Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 https://doi.org/10.5194/nhess-19-471-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Effects of different land use and land cover data on the landslide susceptibility zonation of road networks Bruno M. Meneses, Susana Pereira, and Eusébio Reis Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Edif. IGOT, Rua Branca Edmée Marques, Lisbon, 1600-276, Portugal Correspondence: Bruno M. Meneses ([email protected]) Received: 13 December 2017 – Discussion started: 18 December 2017 Revised: 12 February 2019 – Accepted: 19 February 2019 – Published: 11 March 2019 Abstract. This work evaluates the influence of land use and land cover (LUC) data with different properties on the landslide susceptibility zonation of the road network in the Zêzere watershed (Portugal). The information value method was used to assess the landslide susceptibility using two models: one including detailed LUC data (the Portuguese Land Cover Map – COS) and the other including more generalized LUC data (the CORINE Land Cover – CLC). A set of fixed independent layers was considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope-over-area ratio, soil, and lithology) while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as a dependent layer, including 259 shallow landslides obtained from the photointerpretation of orthophotos from 2005, and further validated in three sample areas. The landslide susceptibility maps were assigned to the road network data and resulted in two landslide susceptibility road network maps. The models’ performance was evaluated with prediction and success rate curves and the area under the curve (AUC). The landslide susceptibility results obtained in the two models present a high accuracy in terms of the AUC (>90 %), but the model with more detailed LUC data (COS) produces better results in the landslide susceptibility zonation on the road network with the highest landslide susceptibility. 1 Introduction Landslides are natural processes that can constrain the free movement of people and goods when they directly or indirectly affect road networks (Bíl et al., 2014, 2015; Hilker et al., 2009; Meneses, 2011; Winter et al., 2013). The total or partial blockages of road networks have economic and societal impacts, particularly on the direct damage to the infrastructure (material damages), on the population (injuries and deaths) when driving on the affected infrastructure (Guillard and Zêzere, 2012; Pereira et al., 2014, 2017), or by causing indirect damages, such as delays, detours, material damage, and the rising prices of raw materials (Zêzere et al., 2008; Bíl et al., 2014, 2015; Jenelius and Mattsson, 2012; Winter et al., 2016). Landslide susceptibility assessment is crucial to identifying locations with higher probabilities of landslide occurrence (Conforti et al., 2014; Guillard and Zêzere, 2012; Guzzetti et al., 2006; Pereira et al., 2014; van Westen et al., 2008). Landslide susceptibility is the likelihood of a landslide occurring in an determined area controlled by local terrain conditions; it may also include a description of the velocity and intensity of an existing or potential landslide (Fell et al., 2008; Günther et al., 2013; Guzzetti et al., 1999). Landslide susceptibility reflects the degree to which a terrain unit can be affected by future slope movements (Günther et al., 2013). In general, the choice of landslide predisposing factors and the main details of the geographical information are not explained in a landslide susceptibility assessment based on statistical methods; rather, criteria defined in the literature (e.g., slope angle, slope aspect, slope curvature, soil, lithology, land use, and land cover) are used for this selection because they can explain the occurrence of slope movements in the study area (Blahut et al., 2010; Castella et al., 2007; Castellanos Abella, 2008; Guzzetti et al., 1999, 2006; Soeters Published by Copernicus Publications on behalf of the European Geosciences Union. 