# Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

**Figure 1.**Identification of the study area in Turkey and the North Anatolian Fault (

**a**); density maps of inactive (

**b**) and active (

**c**) landslides (see Section 2.2 for definitions); Peak Ground Acceleration map (

**d**) from [40]; mean annual precipitation map (

**e**) from [41].

#### 2.1. Mapping Units

#### 2.2. Landslides

## 3. Modelling Strategy

_{i}| η

_{i}∼ Binomial (N

_{i}, p

_{i})

_{i}= p

_{i}⁄ (1−p

_{i})

_{i}is the binomial probability.

_{i}= 1 for all

_{i}because we have binary data. The η

_{i}as a function of p

_{i}is called the link function, and we describe it using a logit, but we note that other link functions are possible. The linear predictor η is where we put the additive model:

_{i}= β

_{1x1,i}+ ... + β

_{mxm,i}+ f(Slope) + f(Precipitation),

_{j}are the fixed (or linear) effects, with weak priors, describing the linear relationship of the covariates x

_{j}. Each f represents a random (or nonlinear) effect with

**Table 1.**List of covariates. L and NL indicate that the linear and nonlinear effects were investigated, respectively. SD stands for standard deviation. All values are calculated within each SU.

Name | Abbreviation | Reference | Usage in the Inventory | |
---|---|---|---|---|

