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

Land

Gerhard Heiss¹  Philip Leopold¹  Theresia Lechner²  Simone Gingrich²  Christoph Plutzar²  Alexander Brenning³  Helene Petschko³  Raphael Knevels³  Herwig Proske⁴ 

[1] Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria;Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, 1070 Vienna, Austria;Department of Geography, Friedrich Schiller University Jena, 07743 Jena, Germany;Remote Sensing and Geoinformation Department, JOANNEUM RESEARCH Forschungsgesellschaft mbH, 8010 Graz, Austria;

关键词: land use/land cover legacy;    airborne LiDAR-based HRDTM;    generalized additive model;    landslide susceptibility modeling;    historical landslide inventory bias;    biomass extraction;   

DOI  :  10.3390/land10090954

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown