Soils and Rocks
Soils and Rocks
Soils and Rocks
www.soilsandrocks.com
ISSN 1980-9743
An International Journal of Geotechnical and Geoenvironmental Engineering ISSN-e 2675-5475
#
Corresponding author. E-mail address: pereira.roberta00@gmail.com
1
Universidade Federal do Rio de Janeiro, Programa de Pós-graduação em Engenharia Civil, Rio de Janeiro, RJ, Brasil.
2
Universidade Federal do Rio de Janeiro, Departamento de Geografia, Rio de Janeiro, RJ, Brasil.
3
Universidade Federal do Rio de Janeiro, Departamento de Geotecnia, Rio de Janeiro, RJ, Brasil.
Submitted on May 15, 2023; Final Acceptance on October 25, 2023; Discussion open until February 28, 2024.
https://doi.org/10.28927/SR.2023.005623
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
The integrated analysis aimed at classifying terrain susceptibility From these three works, in this research the incorporation
involves geological-geotechnical, hydrogeomorphological, of the curvature parameter as another terrain condition
vegetation cover, and land use indicators. The systemic view was proposed. Despite the recognized importance of
of geographic space, based on multiple (spatial and temporal) curvature (plan and profile) and the gradual insertion of this
scales of analysis, is essential for the geo-hydroecological parameter in models of susceptibility zoning, the curvature
approach (Coelho Netto et al., 2020). remains underestimated as a geomorphological parameter
In one of the methodological review and update stages (Catani et al., 2005; Kayastha et al., 2013; Meirelles et al.,
for classifying terrain susceptibility to shallow landslides, in 2018). The present study was developed in the same study
a detailed scale (between 1:5,000 and 1:10,000), the study area used by Coutinho (2015), which allowed the use of some
by Silva et al. (2022) evaluated the relevant geotechnical formerly produced data, applying the necessary adjustments
parameters in the definition of geological-geotechnical units to adapt to the current proposal.
in the D’Antas Creek basin (53 km2), in Nova Friburgo (RJ),
Brazil. The method proposed by the authors was based on the 2.1 Study area
integration of parameters and indices, such as granulometry,
Atterberg limits (LL, PL, PI, A), aggregate stability index, void The D’Antas Creek basin (53 km2) located in Nova
ratio, saturated hydraulic conductivity, c’ (effective cohesion) Friburgo, a municipality in the highland region of the state
φ’ (internal effective friction angle) for the categorization of Rio de Janeiro (Figure 1), was defined as a pilot area
of terrain units according to the mechanical and hydraulic given the occurrence of hundreds of landslides and the high
behavior of the materials. This study enabled the regrouping losses and damages of a social, economic and environmental
of the six lithological units, defined by Avelar et al. (2016), nature in the last catastrophic event in January 2011. Coelho
in three geological-geotechnical units, based on a functional Netto et al. (2013) identified 3,622 landslide scars, in an area
reading of the geotechnical behavior of soils. of 421 km2 (Figure 1b), largely inserted in the municipality
The present work constitutes one more stage of revision of Nova Friburgo and including small areas of Teresópolis
of the methodology developed by GEOHECO-UFRJ/ and Sumidouro municipalities, out of which more than 80%
Geo-Hydro Ecology and Risk Management Laboratory were concentrated in Nova Friburgo.
(COPPETEC/SMAC-RJ, 2000; Coelho Netto et al., Since 2012, GEOHECO-UFRJ has been developing
2007; COPPETEC/SEA -RJ, 2010 and Coutinho, 2015). research on the conditions of shallow translational landslides,
Now, the focus lies on the functional categorization and prevalent in the region. It is a 5th order basin (Strahler, 1952),
improvement of hydro-geomorphological indicators related with altitude varying between 840 m and 2,054 m, according
to the detonation of shallow landslides. An improvement to the Hipsometric model (Coutinho, 2015). The D’Antas
of the tools available in the Geographic Information Creek is a tributary of the Bengalas River, which drains to
System (GIS) used in this study was sought, to obtain the Grande River, a tributary of the Dois Irmãos River. The
parameters, such as slope angle and curvature, which latter runs into the right bank of the Paraíba do Sul River
were more adequate to the analysis. (regional collector).
