Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study

Raphael Knevels, Alexander Brenning, Simone Gingrich, Gerhard Heiss, Theresia Lechner, Philip Leopold, Christoph Plutzar, Herwig Proske and Helene Petschko
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Raphael Knevels: Department of Geography, Friedrich Schiller University Jena, 07743 Jena, Germany
Alexander Brenning: Department of Geography, Friedrich Schiller University Jena, 07743 Jena, Germany
Simone Gingrich: Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, 1070 Vienna, Austria
Gerhard Heiss: Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria
Theresia Lechner: Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, 1070 Vienna, Austria
Philip Leopold: Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria
Christoph Plutzar: Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, 1070 Vienna, Austria
Herwig Proske: Remote Sensing and Geoinformation Department, JOANNEUM RESEARCH Forschungsgesellschaft mbH, 8010 Graz, Austria
Helene Petschko: Department of Geography, Friedrich Schiller University Jena, 07743 Jena, Germany

Land, 2021, vol. 10, issue 9, 1-29

Abstract: Land use/land cover (LULC) changes may alter the risk of landslide occurrence. While LULC has often been considered as a static factor representing present-day LULC, historical LULC dynamics have recently begun to attract more attention. The study objective was to assess the effect of LULC legacies of nearly 200 years on landslide susceptibility models in two Austrian municipalities (Waidhofen an der Ybbs and Paldau). We mapped three cuts of LULC patterns from historical cartographic documents in addition to remote-sensing products. Agricultural archival sources were explored to provide also a predictor on cumulative biomass extraction as an indicator of historical land use intensity. We use historical landslide inventories derived from high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), which are reported to have a biased landslide distribution on present-day forested areas and agricultural land. We asked (i) if long-term LULC legacies are important and reliable predictors and (ii) if possible inventory biases may be mitigated by LULC legacies. For the assessment of the LULC legacy effect on landslide occurrences, we used generalized additive models (GAM) within a suitable modeling framework considering various settings of LULC as predictor, and evaluated the effect with well-established diagnostic tools. For both municipalities, we identified a high density of landslides on present-day forested areas, confirming the reported drawbacks. With the use of LULC legacy as an additional predictor, it was not only possible to account for this bias, but also to improve model performances.

Keywords: land use/land cover legacy; airborne LiDAR-based HRDTM; generalized additive model; landslide susceptibility modeling; historical landslide inventory bias; biomass extraction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:10:y:2021:i:9:p:954-:d:631627

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