 
Landslide Susceptibility (C3S-ISLS). Most of those registered 
landslides relate to the 1999 and 2005 rainfall events. Figure 2 
shows the mapped landslides in three time periods that were 
analysed in this research. This multi-annual landslide inventory 
with a 1:400 nominal scale is used as reference dataset for the 
location of shallow landslides. 
 
 
 
Figure 2. Mapped shallow landslides in the Laternser valley.  
 
 
3.2   Workflow  
Figure 3 shows a schematic overview of the general workflow 
of this research. Four steps are included: pre-processing, 
analysis, accuracy assessment and the deliverables to work 
towards the two aims: land-cover change maps and assessing 
the correlation between LCC and shallow landslide occurrence.
   
 
 
 
 
Figure 3. Schematic overview of the workflow. 
 
 
3.3      Pre-processing and software 
 
Pre-processing of the data was performed using ArcGIS 10.3.1 
and QGIS 2.4.0. eCognition Developer 9.1.2 software was used 
to develop the LCC maps. Pre-processing consists of 
resampling to 0.25x0.25 m and tiling the orthophotos to tiles of 
2750 m edge length including an overlap area of 10% to ensure 
minimization of object segmentation edge effects. 
 
3.4     Land Cover Change Analysis 
 
Our workflow in eCognition Developer for LCC combines 
segmentation and object-based image analysis of four sets of 
high resolution orthophotos. Subsequently, post-classification 
change detection using image differencing was utilized. 
 
Multiscale multiresolution segmentation was utilized to obtain 
image objects of every land-cover class throughout the whole 
study area. Scale parameters for multiresolution segmentation 
were determined with the estimation of scale parameter tool 
(ESP2) developed by Drăguţ et al. (2014). 
 
The class hierarchy used in this research is: forest, grassland, 
shadow, bare soil and infrastructure. 
To classify the land-cover classes, supervised feature space 
optimization (FSO) is used to determine the parameter 
combinations with the highest separation distance (Platt & 
Rapoza, 2008). 250 representative (based on expert knowledge) 
samples of every land-cover class are used by the FSO 
algorithm on 48 distinct features. 
 
Classification consisted of two steps: (1) stratified membership 
classification and (2) contextual improvements. The 
classification workflow was developed and optimized in one tile 
and subsequently upscaled to the whole of Laternser valley for 
analysis and validation. A minimum mapping unit of 3.125 m² 
(50 pixels) was utilized. 
 
The stratified membership classification was based on the 
feature thresholds derived from the FSO. The stratified 
membership classification workflow was designed to be 
conservative in order to minimize false positives. The 
contextual improvements were used to classify remaining false 
negatives. 
 
The contextual improvement rationale was developed to 
account for landscape homogeneity, variability and overlap in 
the feature space. The contextual improvements were based on 
expert interpretation of false positive and false negatives in 
combination with the original feature space parameters of the 
objects. Every rule in the workflow only removed a small 
portion of omission and commission errors. 
 
The land-cover change analysis is performed using post-
classification image differencing by automatic comparison of 
image sub-object hierarchies (Gutierrez et al., 2012; Zhou et al., 
2008). 
 
Derivation of LCC trends is utilized for interpretation of LCC in 
the vicinity (<25m) of shallow landslides to quantitatively 
assess the effect of LCC on rainfall induced landslide 
occurrence (Begueria, 2006). An automated ArcGIS model is 
used to calculate the area per land-cover change class using a 25 
[m] buffer around the scarpoints derived from the C3S-ISLS 
project. 
 
 
3.5  Accuracy Assessment 
 
The accuracy assessment consists of two parts: (1) standard 
confusion matrix, (2) classification stability.   
  
 
A stratified sampling scheme was used to obtain 150 samples 
per period based on the relative occurrence of land-cover 
classes and the entire range of membership probabilities to 
obtain a representative sample. Additionally, 150 samples in the 
vicinity (<25 m) of landslides were specified to compare the 
standard overall accuracy with the landslide specific 
application. 
 
In this study the classification stability was used to differentiate 
between the segments correctly classified by the initial 
membership-based classification and the contextual 
improvements. 
 
 
4. RESULTS 
4.1 Land Cover Change Analysis 
Classification accuracies range from 76% (198x) to 88% for the 
2009 orthophotos. Kappa statistic falls between 64% (198x) to 
81% in 2009. The context based classification ruleset improved 
52% of all accurately classified samples in 198x and decreases 
over time to 39% in 2009 due to better image quality and 
reduced overlap in the feature space in the more recent years. 
This research shows the effectiveness of utilizing context to 
improve classification accuracy after initial membership-based 
classification in an object-based workflow to deal with overlap 
in the feature space. 
 
Figure 4 shows the results of the LCC between 198x and 2009.  
 
Figure 4. LCC results 198x-2009. 
 
Figure 5 shows the relative area change for every land-cover 
class from 198x to 2009 in the whole Laternser Valley. A 
general increase in forest is observed while grassland decreases. 
Approximately 18% of the total area undergoes LCC between 
198x and 2009. The biggest LCC is the shift from grassland to 
forest attributing to 10.5% of the area and 58% of total LCC. 
This LCC shift occurs mainly at open areas within the canopy 
and at the edges of the tree-line. 
 
 
Figure 5. Relative land cover change for the four classes per 
orthophoto series in the whole Laternser valley. Error bars are 
derived from the average user/producer accuracy per land-cover 
class. 
 
 
4.2    Change Detection and Landslides 
 
Land-cover changes that occur in the vicinity (<25 m) of 
landslides are shown in figure 6. Land-cover changes account 
for 33.9% (198x-2009) within the 25 m buffer area. The land-
cover changes that occur the most are: a shift from grassland to 
bare soil / infrastructure, and a shift from forest to grassland. 
 
 
Figure 6. Relative land cover change for the four classes per 
orthophoto series in the vicinity of landslides. Error bars are 
derived from the average user/producer accuracy per land-cover 
class. 
 
5. CONCLUSION 
In the Laternser valley 18% of the total area undergoes LCC 
while in the vicinity of landslides LCC occurs on 34%. 
Although overall LCC trends indicate a shift from grassland to 
forest for the Laternser valley, the opposite is observed near 
shallow landslides. So far, a clear correlation between LCC and 
landslide occurrence could not be established. The question 
remains whether the observed land-cover change actually 
caused the landslides or was the eventual effect of the 
landslides. Further analysis will incorporate topographic 
LiDAR data to optimize segmentation and classification and 
integrate additional explanatory variables for shallow landslide 
susceptibility such as slope angle, aspect and curvature. 
Additional ancillary data such as lithology would contribute to 
strengthening the analysis. 
 
 
ACKNOWLEDGEMENTS 
We thank the Federal State of Vorarlberg for providing the 
necessary data. This work has been conducted within the project 
C3S-ISLS, which is funded by the Austrian Climate and Energy 
Fund, 5th ACRP Program. 
 
REFERENCES 
Aguirre-Gutiérrez, J., Seijmonsbergen, A. C., & 
Duivenvoorden, J. F. (2012). Optimizing land cover 
classification accuracy for change detection, a combined pixel-
based and object-based approach in a mountainous area in 
Mexico. Applied Geography, 34, 29-37. 
 
Beguería, S. (2006). Changes in land cover and shallow 
landslide activity: a case study in the Spanish 
Pyrenees. Geomorphology, 74(1), 196-206. 
 
Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). 
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Assessment of the interaction of land-cover change on shallow landslide occurrence using an automated object-based approach

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