O R I G I N A L P A P E R
Identification of Susceptible Rainfall-Induced Landslide
Areas Based on Field Experiments and Multi-criteria
Analysis in GIS Environment: A Proposal for
Non-inventoried Areas
Leonardo C. Assis.Maria L. Calijuri.Mateus M. Salvador.Jackeline de S. Castro. Carolina F. Carvalho
Received: 4 June 2018 / Accepted: 24 April 2019 Springer Nature Switzerland AG 2019
Abstract The objective of this study is to present a method for identifying zones susceptible to rainfall-induced landslides, based on field experiments in combination with strategic decision analysis through multi-criteria evaluation to obtain landslide suscepti-bility maps for areas where no landslide inventory maps are available. Field experiments were conducted to characterize runoff and water infiltration in different conditions of soil, land cover, and slope, as the occurrence of a combination of these factors is correlated with mass movements. An empirical model was used to transform at-site runoff information to a runoff map (spatial form). Spatial patterns were then utilized in a multi-criteria evaluation procedure, resulting in a digital model showing very low, low, moderate, high, and very high landslide susceptibility zones. Areas of high and very high landslide suscep-tibility corresponded to 17.6% and 8.6% of all areas classified as being at risk, respectively. Together, these classes accounted for 8% of approximately 6600 km2.
We conclude that the use of this methodology allowed the identification of landslides induced by rainfall, and due to the nature of experimental-empirical mod-elling, we recommend that this method is replicated for other conditions.
Keywords Runoff/infiltration field experiment AHP analysis GIS techniques Fuzzy transformation
1 Introduction
Landslides are among the most common natural hazards in many parts of the world (Saadatkhah et al. 2015), and frequently cause damage in moun-tainous regions. Studying these events has been of international interest as awareness of their socioeco-nomic impacts is increasing (Aleotti and Chowdhury
1999). Landslides accounted for approximately 56% of mass movements registered worldwide between April 1903 and January 2013 (EM-DAT 2013). According to Schuster (1996) and Ercanoglu and Gokceoglu (2004), the problem will increase in the future due to unplanned urbanization, continuous deforestation, and increasing rainfall caused by cli-mate change.
Dynamic land and ecosystem processes constantly act on slopes and influence their stability (Sidle and Bogaard2016). Weather conditions characterized by localized rainfall events of high intensity and short
L. C. Assis
Laborato´rio de Geoprocessamento, Universidade de Uberaba, Av. Neneˆ Sabino, 1801, Campus Aeroporto, sala W2, Uberaba, MG 38055-500, Brazil
M. L. Calijuri M. M. Salvador J. S. Castro (&) C. F. Carvalho
Departamento de Engenharia Civil, Universidade Federal de Vic¸osa, Av. P. H. Rolfs, s/n, Edifı´cio CCE – sala 320, Campus UFV, Vic¸osa, MG 36570-000, Brazil
e-mail: jackeline.castro@ufv.br
duration, together with the modifications of the land use and an increase of urban areas, have led to a progressive increase in the frequency and extent of rainfall induced shallow landslides (Valentino et al.
2014). Cho (2014) attributed the occurrence of landslides to a series of factors, such as topography characteristics, vegetation, and climate, or a combi-nation of these factors. According to Pradhan(2010) and Lim and Lee (1992), geomorphological processes that directly influence landslide occurrence, such as erosion and surface runoff, are becoming increasingly prevalent in equatorial zones. These events are enhanced by high rainfall intensity over short periods of time, which promotes soil disaggregation, hillside deterioration, and mass movements (Brunsden and Prior1984; Lim and Lee1992). Additionally, previous landslides may be reactivated after periods of intense and prolonged rainfall (Chowdhury and Flentje2002). Moreover, deforestation in mountainous areas, espe-cially those with slopes over 20, can also cause erosion and landslides (Chan1998a,b). Thus, there is an increasing need for methods that guide managers to choose the best strategies that reduce the impacts of land use activities in vulnerable hillside areas (Gor-sevski et al. 2006). Mapping areas susceptible to landslides is, therefore, essential for decision making and territorial management. Despite the importance of landslide inventory maps to document landslides and provide information for investigating their distribu-tion, types, pattern, and recurrence, and statistics for slope failures to determine landslide susceptibility, hazard, vulnerability, and risk, they are rare (Guzzetti et al.2012).
Support from this type of survey is extremely relevant for civilians (public safety) and agencies that conduct preventive action, aid, and assistance to mitigate the impact of natural disasters and techno-logical incidents on the population, for example, civil defense. Such support is especially valuable for countries like Brazil, who have been suffering from this problem in recent years. Furthermore, preserving soil means conserving water, as well as assuring the possibility of cultivating foods and managing sustain-able forests. Landslide hazard assessments aims to estimate the spatial and temporal probabililty of occurrence of landslides in a study area, together with their mode of propagation, intensity and size (Coromi-nas et al.2014).