472 B. M. Meneses et al.: Effects of different land use and land cover data and van Westen, 1996; van Westen et al., 2008; Zêzere et al., 2008, 2017). Beyond the influence of different environmental factors (e.g., lithology, slope angle, slope morphology, topography, soils, and hydrology) on the spatial distribution of landslides, land use and land cover (LUC) dynamics are also an important factor on landslide susceptibility assessment (Guillard and Zêzere, 2012). Certain land use and land cover changes (LUCCs) (e.g., deforestation, slope ruptures to road construction, steep slopes) increase the number of unstable slopes (Reichenbach et al., 2014), i.e., promoting the propensity for landslide occurrence, and can have an important impact on landslide activity (Beguería, 2006; Glade, 2003; Mugagga et al., 2012; Persichillo et al., 2017; van Westen et al., 2008). The LUC, while a proxy variable, is very dynamic over time and is influenced by climate-driven changes and direct anthropogenic impacts (Promper et al., 2014). In this regard, it is an important predisposing factor to landslide susceptibility assessment, and Dymond et al. (2006) mention that importance: “the quality of the input land-cover map is important because the main purpose of the landslide susceptibility model is to identify where land cover needs to be changed.” For instance, performing a landslide susceptibility analysis with a historical inventory over long periods (e.g., decades) demands the use of a permanent set of predisposing factors along the landslide inventory timeline. LUC can change over time; for this reason, it will be more accurate to use the LUC for different periods (Reichenbach et al., 2014) than using the most recent LUC map, to avoid spatial relations between past slope instability and incorrect LUC classes. The scale of the predisposing factors directly influences the map elements’ representation and detail, as well as the choice of the scale of analysis of the final results (Leitner, 2004; Stoter et al., 2014). The choice in the level of detail will also constrain the modeling results. For example, Meneses et al. (2018b, c) obtained different LUCC results in Portugal due to the use of different LUC datasets, namely the CORINE Land Cover (CLC) and the official Land Cover Map of Portugal (Portuguese designation and acronym Carta de Ocupação do Solo, COS), with different properties concerning the scale (1 :100000 and 1 :25000, respectively), minimum mapping unit (25 and 1 ha, respectively), and generalization level (Table 1). Due to the variation in the road network morphology (the length vs. width of the roads), the selection of appropriate data that integrate the analysis of road blockages caused by landslides requires a systematic assessment of the detailed properties of the landslide predisposing factors (Drobnjak et al., 2016; Imprialou and Quddus, 2017; Kazemi and Lim, 2005; Orongo, 2011) to obtain detailed landslide susceptibility results at the local scale (roads). In this context, the main goal of this work is to evaluate the influence of the LUC data properties on the landslide susceptibility zonation of road networks. Two specific goals were defined: (i) to evaluate and quantify the landslide susceptibility results using two LUC datasets (CLC 2006 and COS 2007) with different properties (scale and minimum mapping unit) in two landslide susceptibility models; (ii) to use the output results of the two landslide susceptibility models to identify the sections of the main road network with the highest landslide susceptibility that will suffer future road blockages. 2 Materials and methods 2.1 Study area This study was performed in the Zêzere watershed (5063.9 km2) located in the center region of mainland Portugal (Fig. 1). The north-northwest sector of this watershed is occupied by the Serra da Estrela, reaching a maximum elevation of 1993 m, where steep slopes can be found; in the central sector, the relief is less irregular when compared to the previous sector, but it still has steep slope areas (e.g., the vicinity of the Castelo de Bode and Cabril reservoirs); in the south-southwest sector, gentle slopes and flat areas are predominant. The soils of the Zêzere watershed are very variable among the north-northwest, center, and southwest sectors. In the northwest sector, Cambisols predominate, with small areas of Fluvisols and eutric Lithosol along the Zêzere River. In the central area, Lithosols are dominant, with some areas of Cambisols. In the south-southwest sector, there are areas of Lithosols intercalated with Cambisols and Luvisols. According to CLC 2006, the predominant types of LUC in the study area are forest and seminatural areas, which represent 72 % of the watershed area. Other LUC types are less representative, for example, agricultural land (25.5 %), artificialized land–urban areas (1.5 %), and water bodies (1 %), including an important freshwater reservoir, the Castelo de Bode dam (Meneses et al., 2015a). The LUC of this watershed is very dynamic, highlighting the LUCC in forest and agricultural areas derived from multiple socioeconomic driving forces (Meneses et al., 2017) and the degradation of vast forest areas by wildfires (Meneses et al., 2018a). Due to the large extension of this watershed, three sample areas were selected according to the high density of landslides observed in these locations: the Serra da Estrela, Vila de Rei, and Ferreira do Zêzere municipalities (areas of 86.7, 191.5, and 190.4 km2, respectively), where fieldwork was developed to validate part of the landslide inventory and the disruption of roads caused by landslides. 2.2 Data The landslide predisposing factors used to model the landslide susceptibility in the Zêzere watershed were selected after reviewing the literature about the causal factors of landslide occurrence (Blahut et al., 2010; Castella et al., 2007; Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 www.nat-hazards-earth-syst-sci.net/19/471/2019/ B. M. Meneses et al.: Effects of different land use and land cover data 473 Table 1. Properties of LUC data. Properties Land cover maps of Portugal CORINE land cover Acronym COS CLC Scale 1 :25000 1 :100000 Minimum mapping unit 1 ha 25 ha Data structure Vector Vector Geometry Polygons Polygons Minimum distance between lines 20 m 100 m Base data Orthophotos Satellite images Spatial resolution 0.5 m 20 m Nomenclature Hierarchical (five levels) Hierarchical (three levels) 225 classes 44 classes Production method Visual interpretation Semiautomated production and visual interpretation Date of production 2007 2006 Castellanos Abella, 2008; Guzzetti et al., 1999; Reichenbach et al., 2018; Soeters and van Westen, 1996; van Westen et al., 2008; Zêzere et al., 2008, 2017) (Fig. 2). Six fixed landslide predisposing factors were considered: slope angle, slope aspect, slope curvature, slope-over-area ratio (SOAR), soil, and lithology. The LUC types of COS and CLC were used to find the differences in the landslide susceptibility zonation. The set of landslide predisposing factors and the corresponding classes (Fig. 2) were the same in all models, only changing the LUC data. In general terms, an increasing slope angle promotes landslide occurrence and is a very good proxy of the shear stress (Zêzere et al., 2017). Slope instability is more frequent at the higher slope angles of the Serra da Estrela and throughout the Zêzere River valley. Also, in these areas, convex slope curvature is predominantly related to slope instability. The slope aspect is important in the spatial distribution of the different LUC types of the study area (Fig. 2) and in slope instability, especially in northwest-facing slopes (more exposed to rain and with higher humidity levels). The SOAR is a proxy variable of the moisture retention, the soil water content, and the surface saturation zones (Zêzere et al., 2017), highlighting, in the Zêzere watershed, the upstream (very close to the Zêzere River) and southwest areas with a higher SOAR. In the sample areas of the Vila de Rei and Ferreira do Zêzere municipalities, where a high landslide density was observed, schist and metasedimentary lithologies are predominant. Further, slope instability in the watershed is higher in the hortic Luvisols and in the LUC classes of forest and shrubland or herbaceous vegetation associations (Fig. 1). The official LUC data available for the study area are CLC produced by the European Environment Agency (EEA) and COS produced by the General Directorate for Territorial Development (DGT) in Portugal. These LUC data (CLC and COS) have different properties and have been used in several studies about landslides in Portugal (e.g., Guillard and Zêzere, 2012; Meneses et al., 2015b; Piedade et al., 2011; Reis et al., 2003; Zêzere et al., 2017). Table 1 describes the main properties of these LUC data (DGT, 2013; EEA, 2007; IGP, 2010). Among the differences between the two LUC datasets, the scale is highlighted because COS is the most detailed relative to CLC (proportion 1/4). However, the properties are not proportional between the two LUC datasets; while the COS features have a minimum mapping unit of 1 ha, CLC has a minimum mapping unit of 25 ha, and the minimum distance between lines is 20 m in COS, while in CLC it is 100 m. To reduce possible discrepancies in the field, the LUC data were collected for near dates: CLC 2006 and COS 2007. The LUC data were developed with base information that matches in temporal terms, for example, the satellite images, orthophotos, and agricultural and forestry inventories used as auxiliary information. The nomenclature of these LUC data corresponds to the third level (see the official CLC nomenclature on the EEA website). In this study, the second level of the CLC nomenclature was used because it has a lower number of classes for the study area (12 of 31 classes). The agreement among the LUC data is presented in Table 2. The forest class shows great differences