High-altitude tropical climate predominates in the
2. Materials and methods Região Serrana, with an annual average temperature of
16ºC, varying between 37º and -2ºC (Coelho Netto et al.,
To define the hydro-geomorphological conditions, 2013). The region’s climate is influenced by the Atlantic
the parameters, and indices were integrated and weighted Tropical air mass, which causes high rainfall, especially in
on a functional basis. The latest reviews of these terrain summer. Nova Friburgo is pointed as one of the cities with
conditions, prepared by Coutinho (2015) and Coelho the highest rainfall rates in the state, with average annual
Netto et al. (2014), considered a synthesis of the parameters precipitation (1977–2000) around 2500 mm in the highest
related to the variable set of slopes angles within the areas, decreasing progressively in the northern area down
landslide scars, however, not discriminating the mean to 1,300 mm (Coelho Netto et al., 2008).
slope angle of each scar. The topography position index The region’s characteristic biome is Atlantic Forest,
(TPI) and the drainage efficiency index (DEI) were not currently fragmented and highly degraded. According
changed. The curvature of the slopes was not considered. to Ribeiro et al. (2009), more than 80% of the forest
In the early work by Coelho Netto et al. (2007), the fragments have less than 50 ha and are more than 1 km apart.
hydro-geomorphological map corresponds to the integration The authors claim that these forest remnants occupy from
of the DEI with other functional parameters, including 11.4% to 16% of this region. The vegetation originally
critical slope angles and curvature, combined in the present in the study area is included in the phytogeographic
following classes: concave/0–10°, convex-straight/0–10°, classification of Montana Dense Ombrophilous Forest which,
concave/10–20°; convex–straight/10–20°; concave/20–35°; according to Veloso et al. (1991), covers the mountains and
convex–straight/20–35°; concave/>35°; convex–straight/>35°. plateaus between 500 m and 1,500 m high.
Figure 1. Location map of the D’Antas Creek basin. a) Location of the municipality of Nova Friburgo; b) Location of the D’Antas
Creek basin in the context of the municipality. In red, there are the landslide scars mapped in an area of 421 km2 by Coelho Netto et al.
(2013); c) D’Antas Creek basin and the indication of landslide scars used in the present study.
With the opening and paving of the highway The landslides previously classified as complex
RJ-130, which connects Nova Friburgo to Teresópolis, in (a combination of two or more types of movements), according
the 1970s, a process of transition from rural to urban in to the definition by Varnes (1978), were dismembered to
the basin area began. The region came to be considered highlight the surfaces of ruptures or erosion zone (Figure 2a).
an area of industrial expansion, a fact that accelerated Scars with an elongated shape and incised in the axes of
population growth. Currently, the basin comprises eight concavities were classified as debris flow at their origin
neighborhoods (D’Antas Creek, Alto Floresta, Dois (Figure 2b) and were excluded from this analysis. Scars
Esquilos, Ponte Preta, Cardinot, Solaris, Venda das Pedras related to shapes with a wider base were associated with
and Jardim California), with a population of approximately shallow translational slips (Figure 2c).
23,000 people (11% of the population of the municipality) Considering these new criteria for mapping shallow
(Coutinho, 2015). translational landslide scars, 382 scars with areas between
50 m2 and 60,000 m2 were counted, totaling around 958,220 m2
2.2 Landslide Inventory (0.96 km2) of the landslide area. Despite the great variability
in sizes between the mapped polygons, approximately 73%
Based on the landslide inventory from the January (280) of the scars had an area of up to 2,000 m2 and 51%
2011 extreme rainfall event prepared by Silva et al. (197) an area of up to 1,000 m2.
(2016), there was a new visual interpretation of the scars. Based on the distribution of scars, the landslide potential
Using a Geoeye satellite image of high spatial resolution of each class was defined, as observed in the works by Gao
(0.5 m) obtained shortly after the event (May/2011), the (1993), Larsen & Torres-Sánchez (1998), Catani et al. (2005),
contour lines at a scale of 1:5,000 and images provided Nakileza & Nedala (2020), among others. The landslide
by Google Earth Pro (dated 01/19/2011 and 06/05/2011), inventory was used to validate the hydro-geomorphological
the scars were reclassified with a focus on shallow conditions map, a process through which one sought to
translational landslides and a prioritization of detonation define the reliability, robustness, degree of adjustment, and
and transport zones. forecasting ability of the proposed model.
Figure 2. Types of gravitational mass movements considered in the scar inventory produced by Silva et al. (2016); a) Complex movement,
which was dismembered from erosion and transport zones by the present study, as indicated by the red polygons; b) Debris flows
associated with relief concave compartments, disregarded by this analysis; c) Shallow translational landslide, type of mass movement
in focus (Adapted from Silva et al., 2016).