As the occurrence of landslides is not attributed to a single factor, several parameters must be assessed and integrated. Geographic information systems (GIS) have recently been used, based on multi-criteria decision analysis (Castellanos Abella and van Westen
2007; Armas 2011; Akgun 2012; Neuhauser et al.
2012; Achour et al.2017; Karim et al.2019). Multi-criteria analysis in GIS is a mathematical tool that allows for comparisons between different alternatives or scenarios according to several, often conflicting, criteria, to guide decision makers (Roy1996). One of the widely used GIS based criteria decision making techniques is known as analytical hierarchy process (AHP) (Saaty 1980; Saaty and Vargas 2001). AHP method provides a flexible and easily understood way of analyzing complex technological problems (Saa-datkhah et al.2015). Therefore, this study presents a method to identify zones susceptible to rainfall-induced landslides based on field experiments in combination with strategic decision analysis through multi-criteria evaluation to produce landslide suscep-tibility maps in areas where no landslide inventory maps are available.
2 Methods and Equipment 2.1 Study Area
This study was conducted in the headwater region of the Doce River, in the Piranga River Watershed, which has a population of approximately 300,000 inhabitants and is located in the state of Minas Gerais, southeast Brazil (Fig.1). It covers an area of 6600 km2between the latitudes of 20160S and 21110S and longitudes of
42420W and 43490W. The relief is strongly
undu-lating, and altitude ranges from 320 to 1450 m. According to the State Water Management Insti-tute—IGAM (2007), most of the soils have low fertility, which, associated with steep hillsides, has favored the development of low aggregate value activities. The main land use/cover classes in this region are pasture (livestock production) and agricul-ture (temporary and permanent crops) (Assis 2012). There are also considerably large eucalyptus-planted areas, which, along with the other classes, comprise the anthropic use of the region. The native vegetation consists of remaining Atlantic forest areas. According to the Ko¨ppen classification, the regional climate is
‘‘tropical of altitude’’, characterized by rainy summers and dry winters. The annual average temperature is 19C, and the annual average precipitation is 1400 mm, according to the National Weather Institute (INMET).
Landslides are frequent in this watershed, and damage areas used for livestock and agriculture, cause interdiction of roads, soil loss, and river siltation, in addition to posing a risk to the population. The most frequent events occur under conditions similar to those shown in Figs. 2a, b. Both figures show high hilltop
vegetation cover, which promotes water infiltration, and very steep hillsides covered with pasture, which are highly vulnerable to runoff.
2.2 Schematic Diagram of the Methodology
Figure3 presents the analytical runoff chart used to identify areas susceptible to landslides, based on field experiments and multi-criteria analysis in a GIS environment. The main processes are highlighted by different colors and are grouped based on similar
Fig. 1 Location of the Doce (blue) and Piranga (green) River watersheds within the state of Minas Gerais, Brazil. * Red dots indicate the experimental sites described in Sect.2.4
characteristics. The rhombus, rectangles, and circles represent operations, raster files, and external data, respectively. Hydrometeorological analyses are pre-sented in blue, orbital remote sensing data and digital processing images in green, field experiment stages for obtaining the runoff coefficient in red, fuzzification of criteria values in orange, the soil type map in brown, and the processes, shown in grey, were used to obtain the constraints. Details of each process in this methodology are discussed throughout this paper. 2.3 Analysis Zone
The occurrence of a landslide in the study area is often the result of two combined situations: (i) good storm water infiltration in hilltops, and (ii) high surface runoff in the adjacent hillsides. Thus, we have limited the analysis zone to areas where both situations were observed.
First, we conducted analysis in GIS to identify hilltops in the watershed. Subsequently, we selected hilltop areas that presented great potential for infiltra-tion, i.e., low slope values and high vegetation cover.
A 200-m buffer surrounding the infiltration areas indicated the analysis. According to the region’s characteristics, this buffer contains all the hillside extensions. The algorithm used to identify hilltops is presented in ‘‘Appendix’’.
2.4 Field Experiment
Field experiments were conducted to characterize runoff in the study area due to its direct relationship with water infiltration into the soil, which is correlated with mass movements.
To measure surface runoff, we installed 16 exper-imental plots (Fig.4a) in four different locations, which were representative of the main soil, slope, and land cover types in the area. The characteristics of these experimental areas are shown in Table1. Additionally, we installed one rain gauge (Fig.4b) in the areas surrounding the plots (i.e., Guaraciaba, Alto Rio Doce, Lamin, and Divine´sia).
The experimental plots were designed to operate autonomously and collect data that represent actual runoff and rainfall conditions.
Fig. 4 Rainfall-runoff experimental plot configuration: runoff on pasture land cover (a); Weather Station for rainfall measurement (b)
Table 1 Characteristics of the experimental plots
Plot location Soil Laud cover Slope (%) C1 NDVI2
Guaraciaba PVAe3 Forest 60.7 0.12 156
Pasture 41.8 0.21 160
Eucalyptus 46.7 – 153
Coffee 29.2 0.13 194
Alto Rio Doce LVAd4 Forest 39.8 0.12 194
Pasture 43.1 0.15 160 Eucalyptus 41.5 0.22 205 Coffee 39.9 0.12 187 Lamin LVd5 Forest 46.1 0.09 177 Pasture 37.5 0.27 162 Eucalyptus 39.7 0.16 175 Coffee 31.1 0.09 152
Divine´sia LVAd Forest 32.3 0.03 199
Pasture 38.0 0.30 141
Eucalyptus 65.3 0.13 186
Coffee 38.4 – 198
1Runoff coefficient;2Normalized Difference Vegetation Index;3PVAe: Typical Eutrophic Red Yellow Argisol, with argillaceous
texture and undulated relief (40%), and well-defined occurrence at the Piranga low region close to its mouth, at North/Northeast (N-NE);4LVas: Typical dystrophic Red Yellow Lactosoil with argillaceous texture and strongly undulated relief (from 35 to 50%) on the Northeast-Southeast axis (NE-SO), undulated/strongly undulated (from 50 to 60%) in the Piranga source, Southwest (SO), and argillaceous/very argillaceous texture with strongly undulated/mountainous relief in the Southeast area;5LVd: Typical dystrophic Red Lactosoil with very argillaceous texture and strongly undulated and undulated/mountainous relief of approximately 30% and 35%, respectively, both occurring in the Northwest (NO) at the source region
2.5 Experimental Plots
The experimental plots were constructed from hand-made metal structures installed over an area of approximately 40 m2, predominantly along the direc-tion of runoff. These structures included a surface runoff catchment area, sediment collection box, and spillway (outlet box) to measure runoff. Rain and recording stage gauges were also installed near the plots. Figure 4shows the configuration of an exper-imental plot, as well as a description of its elements.
To quantify and characterize rainfalls and their respective runoff, we installed four rain gauges near the four locations of the experimental plots, based on land cover type (i.e., pasture, coffee, forest, and eucalyptus).
To quantify the volume of surface runoff, recording stage gauges were installed in each experimental plot. These devices automatically record the water level at pre-established time intervals. Data are stored in a data logger device, and the autonomy of the design is influenced by the logger’s storage capacity.
The recording stage gauges measure total (or absolute) pressure, which is the sum of atmospheric and gauge pressure. The water level in the box was measured by barometric compensation, i.e., subtract-ing atmospheric pressure from the total pressure measured by the recording stage gauge. This proce-dure compensates for natural variations in air pressure and avoids interference in water level measurements, which can affect the quantification of surface runoff.
An additional recording stage gauge was installed at each experimental plot and configured to the same unit and reading frequency as the others. The data recorded were stored in a datalogger device and were recovered monthly by the research team.
2.6 Runoff and Infiltration
The information about infiltration was inferred by in loco experimental plots with real-time measures of rainfall and runoff volumes because when the quan-tities of rainfall depths and the runoff volume are known, the difference is the approximated infiltration volume - disregarding the evapotranspiration. Hence, it its possible to address the infiltration though the runoff because where the runoff is lower, the infiltra-tion is higher and vice versa.
The total volume of runoff was obtained from the sum of the water volume stored in each outlet box during each rainfall event, given that the box dimen-sions and volume drained by the spillway are known. Runoff flow was obtained from the water level measured at the outlet box. Calibration was then conducted to obtain an elevation-discharge curve for the respective spillway.
2.7 Multi-criteria Aggregation in Decision Making
A ‘‘decision’’ is defined as a choice between available alternatives, while ‘‘criterion’’ refers to evidence that can be measured and evaluated, and provide the basis for a decision (Eastman et al. 1995). In a decision-making process based on multi-criteria aggregation, the structure of combined criteria is essential to determine the relationship between them, where an alternative that satisfies all criteria is expected at one extreme, and, at the other extreme, a solution that satisfies at least one of the criteria is desired (Yager
1988). These opposing conditions use the operators and/or, respectively. As an alternative, the aggrega-tion operator, known as the Ordered Weighted Aver-aging (OWA), was presented by Yager (1988).
The OWA operator is not a statistical tool, but a fundamental technique that governs the Multi-Criteria Evaluation (MCE) method, i.e., the decision rule. This has been widely discussed due to its extensive application possibilities (Fulle´r1996).
According to Chiclana et al. (2007), an OWA operator of dimension n is function F : Rn! R, where
R = [0,1], that has a weighting vector W = (W1,…,Wn) associated to it, so that the conditions wi= [0,1] and Pni¼1wi¼ 1 are satisfied, and
aggre-gate a list of values {p1,…,pn} according to Eq.1:
F pð 1; . . .; pnÞ ¼
Xn i¼1
wi pr ið Þ ð1Þ
where r:{1,…,n} ! {1,…,n} is a permutation such that pr ið Þ pr iþ1ð Þ;8i¼ 1; . . .; n 1, i.e., pr(i)is the i-th highest value in i-the set {p1,…,pn}, and represents the ordered criterion.
The weight (W) values are typically determined through the Analytical Hierarchy Process (AHP), as its use in spatial analysis is consolidated in GIS environments (Eastman et al.1995; Vieira et al.2010).
The AHP is a general method of measurement used to produce ratio scales from discrete or continuous paired comparisons (Saaty1987). It is based on a reciprocal square matrix (matrix A) where n criteria are placed in n lines and columns. The values of matrix A refer to the subjective pairwise comparison of the analysis criteria given their relative importance. Thus, any aij value represents the relative importance of alternative i over alternative j. Consequently, aij= 1/ajior aij= 1 when i = j. Weights are obtained through computation of the principal eigenvector of A. However, because A values are subjective comparison measures, it is necessary to assess whether those ratings, with respect to the relative importance of alternatives, are consis-tent. The Consistency Ratio (CR) indicates the prob-ability that the ratings of matrix A were randomly generated (Eastman et al.1995). Saaty (1987) suggests that a tolerance of 10% represents inconsistency, as the priority of consistency to coherently explain a set of facts may differ from the priority of inconsistency, which is an error in the consistency measure. There-fore, in cases where CR exceeds 10%, the ratings must be re-evaluated.
In a GIS environment, MCE is one of the available techniques for spatially analyzing problems that involve multiple themes related to a given subject (Vieira et al.2010). The MCE method is established by a sequence of processes, which can be reviewed in several stages. Initially, themes or criteria are selected for analysis. Due to the different scales upon which criteria are measured, the factors must be standardized and transformed prior to combination. There are several approaches for this, and choosing a procedure depends on the characteristics of data to be standard-ized. Transformation functions offer flexibility for most situations. A common transformation function with that purpose is the monotonically increasing or decreasing sigmoidal function, which sets a degree of relevance between 0 and 1 for each object in the fuzzy set (Zadeh1965).
The sigmoidal function adopts the transformation model given by Eq.2:
f uð Þ ¼ cos2
a ð2Þ
where a¼p
2½ðx xcÞ= xð d xcÞ when the degree of
relevance is monotonically decreasing, or a¼
p
2½1 x xð aÞ= xð b xaÞ if it is monotonically
increasing, and u = 1 when x \ xcor x [ xb; u is the
mapped x value on the new scale [0, 1], x is the value to be scaled; and xa, xb, xc, and xd define the interval limits.
This operation is conducted pixel by pixel for the entire image.
Therefore, the results of MCE analysis are expressed in terms of suitability levels within the [0,1] interval. This interval is often scaled to a 0 to 255 8-bit integer range, which reduces file sizes and, consequently, the computing resources required for data processing and storage.
2.8 Multi-criteria Evaluation
Table 2describes the criteria involved in the analysis. In addition, the weights of each factor, obtained by the AHP technique, are given, where the control points of the gradually increasing monotonic sigmoidal fuzzy function of continuous rainfall (Fig. 7a) and decreas-ing monotonic sigmoidal fuzzy function to surface runoff/infiltration (Fig.8a) were defined by their respective minimum and maximum values. The land cover (Fig. 5a) and soil types (Fig.6a) were reclassi-fied by consultation with experts, deliberation, and the authors’ background knowledge. Surface runoff/infil-tration was considered to be of major importance, given that this variable was determined from field experiments designed to characterize the rainfall-runoff/infiltration patterns of the most common land cover and soil types in the entire basin. All other factors were considered to be moderately less impor-tant than surface runoff/infiltration, and equally important among themselves, as they consist of secondary data, such as land cover maps (Scale 1:150,000), and soil type (Scale 1:650,000) and precipitation data obtained following the data inter-polation method (Table 2). To generate a risk-aver-sion scenario, the ordered weight values used were 0.4 for surface runoff/infiltration and 0.2 for the other criteria.
A variation to the proposal was the integrated analysis methodology with support from field exper-iments under conditions where soil type, land cover-age, and inclination are representative of the study area.
The criteria used in the analysis were adjusted to fit within the same interval of values based on their susceptibility to the occurrence of landslides, exclud-ing the constraints. This phase standardized different
themes expressed at different magnitudes for further comparison. The individual categories of criteria, such as types of soil and land coverage, were reclassified hierarchically according to their susceptibility to landslides. In contrast, the continuous, spatial varia-tion criteria, such as rainfall and surface runoff/
infiltration, were classified by increasing or decreasing fuzzy transformation functions, according to their intrinsic characteristics.
During criteria weighting, more importance was given to the runoff coefficient (i.e., representing runoff/infiltration) as it was a variable obtained from
Table 2 The evaluated criteria and their respective weights for analysis
Criteria Description Fuzzy function Control points Weights a b Factors Soil cover
Land cover map produced by Assis (2012) with an approximate scale of 1:150,000. The categories involved were: secondary forest, pasture, coffee, eucalyptus, and agricultural areas
Scale (0–255) – – 0.1667
Soil type Soil map obtained from the ‘‘Soil survey and agricultural aptitude of the Minas portion’s Doce River watershed’’ published by Fernandes Filho (2010) with a scale of 1:650,000. The types of soil included were: Dystrophic Red-Yellow Latosol (LVAd); Dystrophic Red-Yellow Latosol (LVAd); Dystrophic Red Latosol (LVd); Eutrophic Red-Yellow Argjsol (PVAe); Dystrophic Red Argisol (PVd); And Dystrophic Tb Haplic Cambisol (CXbd)
Scale (0–255) – – 0.1667
Rainfall Rainfall map, which was obtained by the Inverse Distance Weighted (IDW) interpolation method. A historical data series of 15 rainy seasons between 1975 and 2009 were used
Ascending monotonic sigmoid 1214 1658 0.1667 Surface runoff
Map of surface runoff produced from the adjusted model described in Sects.2.9and2.10 Ascending monotonic sigmoid 0 0.24 0.5000 Constraints Urban are
Urban areas were not considered in the analysis as their scale was for the study
Zone of analysis
MCE evaluation was restricted to the Analysis Zone, previously described in Sect.2.7
field experiments and is relevant to rainfall-induced landslide. In contrast, the land coverage, soil types, and rainfall criteria were considered as moderately less important than the runoff coefficient, with equiv-alent levels of importance between them. The relative importance between the criteria was comparatively analyzed through the Analytical Hierarchy Process (AHP), as proposed by Saaty (1987), which is widely used in the spatial weighting of multicriteria. Table3
contains the factors and constraints that comprised the analysis criterion, as well as the defined weights for each item and their respective susceptibility maps to landslides. The consistency ratio obtained from this configuration was equal to zero.
The susceptibility maps for each criterion were created with value intervals from 0 to 255. For easier interpretation, proposed in Akgun (2012), criterion were reclassified into five levels of susceptibility to landslides: very low (0–50), low (51–100), moderate (101–150), high (151–200), and very high (201–255). Figure5b shows the susceptibility map generated according to the influence of each land coverage category on the occurrence of landslides. Urban areas were not considered in the MCE analysis because their
scale was not suitable (i.e., small cities with practically negligible area).
Figure6b shows the susceptibility map according to the degree of influence of the respective soil categories.
Figure7b shows the susceptibility map obtained by increasing the monotonic sigmoid fuzzy function, where the highest susceptibilities are associated to the highest rainfall indexes.
Figure8b shows the susceptibility map obtained from transforming the increasing monotonic sigmoid fuzzy values, where the highest susceptibility values are associated with the lowest surface runoff coefficients.
For composing the constraints, a Boolean intersec-tion was created between the layers: urban area, outside the hydrographic basin limit, and outside the analysis zone. Therefore, areas of less relevance to the landslide issue were excluded from the analysis. The result of this can be seen in Fig.9.
Fig. 6 Soil map (a); susceptibility to landslides based on soil category (b)
Table 3 Model fitting summary statistics
Coefficient Estimate Std. error t-value Pr([ |t|) RSE DF b0 -1.07028 0.09098 - 11.765 6.03e-0.08*** 0.06374 12
b1 9.26735 3.36809 2.752 0.0176*
2.9 Surface Runoff/Infiltration Modeling
Analysis of events with different rainfall intensities showed that the runoff process, represented by the surface runoff/infiltration coefficient (C), was gov-erned by two main variables: slope and land cover. In this study, land cover was represented in the contin-uous space through vegetation intensity, expressed by the Normalized Difference Vegetation Index (NDVI).
The surface runoff/infiltration coefficient was defined as the ratio of the volume of runoff to the volume of rainfall. For each experimental plot, we calculated the average of the C values of different events that occurred during the rainy period (between October 2012 and March 2013). Runoff was not monitored in the eucalyptus region of Guaraciaba, or
Fig. 7 Rainfall map (mm/year) (a); susceptibility to landslides based on rainfall (b)
Fig. 8 Digital surface runoff model (a); Susceptibility to landslides based on surface runoff (b)
the coffee region of Divine´sia, due to a failure in the recording stage gauges.
NDVI is a spectral variable obtained from the ratio of the difference between near infrared and red bands to their sum. NDVI can distinguish between surfaces such as vegetation, soil, and water, in addition to encountering less atmospheric interference.
2.10 Runoff Coefficient Spatial Distribution
An empirical model to associate the experimental runoff/infiltration coefficient with slope obtained in the field and NDVI was fitted, according to Eq.3. Data presented in Table1 were used in the fitting proce-dure, for which we used the Nonlinear Least Squares (NLS) function of the R statistical software (R Development Core Team2011).