between the two LUC datasets. For example, COS represents more forest area relative to CLC (34 % and 26.9 % of the study area, www.nat-hazards-earth-syst-sci.net/19/471/2019/ Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 474 B. M. Meneses et al.: Effects of different land use and land cover data Figure 1. Zêzere watershed and landslide inventory. The pictures represent landslides that affected roads: A, B, C, D, and E – municipality roads of Serra da Estrela; F – Ferreira do Zêzere; G – Vila de Rei. respectively) because a part of COS (approximately 10 % of the study area) is classified as scrub and/or herbaceous vegetation associations in CLC. The reverse was also verified; approximately 5 % of the study area is classified as scrub and/or herbaceous vegetation associations in COS, and this same area is represented by forest class in CLC. These discrepancies are derived from the LUC data properties because COS is more detailed and represents more degraded forest areas, especially where wildfires occurred. These events affected a large percentage of the watershed (Meneses et al., 2018a), especially the central sector, as a vast burned area culminated in a large transition of forest area to shrubland. The forest, scrub and/or herbaceous vegetation associations and open spaces with little or no vegetation are the LUC types predominant in the hillsides with steep slopes (see Tables S1 and S2 in the Supplement). The remaining LUC classes present more area in the lower slopes (>10◦). The soil and lithology data were obtained from the environment atlas web platform published by the Portuguese Environment Agency (APA) at a 1 :1000000 scale. A digital elevation model (DEM) was built using digital topographic maps at a 1 :25000 scale (IGEOE), containing contour lines with 10 m equidistance. Slope angle, slope aspect, slope curvature, and SOAR (topographic wetness index) layers were extracted from the DEM. Road network data (vector lines) were extracted from Portugal’s military cartography (itinerary maps, 1 :500000 scale), available on the Portuguese Army Geospatial Information Center’s website. The road network was classified according to the roads’ width and their network hierarchy. Considering the road center line, a buffer of 5 m was defined for municipal roads, 10 m for complementary roads, and 20 m for superhighways. These distances were measured with geographic information systems (GIS) on the study area roads (directly on the orthophotos). The landslide inventory was obtained using photointerpretation (orthophotos from 2005 and Google Earth images), a process supported by the ancillary topographic data and further fieldwork validation only performed in the sample areas (Fig. 1) due to the extension of the study area. A total of 128 landslides (predominantly shallow translational slides), with a total area of 74 042 m2, were validated during fieldwork in the sample areas (49.4 % of the total inventoried landslide cases). Among the landslides initially inventoried by photointerpretation in the sample areas, more than 90 % of cases Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 www.nat-hazards-earth-syst-sci.net/19/471/2019/ B. M. Meneses et al.: Effects of different land use and land cover data 475 Figure 2. Predisposing factors used in the landslide susceptibility assessment. Predisposing factor map legend. Curvature – Cv: convex; St: straight; Cc: concave. Lithology – A: alluvium; ACLD: arenites, conglomerates, limestones, dolomitic limestone; ACLM: arenites, conglomerates, limestones, dolomitic limestone and marl; ALSC: arenites, limestone, sand, stony banks and clay; CGA: clayey schist, graywackes and arenites; CALD: conglomerates, arenites, limestone, dolomitic limestone, marly limestone and marl; CALM: conglomerates, arenites, white limestone and red marl; G: gabbro; GD: glacial deposits; GS: granite and other stones; GP: granite porphyritic; LDM: limestones, dolomitic limestone, marly limestone and marl; Q: quartzite; RCMD: red sandstone, conglomerates, marl and dolomitic limestones; SG: sands and gravel; SRAC: sands, rocky, arenites and clay; SG: schists and graywackes; SGC: schist and graywacke complex; SAMQ: schists, amphibolite, mica schists, quartzite graywackes, carboned stones and gneisses. Soil – HC: humic Cambisols; R: rankers; DC: dystric Cambisols; DF: dystric Fluvisols; EL: eutric Lithosol; CC: calcic Cambisols; CL: calcic Luvisols; HL: hortic Luvisols; ChC: chromic Cambisols; EC: eutric Cambisols; CcC: calcic–chromic Cambisols; HP: hortic Podzols; EF: eutric Fluvisols. LUC – UF: urban fabric; ICT: industrial, commercial and transport units; MDC: mine, dump and