Inactive | Active | |||

Mean slope | Slope | [62] | NL | NL |

SD of slope | Slopeσ | [62] | L | L |

Mean Rainfall | Precipitation | [5] | NL | NL |

Mean peak ground acceleration | PGAμ | [5] | L | L |

Topographic relief | Reliefμ | [35] | L | L |

Elongation of the SU | Elongation | [46] | L | L |

Mean Eastness | ESTμ | [50] | L | L |

Mean Northness | NRTμ | [50] | L | L |

SD of Northness | NRTσ | [50] | L | L |

SD of planar curvature | PLCσ | [63] | L | L |

Mean profile curvature | PRCμ | [63] | L | L |

Mean Relative slope position | RSPμ | [64] | L | L |

SD of Relative slope position | RSPσ | [64] | L | L |

Mean topographic wetness index | TWIμ | [64] | L | L |

SD of topographic wetness index | TWIσ | [64] | L | L |

Mean Stream power index | SPIμ | [65] | L | L |

SD of Stream power index | SPIσ | [65] | L | L |

Mean Distance to Fault | D2Fμ | [15] | L | L |

SD of Distance to Fault | D2Fσ | [15] | L | L |

## 4. Results

#### 4.1. Distinct Patterns of Explanatory Variables

#### 4.2. Distinct Landslide Triggers

#### 4.3. Distinct Susceptibility Maps

## 5. Discussion

#### 5.1. Controls and Fate of Active Landslides

#### 5.2. Accuracy of the Active/Inactive Landslide Classification

## 6. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Hutton, J.X. Theory of the Earth; or an Investigation of the Laws observable in the Composition, Dissolution, and Restoration of Land upon the Globe. Trans. R. Soc. Edinb.
**1788**, 1, 209–304. [Google Scholar] [CrossRef] [Green Version] - Lyell, C.; Clowes, W.; Deshayes, G.P.; Murray, J. Principles of Geology. In Being an Attempt to Explain the Former Changes of the Earth’s Surface, by Reference to Causes Now in Operation; John Murray: London, UK, 1830; Volume 1. [Google Scholar] [CrossRef]
- Reichenbach, P.; Rossi, M.; Malamud, B.D.; Mihir, M.; Guzzetti, F. A Review of Statistically-Based Landslide Susceptibility Models. Earth Sci. Rev.
**2018**, 180, 60–91. [Google Scholar] [CrossRef] - Guzzetti, F.; Reichenbach, P.; Cardinali, M.; Galli, M.; Ardizzone, F. Probabilistic Landslide hazard Assessment at the Basin Scale. Geomorphology
**2005**, 72, 272–299. [Google Scholar] [CrossRef] - Fan, X.; Yunus, A.P.; Scaringi, G.; Catani, F.; Subramanian, S.S.; Xu, Q.; Huang, R. Rapidly Evolving Controls of Landslides after a Strong Earthquake and Implications for Hazard Assessments. Geophys. Res. Lett.
**2021**, 48. [Google Scholar] [CrossRef] - Tang, R.; Fan, X.; Scaringi, G.; Xu, Q.; Van Westen, C.J.; Ren, J.; Havenith, H.-B. Distinctive Controls on the Distribution of River-Damming and Non-Damming Landslides Induced by the 2008 Wenchuan Earthquake. Bull. Eng. Geol. Environ.
**2018**, 78, 4075–4093. [Google Scholar] [CrossRef] - Orme, A.R. Shifting Paradigms in Geomorphology: The Fate of Research Ideas in an Educational Context. Geomorphology
**2002**, 47, 325–342. [Google Scholar] [CrossRef] - Ercanoglu, M.; Gokceoglu, C.; Van Asch, T.W.J. Landslide Susceptibility Zoning of North of Yenice (NW Turkey) by Multivariate Statistical Techniques. Nat. Hazards
**2004**, 32, 1–23. [Google Scholar] [CrossRef] - Goudie, A.S. (Ed.) International Association of Geomorphologists Encyclopedia of Geomorphology; Routledge: New York, NY, USA, 2004; ISBN 978-1-134-48275-7. [Google Scholar]
- Saponaro, A.; Pilz, M.; Wieland, M.; Bindi, D.; Moldobekov, B.; Parolai, S. Landslide Susceptibility Analysis in Data-Scarce Regions: The Case of Kyrgyzstan. Bull. Eng. Geol. Environ.
**2014**, 74, 1117–1136. [Google Scholar] [CrossRef] [Green Version] - Zêzere, J.; Pereira, S.; Melo, R.; Oliveira, S.; Garcia, R. Mapping Landslide Susceptibility Using Data-Driven Methods. Sci. Total Environ.
**2017**, 589, 250–267. [Google Scholar] [CrossRef] - Jones, J.N.; Boulton, S.J.; Bennett, G.L.; Stokes, M.; Whitworth, M.R.Z. Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling. J. Geophys. Res. Earth Surf.
**2021**, 126. [Google Scholar] [CrossRef] - Scaringi, G.; Loche, M. A Thermo-Hydro-Mechanical Approach to Soil Slope Stability under Climate Change. Geomorphology
**2022**, 401, 108108. [Google Scholar] [CrossRef] - Domènech, G.; Fan, X.; Scaringi, G.; van Asch, T.W.; Xu, Q.; Huang, R.; Hales, T.C. Modelling the Role of Material Depletion, Grain Coarsening and Revegetation in Debris Flow Occurrences after the 2008 Wenchuan Earthquake. Eng. Geol.
**2019**, 250, 34–44. [Google Scholar] [CrossRef] - Lombardo, L.; Mai, P.M. Presenting Logistic Regression-Based Landslide Susceptibility Results. Eng. Geol.
**2018**, 244, 14–24. [Google Scholar] [CrossRef] - Loche, M.; Scaringi, G.; Yunus, A.P.; Catani, F.; Tanyaş, H.; Frodella, W.; Fan, X.; Lombardo, L. Surface Temperature Controls the Pattern of Post-Earthquake Landslide Activity. Sci. Rep.
**2022**, 12, 1–11. [Google Scholar] [CrossRef] - Duman, T.Y.; Çan, T.; Emre, Ö. 1: 1,500,000 Scaled Turkish Landslide Inventory Map; General Directorate of Mineral Research and Exploration Publication: Ankara, Turkey, 2011; Volume 27. [Google Scholar]
- Glade, T. Establishing the Frequency and Magnitude of Landslide-Triggering Rainstorm Events in New Zealand. Environ. Earth Sci.
**1998**, 35, 160–174. [Google Scholar] [CrossRef] - Fan, X.; Scaringi, G.; Domènech, G.; Yang, F.; Guo, X.; Dai, L.; He, C.; Xu, Q.; Huang, R. Two Multi-Temporal Datasets That Track the Enhanced Landsliding after the 2008 Wenchuan Earthquake. Earth Syst. Sci. Data
**2019**, 11, 35–55. [Google Scholar] [CrossRef] [Green Version] - Ali, M.Z.; Chu, H.-J.; Chen, Y.-C.; Ullah, S. Machine Learning in Earthquake- and Typhoon-Triggered Landslide Susceptibility Mapping and Critical Factor Identification. Environ. Earth Sci.
**2021**, 80, 1–21. [Google Scholar] [CrossRef] - Steger, S.; Brenning, A.; Bell, R.; Glade, T. The Propagation of Inventory-Based Positional Errors into Statistical Landslide Susceptibility Models. Nat. Hazards Earth Syst. Sci.
**2016**, 16, 2729–2745. [Google Scholar] [CrossRef] [Green Version] - Lima, P.; Steger, S.; Glade, T.; Tilch, N.; Schwarz, L.; Kociu, A. Landslide Susceptibility Mapping at National Scale: A First Attempt for Austria. In Advancing Culture of Living with Landslides; Mikos, M., Tiwari, B., Yin, Y., Sassa, K., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 943–951. [Google Scholar] [CrossRef]
- Steger, S.; Kofler, C. Statistical Modeling of Landslides. In Spatial Modeling in GIS and R for Earth and Environmental Sciences; Elsevier: Amsterdam, The Netherlands, 2019; pp. 519–546. ISBN 978-0-12-815226-3. [Google Scholar]
- Eeckhaut, M.V.D.; Hervás, J.; Jaedicke, C.; Malet, J.-P.; Montanarella, L.; Nadim, F. Statistical Modelling of Europe-Wide LandSlide Susceptibility Using Limited Landslide Inventory Data. Landslides
**2011**, 9, 357–369. [Google Scholar] [CrossRef] - Kirschbaum, D.; Stanley, T.; Zhou, Y. Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology
**2015**, 249, 4–15. [Google Scholar] [CrossRef] - Petschko, H.; Bell, R.; Brenning, A.; Glade, T. Landslide Susceptibility Modeling with Generalized Additive Models–Facing the Heterogeneity of Large Regions. Landslides Eng. Slopes Prot. Soc. Improv. Underst.
**2012**, 1, 769–777. [Google Scholar] - Das, I.; Stein, A.; Kerle, N.; Dadhwal, V.K. Landslide Susceptibility Mapping along Road Corridors in the Indian Himalayas Using Bayesian Logistic Regression Models. Geomorphology
**2012**, 179, 116–125. [Google Scholar] [CrossRef] - Alvioli, M.; Marchesini, I.; Reichenbach, P.; Rossi, M.; Ardizzone, F.; Fiorucci, F.; Guzzetti, F. Automatic Delineation of Geomorphological Slope Units with r.slopeunits v1.0 and Their Optimization for Landslide Susceptibility Modeling. Geosci. Model Dev.
**2016**, 9, 3975–3991. [Google Scholar] [CrossRef] [Green Version] - Alvioli, M.; Marchesini, I.; Guzzetti, F. Nation-Wide, General-Purpose Delineation of Geomorphological Slope Units in Italy. PeerJ Prepr.