2.3 Hydro-geomorphological conditions Higher values of DEI tend to favor the rainwater flow of
surface and subsurface, which converges to the topographic
2.3.1 Drainage Efficiency Index (DEI) concavity axis, channeled or not (Coelho Netto et al.,
2007). The increase in drainage efficiency tends to favor the
Data referring to the Drainage Efficiency Index (DEI) incision of erosive channels whose incision and regressive
used in this research were produced by Coutinho (2015). growth can destabilize the steep slope and trigger landslides
The channel network was traced with the support of the (positive feedback). Like the drainage density parameter,
ArcHydro tool (ArcGis 10.2) and through field observations, this index translates the time or response capacity of the
especially in the low hierarchical order sub-basins basin which, together with the topographic gradient of the
(up to 2nd order). The higher hierarchical order basins, as contribution basin, configures the drainage efficiency, as seen
well as the lateral slopes that drain directly into the main in Equation 1 proposal by Coelho Netto et al. (2007), where:
river channels, were adjusted to the adjacency pattern with
the closest neighbors that went through the classification
n n
process (Coutinho, 2015). Initially, this author classified
the numerical products of the DEI as “natural breaks1” into DEI= HDd × G=
∑ L +∑ L
1 h 1 c
×
∆Z
(1)
four classes (very high, high, medium, and low). However, Ab L
in the present study, the option was the use of three DEI
classes: high, medium and low, as proposed by Coelho where: HDd is the Hollow-Drainage density, G is the basin
Netto et al. (2007). gradient (non-dimensional), Lh is the total length of hollow
axis, Lc is the total length of channels, ΔZ is the basin
elevation, L is the basin length and Ab is the basin area.
1
The “natural breaks” method adjusts the limits of the classes
according to the distribution of the data, identifying breakpoints 2.3.2 Slope angle
between the classes from a statistical analysis based on the
variability of the data, aiming to minimize the sum of the variance The slope angle values were extracted from the Digital
within each of the classes (Jenks, 1977). Terrain Model (DTM) (1:5,000), using the Slope tool
(ArcGis 10.7). The generated file was reclassified using the information only within the polygons of scars, the Slope tool
Reclassify tool within the same software. The works by Gao was applied. The generated file was reclassified (Reclassify)
(1993), D’Amato Avanzi et al. (2004), Coelho Netto et al. one by one grade (through the manual classification method)
(2007, 2014), Cevasco et al. (2013), Coutinho (2015) were covering all slope angles recorded inside the scars, which in
used as a base for the definition of these classes. They discuss this case was from 0° to 69°.
the role of slope angle in the detonation of shallow landslides The landslide scars were evaluated individually
and helped define the following class intervals: concerning the mean, maximum, minimum, standard deviation,
i) 0° to 10°: considered as a potential area for deposition; and frequency of distribution of slope values per scar. Mean
ii) 10° to 20°: low potential for landslides, considered slope angle values were assumed to be representative of the
an area of use permitted by law; polygons. Figure 3 shows an example of how the reading of
iii) 20° to 30°: medium landslide potential; area with slope angle values was performed in each scar.
legal restrictions for human use;
iv) 30º to 45º: critical angles of slope stability rupture, 2.3.3 Curvature
that is, with high potential for landslides;
v) > 45º: unstable slopes, generally with thin soil or The curvature of the slopes was also obtained from
rocky cliffs. the DTM, using the Curvature tool (ArcGis 10.7). It was
The files were converted from raster to vector (polygon) decided to work with the standard curvature, due to the
format, using the Raster to polygon tool. To define the intervals greater possibility of understanding the role played by the
that best fit the critical angles of the basin, the DTM was concave portions of the relief in the convergence of superficial
extracted from the landslide scar polygons of January 2011 and shallow subsurface flows and consequent instability of
using the Extract by mask tool. To this file, containing elevation the slopes.
Figure 3. Morphological and topographical analysis of slipping scars. a) Example of scar demarcation with contour line (1:5,000);
b) Example of data analysis referring to the distribution of slope angle values along the scar; c) Adapted model of how data is
processed and presented by the software used (ArcGis 10.7).