construction sites; ANA: artificial, nonagricultural vegetated areas; AL: arable land; PC: permanent crops; P: pastures; HAA: heterogeneous agricultural areas; F: forests; SHV: scrub and/or herbaceous vegetation associations; OSV: open spaces with little or no vegetation; IW: inland waters. www.nat-hazards-earth-syst-sci.net/19/471/2019/ Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 476 B. M. Meneses et al.: Effects of different land use and land cover data Table 2. LUC data agreement (area in hectares) between CLC and COS classes. Data COS Urban Industrial, Mine, dump, Artificial, Arable Permanent Pastures Heterogeneous Forests Scrub Open Inland fabric commercial, and connonland crops (PC) (P) agricultural (F) and/or spaces waters (UF) and transport struction agricultural (AL) areas (HAA) herbaceous with little (IW) units (ICT) sites (MDC) vegetated vegetation or no areas (ANA) associations vegetation CLC (SHV) (OSV) Total UF 3160.2 439.8 77.3 100.8 207.7 502.0 15.7 929.2 337.7 251.5 0.1 18.7 6 040.7 ICT 134.1 650.4 83.0 9.5 33.4 27.4 9.0 62.5 130.8 207.7 0.3 8.1 1356.1 MDC 6.1 58.3 283.0 0 3.6 3.6 6.8 6.5 48.2 53.5 0.2 5.4 475.0 ANA 29.3 2.9 0 22.5 0 0 0 0 1.7 9.1 0 0 65.6 AL 245.3 171.7 25.0 12.2 9166.1 1304.4 2225.0 1317.1 1133.2 1435.9 51.0 190.7 17 277.5 PC 1271.4 93.3 37.3 21.2 1357.9 7948.5 315.4 2930.0 2004.5 2300.2 7.9 38.1 18 325.7 P 4.4 2.4 0 0 61.3 0.9 36.1 58.4 41.2 188.6 0 0 393.2 HAA 7791.6 736.5 271.4 73.7 11 773.1 15 553.2 2341.0 23 762.4 16 514.4 12 935.5 143.3 243.9 92 140.0 F 745.3 392.9 173.1 29.3 741.9 1715.5 238.1 4058.7 100 486.5 26 805.7 42.0 735.8 136 164.8 SHV 826.5 510.0 259.3 38.0 1353.1 2543.2 958.3 5832.8 50 509.8 149 644.0 4052.8 846.7 217 374.5 OSV 29.4 13.8 5.3 1.4 18.3 10.3 10.7 140.4 860.0 6367.1 4206.6 30.3 11 693.7 IW 5.6 12.0 0 0.2 1.3 7.5 0 15.2 278.5 180.7 2.4 4589.5 5093.0 Total 14 249.1 3084.1 1214.7 308.8 24 717.7 29 616.3 6156.0 39 113.2 172 346.6 200 379.5 8506.6 6707.1 506 399.7 Figure 3. Landslide size frequency distribution. were confirmed. In these sample areas, road disruptions were also validated. For the complete Zêzere watershed, 259 landslides were identified, predominantly of shallow type. Of the total, 32 landslides directly affected the road network (total or partial blockages by the material and seven cases with partial loss of infrastructure). The landslide inventory was randomly divided into two subsets (Fig. 1) (Chung and Fabbri, 2003): the landslide training group and the landslide test group (81.5 % and 18.5 % of the total landslide affected area, respectively). The statistical description of each landslide group is presented in Table 3. The landslide size frequency distribution is different between the landslides that affected the road network and those that did not (Fig. 3). The area of the majority of landslides ranges between 101 and 200 m2, while most of the landslides that affected the road network present a larger area (>1000 m2). All the predisposing factors and landslide inventory were converted to raster (resolution 10 m) to assess the landslide susceptibility. The selection of the predisposing factors’ cell size was based on several geoinformation conversion tests in the Zêzere watershed previously performed by Meneses et al. (2016, 2018b). 2.3 Methods The landslide susceptibility modeling was carried out using the information value (IV) method (Yan, 1988; Yin and Yan, 1988). The IV method is a bivariate statistical method that has been used in several studies and different areas with good results for landslide susceptibility assessment (e.g., Guillard and Zêzere, 2012; Oliveira et al., 2015a; Zêzere et al., 2017). Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 www.nat-hazards-earth-syst-sci.net/19/471/2019/ B. M. Meneses et al.: Effects of different land use and land cover data 477 Table 3. Statistics description of the training group and test group landslide inventories. Training group Test group Non-affected Affected Non-affected Affected Total roads roads roads roads inventory Total landslides 185 26 42 6 259 Total area (m2) 44 604 369 404 10 444 12 089 104 077 Minimum (m2) 134 7 18 82 7 Maximum (m2) 27 364 12 507 1911 5881 12 507 Mean (m2) 2414 1421 249 2015 402 Standard deviation (m2) 3284 2647 304 2627 1069 The IV of each class within each explanatory variable is given by Eq. (1) (Yan, 1988; Yin and Yan, 1988): IVxi=ln Si/Ni S/N ,(1) where IVxiis the IV of the variable xi,Siis the number of terrain units with landslides and the presence of variable xi; Niis the number of terrain units with variable xi,Sis the total number of terrain units with landslides, and Nis the total number of terrain units. The