^
Ck¼ b0þ S
b1NDVI1ði;j;kÞ
k ð3Þ
where C is the runoff coefficient of the plot; S is the slope value, expressed in percentage; b0 and b1 are coefficients fitted to the model; k is the numerical identifier of the experimental plot; and i and j are the numbers of the lines and columns, respectively, that identify the pixel in the NDVI image.
In Table3, we summarize the statistics of the fitted model. The coefficients were tested at the 0.05 significance level.
To represent the adjusted model for a continuous spatial surface (surface runoff map), the notation matrix for Eq.3was used, as expressed by Eq. 4: Cði;jÞ¼ b0þ SDM
b1NDVIði;jÞ1
ði;jÞ ð4Þ
3 Results and Discussion
Any map has uncertainty because it is an inherent property of mapping. Map overlay operation, common on GIS environment, just increase this uncertainty because of the variance propagation law, a well know concept of cartography. Hence, when multiple map overlay operations takes place such as the MCE method approach demands, this unlikeable feature increases in the final map outcome. Among the different scales of the map we used (i.e., soil, slopes, rainfall depth, NDVI, land cover type), the soil map
has the lower of them, 1:650,000. Hence, the MCE map outcome scale is, at least, at 1:650,000 and the results are valid to attend the same scale which is adequate to represent the 6600 km2of the study area. The multi-criteria analysis produced a digital rainfall-induced landslide susceptibility map scaled to the values interval from 0 to 255 which were then reclassified into five categories (very low, low, moderate, high and very high) to easy interpretation purposes, as mentioned (Fig.10).
High and very high rainfall-induced landslide susceptibility classes (high risk zones) were identified in the upper and central regions of the watershed and these areas correspond to 11.71% and 4.02% of all areas classified as being at risk, respectively. Incli-nometers and/or piezometers should be installed in these zones to monitor landslide movement, as reported by Chowdhury and Flentje (2002). Many of the high-risk zones are located in the headwater region, particularly in the southwestern portion (Fig.11).
A considerable portion of this area most susceptible to landslides is under the influence of land uses for pasture and agriculture (Fig.12). The inadequacy of the agricultural maintenance practices increases the surface water runoff and consequently intensifies erosion processes and instability phenomena (Per-sichillo et al.2017).
Very low, low and moderate rainfall-induced landslide susceptibility classes (low risk zones) were identified mainly in the northeast and southeast of the watershed regions (Fig.13). These areas correspond to 15.67% of the watershed.
A considerable portion of this area less susceptible to landslides is under the influence of land uses intended for pasture, forest and coffee (Fig. 14). In the forest case, the trees are known to be able to stabilise a hillslope. Rooting depth and architecture of the root system mainly depend on species, age, substrate and relief. Coarse tree roots anchor into the underlying soil mantle and give stability to the tree. (Ghestem et al.
2011). Furthermore, forest stands lower the soil moisture content by soil water assimilation of the roots (Schmaltz et al.2017).
The land uses most affected by landslides are those where the soil is more exposed to erosion processes, such as areas of bare vegetation, crops and pastures. (Pellicani et al. 2014). As shown in Fig.5, the watershed is largely covered by pasture. This one is
at different phenological growth stages, which are dependent on the hydrological regime. During drought, soils are more susceptible to sliding due to the loss of green biomass (Canavesi et al. 2013). Another factor that contributes significantly to the occurrence of sliding is the rainfall patterns of the area,
which promote mass movement. Areas less suscepti-ble to slippage are located in the lower region of the basin.
Figure11shows green that represent hilltops with high infiltration potential, and the hydrographic val-leys were not included in the analysis. Most of the
Fig. 10 Landslide susceptibility map
hillsides with high risk of landslides are crossed or bordered by roads. This is a recurring local feature, particularly in the headwaters of the watershed. In
general, areas less susceptible to landslides (very low and low susceptibility classes) do not require imme-diate attention as they do not offer imminent risk to the
Fig. 12 Detail of the landslide susceptibility map under the influence of land use
population. Areas of moderate susceptibility, how-ever, must be evaluated. As soil management practices in the region are not suitable, such areas may evolve to high landslide susceptibility.
In contrast, areas more susceptible to landslides require greater attention, as they impact the economic sector of the affected area and the safety of the population.
Examples of these impacts are the landslides that occurred in January 2011, in the Serrana region of Rio de Janeiro, which killed 9051 people in seven cities, and affected over 300,000 people. Known as the worst Brazilian disaster in terms of human loss, economic losses and damages were also significant, estimated at a total of R$4.8 billion (World Bank 2012). These events are not only common in Brazil, but also in other countries. In Italy, for example, these events are recurrent and abundant (Guzzetti et al.1994; Guzzetti and Tonelli2004), affecting 7.3% of the country. The district of Frosinone, central Italy, experiences the largest number of events in the region, 52.7% of which are in areas of high risk (Trigila et al.2015).