Different intervals were tested in search of the best According to Weiss (2000), many physical and biological
adjustment of this parameter to the reality of the study processes that act on the landscape are highly correlated
area. Initially, the tests were based on the values predefined with the topographic position. The TPI becomes relevant
in the literature (Valeriano, 2008; Tagil & Jenness, 2008; for the analysis of slope stability, as it helps to identify the
Bortoloti et al., 2015; Wang et al., 2015; ESRI, 2019; preferential zones for the occurrence of landslides through
Dias et al., 2021b; Nohani et al., 2019), however, none of the automation of relief classification (Tagil & Jenness,
them proved to be adjusted to the study area. 2008; Seif, 2014).
In general, it was observed that the automated data were The data referring to the TPI used in this research were
overestimating the convex-divergent areas and underestimating produced by Coutinho (2015), based on the criterion defined
the rectilinear-planar areas. This problem was also pointed out by Weiss (2000) and the tool proposed by Jenness (2006).
by Valeriano (2008). According to the author, there is a need These data were generated at a scale of 1:5,000, with a spatial
to admit a margin of values greater than zero so that planar resolution of 2.5 m and radii of 12.5 m, 25 m, 50 m, and
slopes can be highlighted. Among the ranges tested, those 100 m, as presented by Coutinho (2015). Adjustments related
that best fit the conditions of the D’Antas Creek basin were: to the radii need to be made based on the local conditions
values < -0.1 associated with concave-convergent slopes, of the study area and the scale of analysis. The results were
values between -0.1 and 3.5 related to rectilinear -planar classified using the “Natural breaks” method into five classes:
slopes, and values > 3.5 assumed as representative of i) Ridge; ii) Upper slope; iii) Middle slope; iv) Lower slope;
convex-divergent slopes. These data were extracted from v) Valley bottom (Coutinho, 2015).
the DTM prior to the occurrence of the landslides. When
necessary, supervised revisions were performed to better fit 2.3.5 Hydro-geomorphological map
the model to the terrain conditions.
The judgment of the suitability of intervals was based Assuming that the forms mirror the processes that
on the mapping of landslide scars about the type of slope gave rise to them, in understanding the geomorphological
curvature, based on visual interpretation. With the aid of a evolution of the landscape, as advocated by Gilbert (1877),
Geoeye satellite image with a high spatial resolution (0.5 m), the hydro-geomorphological conditions listed for this study
contour lines on a scale of 1:5,000, and a visualization scale result from the integration of geomorphological indices and
between 1:5,000 and 1:3,000, this mapping proved to be parameters, including DEI, slope angle, curvature and TPI.
essential to define the intervals of curvature. However, it Figure 4 summarizes the parameters and synthesis indices
is necessary to highlight that threshold adjustments need used to build this mapping and their respective classes.
to be made depending on local conditions, mapping scale, For the integration of the parameters and synthesis
and type of input data. indices that make up the hydro-geomorphological
conditions, weights were assigned to the classes and
2.3.4 Topography Position Index (TPI) maps of each terrain condition according to their erosive
potential. The generated mappings were intersected, using
The TPI has been used to determine the terrain the Intersect tool (ArcGis 10.7), with the landslide scars
susceptibility to landslide, as presented by Tagil & Jenness inventory shape, and the results supported the establishment
(2008), Seif (2014), and Nseka et al. (2019), among others. of weights for each class, based on the AHP method.
Figure 4. Methodological structure of the geo-hydroecological approach for terrain susceptibility zoning, focusing on the parameters
considered relevant for the establishment of Hydro-geomorphological Conditions and the respective classes adopted in this research.
The incidence, which consists of the ratio between the slid It was decided to work with three classes of erosion
area (%) in each class and the area occupied by this class in potential: Low, Medium, and High, as proposed by Coelho
the basin, was used as a support for establishing weights in Netto et al. (2007, 2014). The grouping of classes was
the AHP matrix for paired comparison. initially conceived based on the “Natural breaks”. However,
The Analytical Hierarchical Process (AHP) was integrated when necessary, adjustments were made according to the
into the geo-hydro-ecological approach to help determine the characteristics of the terrain.
criteria and weights of each variable used in the crossings. After the class definition step, the area of the polygons
Using the selected criteria, a paired comparison matrix was generated from the combination of shapes was calculated.
created to represent the relative importance of classes, based All polygons smaller than 200 m2 were merged with neighboring
on the attribution of weights and the establishment of priorities. polygons with a larger area or a longer shared border, using
These weights were determined from the Saaty Fundamental the Eliminate tool. This minimum area value was assumed
Scale, which ranges from 1 to 9, where the value 1 is equivalent after tests had been carried out with all polygons and with
to equal importance between the factors and the value 9, the the elimination of polygons with areas < 100 m2, < 150 m2,
extreme importance of one factor over the other (Saaty, 1994). < 200 m2, < 300 m2, and < 400 m2 (Figure 5).
In addition to the weight attributed to each class and
The shape including all the generated polygons, in
map, the final weight of the classes (Pf) is calculated, which
addition to containing many features (> 287,000), presented
corresponds to the multiplication between the weight of the
polygons with very small areas and not representative for
class (Pclass) and the weight of the map (Pmap), as shown
the analysis, such as the approximately 44,000 features
in Equation 2:
with an area of less than 1m2. The elimination of polygons
with areas < 100 m2 and < 150 m2 showed similar behavior
Pf Pclass × Pmap
= (2) about the distribution of classes, but still with the presence
of unrepresentative polygons for mapping.
The elimination of polygons with an area < 200 m2
The shapes corresponding to the DEI, slope angle, presented a more stimulating result, considering the reduction
curvature, and TPI were grouped using the Union tool
in the number of features (maintenance of approximately 20%
(ArcGis 10.7). The final weight of the classes was added to
of the initial number of polygons) and the maintenance of
obtain the synthesis values, which made it possible to classify
the proportion of areas occupied by each class in the basin
the hydro-geomorphological conditions, as suggested by
when compared to the previous tests. The tests carried out
Coutinho (2015). In this sense, the equation applied to obtain the
with the elimination of polygons < 300 m2 and < 400 m2
values of hydro-geomorphological conditions was as follows:
showed a more significant change in the areas occupied by
each class, based on the overestimation of the high erosion
CH Pf Curvature + Pf DEI + Pf TPI + Pf Slope (3)
Pf = potential class, to the detriment of the others.
Figure 5. Number of polygons contained in each shape and the area occupied by each class of hydro-geomorphological conditions, from
the elimination of polygons depending on the area or maintenance of all polygons generated with the union of the shapes.
Polygons < 200 m2 were also eliminated from the The incidence was used as a support for establishing
curvature, slope angle and TPI shapes. It should be noted weights in the AHP matrix for paired comparison (Table 2).
that, for each area of study and scale of analysis, these An association was sought between the weight of classes
values must be revised. Tests need to be carried out so that and the concentration of landslide area per class (%). For
the elimination of polygons does not lead to an inconsistent this reason, the high DEI class (0.76) assumed the greatest
reading of the generated files. weight, followed by the medium (0.16) and low (0.08)
classes. Figure 6 shows the spatialization of the DEI in the
3. Analysis and results D’Antas Creek basin.
Table 1. Total area in the basin and slid area of each class used in the construction of the Drainage Efficiency Index (DEI).
Class area Landslide area
Classes Incidence
km2 % km2 %
Low 11.16 20.98 0.08 8.37 0.40
Medium 12.05 22.65 0.12 13.00 0.57
High 29.98 56.36 0.75 78.62 1.39
Total 53.19 100 0.96 100 -
Table 2. AHP matrix of paired comparison between Drainage Efficiency Index (DEI) classes.
Classes High Medium Low Weight
High 1 5 9 0.76
Medium 1/5 1 2 0.16
Low 1/9 1/2 1 0.08
Consistency ratio = 0.001 (< 0.10 consistent).
3.2 Slope angle In the analysis of the landslide area per class, the results
assumed the same pattern found with the number of scars.
The results were distributed into five slope angle classes: The highest incidence of the slipped area occurred in the
0°–10° (11%); 10°–20° (24.06%); 20°–30° (33.34%); 30°–45º class from 30° to 45° (1.99), followed by classes 20° to 30°
(27.74%); > 45° (3.85%). Crossing the slope angle classes (1.01), > 45° (0.78), 10°–20° (0 .32), 0°–10° (0.01) as seen
with the landslide inventory showed that 73.04% (279) of in Table 3. Coutinho (2015), when correlating the inventory
the landslide scars had a mean value of slope angle between of landslide scars (n = 244) with the slope angle classes
30° and 45° (Table 3; Figure 7). The second group with (0°–10°; 10°–20°; 20°–35°; > 35°), obtained a concentration
the highest concentration of landslide scars was between of 52.33% of the slid area in the 20°–35° class. However,
20° and 30° (21.47%; n = 82). The classes from 0° to 10°, how the slope angle values were obtained (through polygons
from 10° to 20° and > 45° were less expressive in the analysis representing a class interval) is not representative of the mean
of the mean slopes; when added together, they represented value of the landslide scar.
less than 6% (21) of the total number of scars.