IV method was applied in several landslide susceptibility zonation studies, providing good results (e.g., Che et al., 2012; Chen et al., 2016; Conforti et al., 2012) at the regional scale. This method was also applied in several studies conducted in Portugal, with good performance in susceptibility assessment (e.g., Guillard and Zêzere, 2012; Oliveira et al., 2015b; Pereira et al., 2014; Zêzere et al., 2017). The a priori probability of finding a landslide unit in the study area (S/N) and conditional probabilities for each class of the independent variables (Si/Ni) were calculated, obtaining the IV for these classes. However, the IV method presents constraints on obtaining the natural logarithm for negative results; in this case, the lower value calculated for each variable was assigned to classes when Siwas equal to zero. The IVs of all the variables were combined to obtain the landslide susceptibility map (LSM). For the final landslide susceptibility assessment, i.e., the integration of the IVs of all the independent variables, the following equation was considered: IVj= n X i=0 Xij Ii,(2) where IVjis the total IV of the cell j,Iiis the information value of each cell of each independent variable, nis the number of variables, and Xij assumes the value 1 or 0, depending on the presence or absence of the variable in the terrain unit. Landslide susceptibility model performance was assessed using training landslides. Landslide areas in the test group were only used to perform an independent validation of the landslide susceptibility. Prediction rate curves (PRCs) were computed for each final LSM (Chung and Fabbri, 1999, 2003) and also the area under the curve (AUC). Success rate curves (SRCs) were obtained for the landslide susceptibility road network maps using only the landslides that affected roads. The importance of each independent variable in the landslide susceptibility assessment was also determined, so that the spatial influence of each predisposition factor in the models can be understood. The accountability (AI) and reliability (RI) indexes have been used in different contexts to assess the importance of each independent variable in the bivariate statistical methods (e.g., Blahut et al., 2010; Meneses et al., 2016). AIexplains how different classes of predisposition factors are relevant in the analysis because they contain the landslide area, while RIdepends on the average density of the landslide area in the predisposing factor classes that are more relevant to the development of this process. In this procedure, the AIand RIwere determined using Eqs. (3) and (4), respectively (Blahut et al., 2010). AI= n P i=1 k N100 (3) RI= n P i=1 k n P i=1 y 100 (4) Here kis the landslide area in classes with the conditional probability values higher than a priori probability, Nis the total landslide area, and yis the area of each class of independent variable with a conditional probability above the a priori probability. Two landslide susceptibility models were built using the IV method (see results in Table S3), using the same set of predisposing factors, except the LUC data (Fig. 4): model 1 (M1) was modeled with COS 2007 and resulted in landslide susceptibility map 1 (LSM1); model 2 (M2) was modeled with CLC 2006 and resulted in landslide susceptibility map 2 (LSM2). LSM1 and LSM2 were correlated, and the corresponding spatial agreement was analyzed. www.nat-hazards-earth-syst-sci.net/19/471/2019/ Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 478 B. M. Meneses et al.: Effects of different land use and land cover data Figure 4. Workflow of landslide susceptibility assessment (using different LUC datasets) and the road susceptibility data integration. Information values of LSM1 and LSM2 were assigned to the road network (using GIS), resulting in a road network map with the landslide susceptibility location (landslide susceptibility of the road network – LSRN1 and LSRN2), where there is a higher spatial probability of road interruption or road interference caused by landslides. Different outputs of the two models (road network) were compared using the overall agreement and kappa coefficient (Congalton and Green, 2009), allowing the assessment of the consistency and agreement of the obtained results with different LUC datasets. The information of road disruptions caused by landslides was used to validate these results. Landslide susceptibility maps were built and classified in 10 classes (deciles) containing an equal number of terrain units to allow visual comparison of the results. 