**2018**, 6, e27066v1. [Google Scholar] - Alvioli, M.; Guzzetti, F.; Marchesini, I. Parameter-Free Delineation of Slope Units and Terrain Subdivision of Italy. Geomorphology
**2020**, 358, 107124. [Google Scholar] [CrossRef] - Tanyas, H.; Rossi, M.; Alvioli, M.; van Westen, C.J.; Marchesini, I. A Global Slope Unit-Based Method for the Near Real-Time Prediction of Earthquake-Induced Landslides. Geomorphology
**2018**, 327, 126–146. [Google Scholar] [CrossRef] - Sengör, A.M.C.; Yilmaz, Y. Tethyan Evolution of Turkey: A Plate Tectonic Approach. Tectonophysics
**1981**, 75, 181–241. [Google Scholar] [CrossRef] - Okay, A.I. Geology of Turkey: A Synopsis. Anschnitt
**2008**, 21, 19–42. [Google Scholar] - Gorum, T.; Gonencgil, B.; Gokceoglu, C.; Nefeslioglu, H.A. Implementation of Reconstructed Geomorphologic Units in LandSlide Susceptibility Mapping: The Melen Gorge (NW Turkey). Nat. Hazards
**2008**, 46, 323–351. [Google Scholar] [CrossRef] - Görüm, T. Tectonic, Topographic and Rock-Type Influences on Large Landslides at the Northern Margin of the Anatolian Plateau. Landslides
**2018**, 16, 333–346. [Google Scholar] [CrossRef] - Akbaş, B.; Akdeniz, N.; Aksay, A.; Altun, İ.E.; Balcı, V.; Bilginer, E.; Bilgiç, T.; Duru, M.; Ercan, T.; Gedik, İ.; et al. 1:1.250.000 Scaled Geological Map of Turkey; General Directorate of Mineral Research and Exploration Publication: Ankara, Turkey, 2011. [Google Scholar]
- Geology and Tectonic Evolution of the Pontides. In Regional and Petroleum Geology of the Black Sea and Surrounding Region; American Association of Petroleum Geologists: Tulsa, OK, USA, 1997; pp. 183–226. ISBN 978-0-89181-348-4.
- Okay, A.; Şengör, A.C.; Görür, N. Kinematic History of the Opening of the Black Sea and Its Effect on the Surrounding Regions. Geology
**1994**, 22, 559–562. [Google Scholar] [CrossRef] - Duman, T.Y.; Çan, T.; Emre, Ö.; Keçer, M.; Doğan, A.; Ateş, Ş.; Durmaz, S. Landslide Inventory of Northwestern Anatolia, Turkey. Eng. Geol.
**2005**, 77, 99–114. [Google Scholar] [CrossRef] - Ulusay, R.; Aydan, Ö.; Kılıc, R. Geotechnical Assessment of the 2005 Kuzulu Landslide (Turkey). Eng. Geol.
**2007**, 89, 112–128. [Google Scholar] [CrossRef] - Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very High Resolution Interpolated Climate Surfaces for Global Land Areas. Int. J. Climatol.
**2005**, 25, 1965–1978. [Google Scholar] [CrossRef] - Carrara, A. Drainage and Divide Networks Derived from High-Fidelity Digital Terrain Models. In Quantitative Analysis of Mineral and Energy Resources; Chung, C.F., Fabbri, A.G., Sinding-Larsen, R., Eds.; Springer: Dordrecht, The Netherlands, 1988; pp. 581–597. ISBN 978-94-010-8288-4. [Google Scholar]
- Guzzetti, F.; Carrara, A.; Cardinali, M.; Reichenbach, P. Landslide Hazard Evaluation: A Review of Current Techniques and Their Application in a Multi-Scale Study, Central Italy. Geomorphology
**1999**, 31, 181–216. [Google Scholar] [CrossRef] - Guzzetti, F.; Reichenbach, P.; Ardizzone, F.; Cardinali, M.; Galli, M. Estimating the Quality of Landslide Susceptibility Models. Geomorphology
**2006**, 81, 166–184. [Google Scholar] [CrossRef] - Pokharel, B.; Alvioli, M.; Lim, S. Assessment of Earthquake-Induced Landslide Inventories and Susceptibility Maps Using Slope Unit-Based Logistic Regression and Geospatial Statistics. Sci. Rep.
**2021**, 11, 1–15. [Google Scholar] [CrossRef] - Camilo, D.C.; Lombardo, L.; Mai, P.M.; Dou, J.; Huser, R. Handling High Predictor Dimensionality in Slope-Unit-Based Landslide Susceptibility Models through LASSO-Penalized Generalized Linear Model. Environ. Model. Softw.
**2017**, 97, 145–156. [Google Scholar] [CrossRef] [Green Version] - Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P. Optimizing Landslide Susceptibility Zonation: Effects of DEM Spatial Resolution and Slope Unit Delineation on Logistic Regression Models. Geomorphology
**2017**, 301, 10–20. [Google Scholar] [CrossRef] - UNESCO Working Party On World Landslide Inventory A Suggested Method for Describing the Activity of a Landslide. Bull. Int. Assoc. Eng. Geol.
**1993**, 47, 53–57. [CrossRef] - Lombardo, L.; Cama, M.; Conoscenti, C.; Märker, M.; Rotigliano, E. Binary Logistic Regression Versus Stochastic Gradient Boosted Decision Trees in Assessing Landslide Susceptibility for Multiple-Occurring Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, Southern Italy). Nat. Hazards
**2015**, 79, 1621–1648. [Google Scholar] [CrossRef] - Lombardo, L.; Opitz, T.; Huser, R. Point Process-Based Modeling of Multiple Debris Flow Landslides Using INLA: An Application to the 2009 Messina Disaster. Stoch. Hydrol. Hydraul.
