CATENA

We used two landslide inventories in our analyses. The first database was compiled in a multi-temporal landslide inventory consisting of landslide objects in form of polygons which were derived in a semi-automated way from time series of optical RapidEye satellite remote sensing data covering the time period between 2009 and 2017. Detailed descriptions of the landslide mapping methods can be found in Behling and Roessner (2017) and Behling et al. (2014; 2016).  The second landslide inventory, we use here, was compiled by Havenith et al. (2015) in form of a historic inventory originally covering the whole Tien Shan (Mohadjer et al., 2016). This inventory comprises various data sources, e.g. remote sensing, field investigations, reports, and previously published inventories without a time stamp (e.g. Strom and Korup 2006; Schlögel et al. 2011). 

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.

Ozturk, Ugur, Massimiliano Pittore, Robert Behling, Sigrid Roessner, Louis Andreani, and Oliver Korup. 2020. “How Robust Are Landslide Susceptibility Estimates?” Landslides, August. https://doi.org/10.1007/s10346-020-01485-5.

Behling R, Roessner S (2017) Spatiotemporal landslide mapper for large areas using optical satellite time series data. In Matjaz Mikos, Binod Tiwari, Yueping Yin, and Kyoji Sassa, editors, Advancing culture of living with landslides, pages 143–152. Springer International Publishing, Cham. ISBN 978-3-319-53497-8 978-3-319-53498-5. https://doi.org/10.1007/978-3-319-53498-5. http://link.springer.com/10.1007/978-3-319-53498-5_17.

Behling R, Roessner S, Segl K, Kleinschmit B, Kaufmann H (2014) Robust automated image co-registration of optical multi-sensor time series data: database generation for multi-temporal landslide detection. Remote Sens 6(3):2572–2600. https://doi.org/10.3390/rs6032572

Behling R, Roessner S, Golovko D, Kleinschmit B (2016) Derivation of long-term spatiotemporal landslide activity—a multi-sensor time series approach. Remote Sens Environ 186:88–104. https://doi.org/10.1016/j.rse.2016.07.017

Havenith, Hans Balder, A. Strom, I. Torgoev, A. Torgoev, L. Lamair, A. Ischuk, and K. Abdrakhmatov. 2015. “Tien Shan Geohazards Database: Earthquakes and Landslides.” Geomorphology 249 (November): 16–31. https://doi.org/10.1016/j.geomorph.2015.01.037.

Mohadjer S, Ehlers TA, Bendick R, Stübner K, Strube T (2016) A quaternary fault database for central Asia. Nat Hazards Earth Syst Sci 160(2):529–542. https://doi.org/10.5194/nhess-16-529-2016

Schlögel, Romy, Isakbek Torgoev, Cédric De Marneffe, and Hans-Balder Havenith. 2011. “Evidence of a Changing Size-Frequency Distribution of Landslides in the Kyrgyz Tien Shan, Central Asia: Landslide Activity in the Kyrgyz Tien Shan.” Earth Surface Processes and Landforms 36 (12): 1658–69. https://doi.org/10.1002/esp.2184.

Strom, Alexander L., and Oliver Korup. 2006. “Extremely Large Rockslides and Rock Avalanches in the Tien Shan Mountains, Kyrgyzstan.” Landslides 3 (2): 125–36. https://doi.org/10.1007/s10346-005-0027-7