For regions where no inventory information about landslides are available, the present methodology based on real-time field experiment to monitoring of
rainfall and runoff/infiltration conditions to support the MCE GIS analysis has showed promising outcomes.
4 Conclusions
For public safety purposes was proposed a method for identifying areas susceptible to landslides based on field experiments and multi-criteria analysis in GIS. The following advantages are highlighted: (i) the field data recording was designed to operate autonomously in real-time; (ii) the spatial criteria associated with landslides were integrated into a single analysis; (iii) and the landslide susceptibility map was consistent with field observations based on visual inspection and photographic records (no landslide inventory map is available to the region). Areas with greater suscepti-bility to landslides induced by rainfall were associated with intensive cultivation and/or inappropriate soil management. As it is a recurring phenomenon in Brazil and in various parts of the world, the method can and should be used to prevent disasters. Once the identification of areas susceptible to landslides is possible through the present approach, bodies such as
the Civil Defense or Fire Department can act to prepare emergency measures and planning to prevent accidents involving loss of life and resources. The use of the proposed methodology allowed the identifica-tion of areas susceptible to landslides induced by rainfall; and due to the nature of experimental-empirical modelling and the positive outcomes, we recommend to replicate it for other conditions (i.e., environment, climate, and landscape).
Acknowledgements This work was financially supported by the Foundation for Research Support of the Foundation of Research Support of the State of Minas Gerais (FAPEMIG) (grant number CRA - APQ - 05851-09) and the Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel Superior – Brasil (CAPES) – Finance Code 001.
Appendix See Fig.15.
Notes:
• The proposed algorithm has been optimized to run in a watershed;
• The operations to obtain sub-watersheds from DEM vary according to the GIS software used. However, the basic steps involve obtaining the drainage direction and drainage accumulation models. The latter was used to identify drainage channels, employed in sequence in a raster/vector conversion procedure. From the vector streams, we identified confluences located at their respective end points (i.e., sub-basin outlets) and used them as the input parameter to define sub-watershed areas; • For each sub-watershed, we then obtained the
range and maximum values of DEM data; • A conditional operation must be applied to the
elevation range raster to exclude areas valued below the user-defined threshold that specifies the minimum hill height;
Fig. 15 GIS algorithm to identify hilltops from a watershed DEM. Input parameters are presented in blue, operations in green, and the result in orange. Rectangles indicate the raster files, ellipses the numeric input values, and rhombuses the operations
• The elevation ranges of the hills were partitioned according to the analysis criterion for defining a fractional range assumed as hilltop areas;
• A subtraction operation performed on the sub-watersheds between the maximum elevation and the fraction range, which created the contour of the hilltop base for each sub-watershed;
• Finally, the hilltop image was obtained through a Greater Than (GT) logic operation between the DEM and hilltop contours.
References
Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arab J Geosci 10:194–210
Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. Landslides 9(1):93–106
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary, review and new perspectives. Bull Eng Geol Environ 58:21–44
Armas I (2011) An analytic multicriteria hierarchical approach to assess landslide vulnerability. Case study: Cornu village, Subcarpathian Prahova Valley/Romania. Z Geomorpholo-gie 55(2):209–229
Assis LC (2012) Hydrological processes simulation in the river Piranga watershed. 2012. 125 f. Tese (Doutorado em Geotecnia)—Universidade Federal de Vic¸osa, Vic¸osa Brunsden D, Prior DB (eds) (1984) Slope instability. Wiley,
Singapore
Canavesi V, Camarinha PIM, Algarve VR, Carneiro RLC, Alvala´ RCS (2013). Ana´lise da susceptibilidade a deslizamentos de terra: estudo de caso de Paraibuna, SP (in Portuguese). Instituto Nacional de Pesquisas Espaciais— INPE, 2013. Disponı´vel em: http://www.dsr.inpe.br/
sbsr2013/files/p0341.pdf. Acesso em: 04 Sept 2017
Castellanos Abella EA, van Westen CJ (2007) Generation of a landslide risk index map for Cuba using spatial multi-cri-teria evaluation. Landslides 4:311–325
Chan NW (1998a) Environmental hazards associated with hill land development in Penang Island, Malaysia: some rec-ommendations on effective management. Disaster Prev Manag 7(4):305–318
Chan NW (1998b) Responding to landslide hazards in rapidly developing Malaysia: a case of economics versus envi-ronmental protection. Disaster Prev Manag 7(1):14–27 Chiclana F et al (2007) Some induced ordered weighted
aver-aging operators and their use for solving group decision-making problems based on fuzzy preference relations. Eur J Oper Res 182:383–399
Cho SE (2014) Probabilistic stability analysis of rainfall-in-duced landslides considering spatial variability of perme-ability. Eng Geol 171:11–20
Chowdhury R, Flentje P (2002) Uncertainties in rainfall-in-duced landslide hazard. Q J Eng Geol Hydrogeol 35:61–70 Corominas J, Van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263
Eastman JR et al (1995) Raster procedures for multi-crite-ria/multi-objective decisions. Photogramm Eng Remote Sens 61(5):539–547
EM-DAT (2013) The OFDA/CRED international disaster database. Universite Catholique de Louvain.http://www.