**2018**, 32, 2179–2198. [Google Scholar] [CrossRef] [Green Version] - Bakka, H.; Rue, H.; Fuglstad, G.; Riebler, A.; Bolin, D.; Illian, J.; Krainski, E.; Simpson, D.; Lindgren, F. Spatial Modeling with R-INLA: A Review. WIREs Comput. Stat.
**2018**, 10, e1443. [Google Scholar] [CrossRef] [Green Version] - Lombardo, L.; Tanyas, H.; Nicu, I.C. Spatial Modeling of Multi-Hazard Threat to Cultural Heritage Sites. Eng. Geol.
**2020**, 277, 105776. [Google Scholar] [CrossRef] - Lindgren, F.; Rue, H. Bayesian Spatial Modelling with R-INLA. J. Stat. Softw.
**2015**, 63, 1–25. [Google Scholar] [CrossRef] [Green Version] - Allen, M.P. The Problem of Multicollinearity. In Understanding Regression Analysis; Springer: Boston, MA, USA, 1997; pp. 176–180. ISBN 978-0-306-45648-0. [Google Scholar]
- Mela, C.F.; Kopalle, P.K. The Impact of Collinearity on Regression Analysis: The Asymmetric Effect of Negative and Positive Correlations. Appl. Econ.
**2002**, 34, 667–677. [Google Scholar] [CrossRef] - Pourghasemi, H.R.; Rossi, M. Landslide Susceptibility Modeling in a Landslide Prone Area in Mazandarn Province, North of Iran: A Comparison between GLM, GAM, MARS, and M-AHP Methods. Arch. Meteorol. Geophys. Bioclimatol. Ser. B
**2016**, 130, 609–633. [Google Scholar] [CrossRef] - Petschko, H.; Brenning, A.; Bell, R.; Goetz, J.; Glade, T. Assessing the Quality of Landslide Susceptibility Maps–Case Study Lower Austria. Nat. Hazards Earth Syst. Sci.
**2014**, 14, 95–118. [Google Scholar] [CrossRef] [Green Version] - Brenning, A. Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation. Nat. Hazards Earth Syst. Sci.
**2005**, 5, 853–862. [Google Scholar] [CrossRef] - Ploton, P.; Mortier, F.; Réjou-Méchain, M.; Barbier, N.; Picard, N.; Rossi, V.; Dormann, C.; Cornu, G.; Viennois, G.; Bayol, N.; et al. Spatial Validation Reveals Poor Predictive Performance of Large-Scale Ecological Mapping Models. Nat. Commun.
**2020**, 11, 1–11. [Google Scholar] [CrossRef] - Valavi, R.; Elith, J.; Lahoz-Monfort, J.J.; Guillera-Arroita, G. Block CV: An r Package for Generating Spatially or Environmentally Separated Folds for k -Fold Cross-Validation of Species Distribution Models. Methods Ecol. Evol.
**2018**, 10, 225–232. [Google Scholar] [CrossRef] [Green Version] - Wadoux, A.M.-C.; Heuvelink, G.B.; de Bruin, S.; Brus, D.J. Spatial Cross-Validation Is Not the Right Way to Evaluate Map Accuracy. Ecol. Model.
**2021**, 457, 109692. [Google Scholar] [CrossRef] - Zevenbergen, L.W.; Thorne, C.R. Quantitative Analysis of Land Surface Topography. Earth Surf. Process. Landforms
**1987**, 12, 47–56. [Google Scholar] [CrossRef] - Heerdegen, R.G.; Beran, M.A. Quantifying Source Areas through Land Surface Curvature and Shape. J. Hydrol.
**1982**, 57, 359–373. [Google Scholar] [CrossRef] - Böhner, J.; Selige, T. Spatial Prediction of Soil Attributes Using Terrain Analysis and Climate Regionalisation. In SAGA—Analyses and Modelling Applications; Böhner, J., McCloy, K.R., Strobl, J., Eds.; Göttinger Geographische Abhandlungen: Göttingen, Germany, 2006; Volume 115, pp. 13–28. [Google Scholar]
- Moore, I.D.; Grayson, R.B.; Ladson, A.R. Digital Terrain Modelling: A Review of Hydrological, Geomorphological, and Biological Applications. Hydrol. Process.
**1991**, 5, 3–30. [Google Scholar] [CrossRef] - Hosmer, D.W.; Lemeshow, S. Applied Logistic Regression, 2nd ed.; Wiley: New York, NY, USA, 2000. [Google Scholar]
- Lacroix, P.; Handwerger, A.L.; Bièvre, G. Life and Death of Slow-Moving Landslides. Nat. Rev. Earth Environ.
**2020**, 1, 404–419. [Google Scholar] [CrossRef] - Tanyaş, H.; Kirschbaum, D.; Görüm, T.; van Westen, C.J.; Lombardo, L. New Insight into Post-seismic Landslide Evolution Processes in the Tropics. Front. Earth Sci.
**2021**, 9, 700546. [Google Scholar] [CrossRef] - Lombardo, L.; Bakka, H.; Tanyas, H.; Van Westen, C.; Mai, P.M.; Huser, R. Geostatistical Modeling to Capture Seismic-Shaking Patterns from Earthquake-Induced Landslides. J. Geophys. Res. Earth Surf.
**2019**, 124, 1958–1980. [Google Scholar] [CrossRef]