3 Results 3.1 Landslide susceptibility The landslide susceptibility results show spatial contrasts in the study area. Some areas in the center of the watershed (highlighting the vicinity of the Castelo de Bode reservoir) and the northern sectors (highlight the Serra da Estrela) present the highest landslide density and susceptibility (Fig. 5). The results of the AIand RIindexes show important differences among the predisposing factors that have been integrated in the landslide susceptibility models (Table 4). The Table 4. Results of the accountability (AI) and reliability (RI) indexes. Factors AIRI Aspect 79.5 0.2 Slope 76.1 0.6 SOAR 13.5 0.7 Soil 62.4 1.0 Lithology 60.6 0.4 Curvature 61.1 0.3 LUC (COS) 82.0 0.3 LUC (CLC) 76.0 0.3 LUC predisposing factors (COS and CLC) registered the highest AIresults, highlighting COS’s LUC types with a higher AI. These results show the relevance of certain classes of COS in the predisposing factor dataset, by the number of landslide areas covered (emphasis on the forests, scrubland, and/or herbaceous vegetation associations, and open spaces with a scarcity or absence of vegetation). The soil, SOAR, and slope angle present the highest values in the case of RI, which shows that landslide density is concentrated in a reduced number of classes of each of the predisposing factor areas (e.g., hortic Luvisols, SOAR [22.5– 25], and slope [between 25 and 45◦]). The landslide susceptibility model’s agreement test was performed using the landslide training inventory used to perform the outputs of each landslide susceptibility model, and these results were validated using the landslide test group. Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 www.nat-hazards-earth-syst-sci.net/19/471/2019/ B. M. Meneses et al.: Effects of different land use and land cover data 479 Figure 5. Landslide susceptibility (IV represented from the highest – red – to the lowest susceptibility – green): the map of Susceptibility M1 represent the results obtained with model 1 (performed with COS data) – LSM1; the map Susceptibility M2 represent the results obtained with model 2 (performed with CLC data) – LSM2. The map on the right is the variation between LSM1 and LSM2. The PRCs of each final susceptibility map (obtained from the results of the landslide training group) show slight variations (Fig. 6), but, in general terms, the curves are identical, demonstrating the high and similar performance of the models in the determination of landslide-susceptible areas. The AUC of LSM1 and LSM2 that includes the same landslide information used to train the models is 94.1 % and 93.9 %, respectively. These results (landslide prediction) were considered to integrate the landslide susceptibility road network (LSRN1 and LSRN2) and the next analyses presented. Additionally, spatial differences were observed in the landslide susceptibility maps (Fig. 5), reflecting the differences of the influence of LUC properties. When the two landslide susceptibility maps are reclassified into two classes (not susceptible IV ≤0 and susceptible IV >0), the susceptible area in LSM1 corresponds to 19.7 % and in LSM2 to 20.8 %. The CLC data provide IV results lower than the IV obtained with the COS data, but CLC is more generalized and justifies that the most susceptible area is observed in LSM2, compared to LSM1. The variation between the maximum and minimum IVs (3 and −3 of 1IV in Fig. 5) show the landslide susceptibility differences derived from spatial representation of LUC classes of the two LUC datasets considered. The highest variations between LSM1 and LSM2 are found in places with reduced IVs (low and moderate susceptibility), marking the central sector of the study area. The areas with the highest IVs in LSM1 and LSM2 present a lower variation. Figure 6. Prediction rate curves (PRCs) of the landslide susceptibility (LSM1 – COS and LSM2 – CLC). 3.2 Landslide susceptibility in the road network Due to the width of the road network, in most cases, these infrastructures are not identified in the LUC data due to the properties or specifications (Table 1), namely, the minimum distance between lines considered in each LUC data in the research. The class “road and rail networks and associated land” (LUC nomenclature, level III) integrates the main class ”industrial, commercial, and transport units” (level II); howwww.nat-hazards-earth-syst-sci.net/19/471/2019/ Nat. Hazards Earth Syst. Sci., 19, 471–487, 2019 486 B. M. Meneses et al.: Effects of different land use and land cover data Meneses, B. M. and Zêzere, J. L.: Modelação da Suscetibilidade e Risco de Movimentos de Vertente no Concelho de Tarouca – Determinação de Rotas de Emergência, in: XIII Coloquio Ibérico de Geografía. 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