em-dat.net. Accessed 20 April 2013
Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75(3&4):229–250
Fernandes Filho EI (2010) Survey of soils and agricultural aptitude of the portion of the Rio Doce basin. Belo Hori-zonte: State Foundation for the Environment (FEAM) (in portuguese)
Fulle´r R (1996) OWA operators in decision making. In: Carls-son Christer (ed) Exploring the limits of support systems, 3rd edn. TUCS General Publication, Turku Centre for Computer Science, pp 85–104
Ghestem M, Sidle RC, Stokes A (2011) The influence of plant root systems on subsurface flow. Implications for slope stability. Bioscience 61:869–879
Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415
Guzzetti F, Tonelli G (2004) Information system on hydrolog-ical and geomorphologhydrolog-ical catastrophes in Italy (SICI): a tool for managing landslide and flood hazards. Nat Hazards Earth Syst Sci 4:213–232
Guzzetti F, Cardinali M, Reichenbach P (1994) The AVI pro-ject: a bibliographical and archive inventory of landslides and floods in Italy. Environ Manag 18:623–633
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66
IGAM (2007) Instituto Mineiro de Gesta˜o das A´ guas. Plano Integrado de Recursos Hı´dricos da Bacia do Rio Doce e dos Planos de Ac¸o˜es de Recursos Hı´dricos para as Unidades de Planejamento e Gesta˜o de Recursos Hı´dricos no Aˆ mbito da Bacia do Rio Doce. Conso´rcio ECOPLAN—LUME Karim Z, Hadji R, Hamed Y (2019) GIS-based approaches for
the landslide susceptibility prediction in Setif Region (NE Algeria). Geotech Geol Eng 37:359–374
Lim RP, Lee SW (eds) (1992) Hill development. In: Proceed-ings of the seminar, Malaysian Nature Society, Kuala Lumpur
Neuhauser B, Damm B, Terhorst B (2012) GIS-based assess-ment of landslide susceptibility on the base of the weights-of-evidence model. Landslides 9(4):511–528
Pellicani R, Westen CJV, Spilotro G (2014) Assessing landslide exposure in areas with limited landslide information. Landslides 11:463–480
Persichillo MG, Bordoni M, Meisina C (2017) The role of land use changes in the distribution of shallow landslides. Sci Total Environ 574:924–937
Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45:1244–1256
R Development Core Team (2011) A language and environment for statistical computing. Vienna
Roy B (1996) Multicriteria methodology for decision aiding. Kluwer Academic Publishers, Dordrecht
Saadatkhah N, Kassim A, Lee LM (2015) Susceptibility assessment of shallow landslides in Hulu Kelang area, Kuala Lumpur, Malaysia using analytical hierarchy pro-cess and frequency ratio. Geotech Geol Eng 33:43–57 Saaty TL (1980) The analytic hierarchy process. Mcgraw-Hill
International, New York
Saaty RW (1987) The analytic hierarchy process—what it is and how it is used. Math Model 9(1):161–176
Saaty TL, Vargas GL (2001) Models, methods, concepts, and applications of the analytic hierarchy process. Kluwer Academic Publisher, Boston
Schmaltz EM, Steger S, Glade T (2017) The influence of forest cover on landslide occurrence explored with spatio-tem-poral information. Geomorphology 290:250–264 Schuster R (1996) Socioeconomic significance of landslides. In:
Turner AK, Schuster RL (eds) Landslides: investigation and mitigation: special report, 247. National Academic Press, Washington, pp 12–36
Sidle RC, Bogaard TA (2016) Dynamic earth system and eco-logical controls of rainfall-initiated landslides. Earth Sci Rev 159:275–291
Trigila A, Iadanza C, Bussettini M, Lastoria B, Barbano A (2015) Dissesto idrogeologico in Italia: pericolosita` e indicatori di rischio. ISPRA—Istituto Superiore per la Protezione e la Ricerca Ambientale
Valentino R, Meisina C, Montrasio L, Losi GL, Zizioli D (2014) Predictive power evaluation of a physically based model for shallow landslides in the area of Oltrepo` Pavese, Northern Italy. Geotech Geol Eng 32:783–805
Vieira CAO, Assis LC, Becker M (2010) Identification of pri-ority zones for conservation considering water resources in the Alto Paraguai Watershed Region. In: Proceedings of the ninth international symposium on spatial accuracy assess in natural resources and environmental sciences. Leicester, pp 433–436
World Bank (2012) Avaliac¸a˜o de Perdas e Danos Inundac¸o˜es e Deslizamentos na Regia˜o Serrana do Rio de Janeiro (in Portuguese)—2011
Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans Syst Man Cybern 8(1):183–190
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
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