TY  - GEN
A1  - Ozturk, Ugur
A1  - Pittore, Massimiliano
A1  - Behling, Robert
A1  - Rößner, Sigrid
A1  - Andreani, Louis
A1  - Korup, Oliver
T1  - How robust are landslide susceptibility estimates?
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1346 
KW  - Landslide susceptibility
KW  - logistic regression
KW  - Southern Kyrgyzstan
KW  - Landslide inventory
KW  - remote sensing
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-541980
SN  - 1866-8372
IS  - 2
ER  - 
TY  - JOUR
A1  - Ozturk, Ugur
A1  - Pittore, Massimiliano
A1  - Behling, Robert
A1  - Rößner, Sigrid
A1  - Andreani, Louis
A1  - Korup, Oliver
T1  - How robust are landslide susceptibility estimates?
JF  - Landslides
N2  - Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
KW  - Landslide susceptibility
KW  - Logistic regression
KW  - Southern Kyrgyzstan
KW  - Landslide inventory
KW  - Remote sensing
Y1  - 2020
U6  - https://doi.org/10.1007/s10346-020-01485-5
SN  - 1612-510X
SN  - 1612-5118
VL  - 18
IS  - 2
SP  - 681
EP  - 695
PB  - Springer
CY  - Heidelberg
ER  -