**1. Introduction**

Landslides pose a threat to human lives and infrastructure. A changing climate and land use/land cover (LULC) alter the landslide risk and thus have societal consequences [1,2]. In Austria, landslides are relevant natural hazards preconditioned by factors such as lithology, geomorphology, tectonic structures and LULC, and are mainly triggered by long-lasting heavy rainfall and rapid snowmelt [3]. Therefore, understanding the factors that increase the chance of landslide occurrence is crucial for spatial planning in the face of ongoing and expected future climate and LULC changes.

**Citation:** Knevels, R.; Brenning, A.; Gingrich, S.; Heiss, G.; Lechner, T.; Leopold, P.; Plutzar, C.; Proske, H.; Petschko, H. Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study. *Land* **2021**, *10*, 954. https://doi.org/10.3390/land10090954 Academic Editors: Matej Vojtek, Andrea Petroselli and Raffaele Pelorosso

Received: 4 August 2021 Accepted: 4 September 2021 Published: 8 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

LULC types, and their changes, are reported to have different hydrological and geomechanical effects controlling slope stability [4]. While forest is often considered to stabilize slopes [5], forest harvesting or road construction undercutting slopes may reduce slope stability [6,7]. However, in landslide studies, LULC has often been considered as a static factor representing solely the present-day LULC [8] (i.e., latest available LULC). Recently, more studies have begun to account for historical LULC in landslide analysis, assigning LULC dynamics an important role in explaining landslides [9–12]. Beguería [9] and Persichillo et al. [11] discovered a high landslide susceptibility on abandoned cultivated land, even after revegetation by shrubs or trees in the Spanish Pyrenees and in the Oltrepò Pavese (Italy), respectively. Gariano et al. [10] and Pisano et al. [12] found evidence that land managemen<sup>t</sup> reduced landslide occurrences in Southern Italy (Calabria and Molise), supporting the importance of LULC changes in spatial planning practice. However, due to the availability of mainly remote-sensing products (aerial or satellite imagery), these landslide analyses were only able to consider historical LULC since the mid-20th century (e.g., since 1954 in [12] or since 1957 in [9]). To the authors' knowledge, only one study used historical cartographic documents as additional sources (e.g., Napoleonic cadastral map) to assess the long-term legacy effects of LULC on mass movements [13].

In general, legacy effects describe the influence of past events or processes on later states, often spanning decades to centuries [14,15]. Long-term legacy effects of past LULC have been shown to exist in the context of socio-ecological dimensions such as contemporary forest structure, managemen<sup>t</sup> and disturbance risk [16] or biodiversity [17,18]. For mass movements, Lopez-Saez et al. [13] revealed the potential of historical LULC changes in explaining the paradoxical observation of reduced rockfall hazards despite an increased urban exposure in the Grenoble conurbation since 1850. Especially the forest densification at the upper part of the slope was considered to contribute to the identified decrease in rockfall frequency and energy for volumes up to 5 m<sup>3</sup> [13].

For landslide analysis and modeling, landslide inventories are a fundamental source to improve the understanding of the factors that precondition and trigger landslides. In the last decade, low-cost, airborne LiDAR-based high-resolution digital terrain models (HRDTM) became available area-wide for all federal provinces of Austria ( ≤10 m × 10 m) [19]. Many studies demonstrated the potential of LiDAR HRDTM and its derivatives to identify landslides, and thus to substantially improve conventionally created landslide inventories [20–22], especially underneath the forest cover, where passive remote-sensing sensors are of limited utility [23].

Generally, landslide inventories have an unknown level of incompleteness [24] and thus may be biased. Inventory biases have previously been studied for remotely-sensed, event-based or archival inventories, and can be caused by focusing solely on administrative boundaries, damage reports or on a single triggering event [25,26]. While most authors reported the usefulness of LiDAR-derived historical landslide inventories, only few analyzed drawbacks of the data source [26,27]. For example, it is very challenging or even impossible to determine a landslide's exact extent, absolute age, trigger, and potential for reactivation when using only HRDTM derivatives [28,29]. Additionally, according to Petschko et al. [27], forest cover may have a conservation effect on the landslide morphology (i.e., "young" morphology of a "very old" landslide), while landslides may be underrepresented on agricultural land and near infrastructure due to land rehabilitation (i.e., "very young" morphology but no visibility on orthophoto) resulting in a landslide inventory that is substantially biased towards a high landslide density in recently forested areas. Analyzing the effect of systematically incomplete landslide inventories, Steger et al. [26] discovered that landslide susceptibility models emphasized bias-describing predictors (e.g., larger regression coefficients), and as a consequence the bias was directly propagated into the landslide predictions. While Petschko et al. [27] recommended to exclude inventory-biasing observations (i.e., "old" and "very old" landslides) or to drop the bias-describing predictor (i.e., present-day land cover) from modeling, Steger et al. [26] included the bias-describing

predictor (e.g., forested area) as a random effect and used only the fixed effects to make model predictions.

In this study, we investigated the association between LULC legacies and landslide susceptibility using an airborne LiDAR-derived historical landslide inventory in two municipalities in Austria (Waidhofen an der Ybbs and Paldau). We addressed the following main questions: (i) are long-term LULC legacies important and reliable predictors of landslide susceptibility? And (ii) can LULC legacies help to understand and account for possible inventory biases in modeling present-day landslide susceptibility? Additionally, we analyzed the transferability of landslide models between study areas and the effect of dropping inventory-biasing observations.

For the analysis, we digitized and classified LULC patterns for three-time cuts comprising nearly 200 years using various spatial data sources. In addition, yields and livestock statistics were compiled from archival sources and statistical publications, and summarized as socio-ecological variables reflecting plot-level LULC legacies. For the assessment, we used generalized additive models (GAM) within a modeling framework considering different combinations of LULC legacy implementations while also accounting for land surface variables (e.g., slope angle, etc.) and lithological conditions as possible confounders. We evaluated the effect of LULC legacies using well-established diagnostic tools for model assessment and interpretation.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The study was conducted in two municipalities in Austria: Waidhofen an der Ybbs (referred to as Waidhofen) in Lower Austria, and Paldau in Styria. The two municipalities represent different landscapes (Figure 1A,C).

Waidhofen is located in the Ybbstaler Alps from 14◦39 E 47◦52 N to 14◦56 E 48◦01 N, covers an area of 131 km<sup>2</sup> and has a population of about 13,000 inhabitants [30]. Its elevation rises towards the south with a relative relief of 54–651 m/km2. In the limestone-dominated south, up to 1205 m above the Adriatic (m AA) are reached; towards the flysch zone in the north the mountains transition into gentle hills (302 m AA) [31]. In contrast, Paldau lies in the East Styrian Basin with a relative relief of 6–138 m/km<sup>2</sup> (282 to 465 m AA), characterized by unconsolidated sediments of the Neogene to Quaternary period [32]. The municipality is an agriculturally favorable region mainly with corn and pig farming. Paldau has a population of 3000 inhabitants [33], it extends from 15◦43 E 46◦54 N to 15◦51 E 46◦59 N and covers 39 km2.

The geological setting coupled with the characteristic very local, frequent and intense rain events in summertime create conditions that make both study areas particularly prone to landslides of different types and magnitudes [34–36]. In the last decades, landslide occurrences have caused substantial damage to settlements and infrastructure in both study areas [37,38].