**Figure 2.**Slope Unit partition of the study area: SUs containing inactive (

**left**) and active (

**right**) landslides are shown. The sub-panels show a detail for a small region, in which it is possible to observe the flat areas excluded by the SU calculation (see Section 4.2 for explanation). The legend is valid for the whole area and zoomed panels.

**Figure 3.**Fixed effects of geomorphological variables expressed as marginal distributions for inactive and active landslides.

**Figure 4.**Nonlinear effects of slope (

**left**column) and precipitation (

**right**column) for inactive (

**top**row) and active (

**bottom**row) landslides. The effect is modelled as a random effect estimated over 20 classes with adjacent dependency. Thick coloured lines represent the posterior means whereas the coloured dashed lines indicate the posterior 95% credible interval. Dashed grey lines indicate the zero line along which coefficients play no role in the modelling outcome.

**Figure 5.**(

**a**) ROC curves and their AUCs for ten cross-validations for the inactive (

**left**) and active (

**right**) landslide models. (

**b**) Confusion plot (

**left**) constructed via the percentage of Observed TP and fitted TP against the percentage of Observed TN and fitted TN (for each landslide type), and error rates (

**right**), both have been obtained from a tenfold CV.

**Figure 6.**Susceptibility maps for inactive (

**a**) and active (

**b**) landslides. The maps are obtained by merging ten cross-validated subsets and thus entirely come from predicted estimates. The resulting probability values have been binned into seven susceptibility classes using a quantile criterion. The difference in susceptibility between (

**a**) and (

**b**) is shown in (

**c**), while the graph in (

**d**) displays their Pearson correlation.

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

© 2022 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/).

## Share and Cite

**MDPI and ACS Style**

Loche, M.; Lombardo, L.; Gorum, T.; Tanyas, H.; Scaringi, G.
Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey. *Remote Sens.* **2022**, *14*, 1321.
https://doi.org/10.3390/rs14061321

**AMA Style**

Loche M, Lombardo L, Gorum T, Tanyas H, Scaringi G.
Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey. *Remote Sensing*. 2022; 14(6):1321.
https://doi.org/10.3390/rs14061321

**Chicago/Turabian Style**

Loche, Marco, Luigi Lombardo, Tolga Gorum, Hakan Tanyas, and Gianvito Scaringi.
2022. "Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey" *Remote Sensing* 14, no. 6: 1321.
https://doi.org/10.3390/rs14061321