How the mean slope of the scars was read enabled a
more precise definition of the critical angle in the basin.
Although it is difficult to establish precise limits for the
critical slope of so-called unstable slopes, values above 30o
proved to be highly susceptible to the occurrence of shallow
translational landslides in the D’Antas Creek basin. D’Amato
Avanzi et al. (2004) in work carried out in the Cardoso
basin in northwest Tuscany (Italy) indicate that 84.5% (547)
of the assessed landslides occurred on slopes with slope
angles between 31° and 45°, among which 35.6% had a
gradient of inclination from 36° to 40°. Coelho Netto et al.
(2007, 2014), based on Lacerda (1997, 2007) who indicate
the critical friction angle for slopes in southeastern Brazil
to be around 38°, assume as a critical angle slope angle
values greater than 35°.
Cevasco et al. (2013) reported that shallow translational
landslides triggered by episodes of intense rain in mountainous
areas occur predominantly on slopes with slope angles
between 30° and 45°. Steeper slopes generally have little
material available for mobilization. Similar results were
obtained by Fernandes et al. (2004), based on the analysis of
the extreme rainfall event that occurred in the city of Rio de
Janeiro in 1996, on the slopes of the Massif of Tijuca (RJ),
observed a concentration of shallow landslides in the class
between 30º and 55º. In the basins studied by the authors
(Quitite and Papagaio), steeper slopes were associated with Figure 7. Slope angle map indicating the area (%) that each class
shallow soils, which could have been slipped previously, occupies in the basin and the landslide area (%) per class from the
suggesting the existence of a threshold angle of inclination inventory of landslides that occurred during the extreme rainfall
for the triggering of landslides. event in January 2011.
Table 3. Total area in the basin and slid area of each class used in the slope analysis.
Class area Landslide area Distribution of landslide scars
Classes (°) Incidence
km2 % km2 % no. %
0–10 5.85 11.00 0.00 0.08 0.01 4 1.05
10–20 12.80 24.06 0.07 7.81 0.32 8 2.09
20–30 17.73 33.34 0.32 33.77 1.01 82 21.47
30–45 14.76 27.74 0.53 55.33 1.99 279 73.04
> 45 2.05 3.85 0.03 3.01 0.78 9 2.36
Total 53.19 100 0.96 100 - 382 100
The results obtained for the D’Antas Creek basin However, both results indicate a strong correlation between
are in line with those reported for mountainous domains: this slope geometry and the spatial concentration of shallow
extreme rainfall induced landslides tend to occur mainly on landslides. Figure 8 presents the curvature map and the
slopes angles between 30° and 45°. For this reason, in the indication of the percentage of occupied area and landslide
AHP paired comparison matrix, the 30°–45° class (0.54) area in each class.
received the highest weight, followed by the 20°–30° class
(0.21), as can be seen in Table 4. These two classes together
concentrated approximately 90% (0.85 km2) of the landslide
area and 361 (94.50%) landslide scars. Figure 7 presents the
slope angle map with the spatial distribution of the classes
in the basin and the indication of the percentage of area
occupied by each class and landslide area.
3.3 Curvature
Table 5. Area of slope curvature classes, in addition to the slipped area accounted for by the automated mode and number of scars based
on visual interpretation.
Automated Mode Visual Interpretation (image May/2011)
Classes Class area Landslide area
Incidence Number of landslide scars %
km2 % km2 %
Concave-Convergent 34.11 64.13 0.73 75.73 1.18 252 65.97
Convex-Divergent 10.21 19.19 0.10 10.83 0.56 45 11.78
Rectilinear-planar 8.87 16.68 0.13 13.45 0.80 85 22.25
Total 53.19 100 0.96 100 - 382 100
The role played by the concave relief compartments the authors point out that the land use (47.3%–10.9%) and
in the convergence of surface and subsurface flows has the slope gradient (39.6%–14.6%) configure the highest
been attested since the studies by Hack & Goodlett (1960), degrees of susceptibility, followed by lithology (23.7%–8.4%),
Anderson & Burt (1978), Coelho Netto (1985), Dietrich contribution area (30%–1.9%) and curvature (14.4%–2.8%).
& Dunne (1993), among others. Hack & Goodlett (1960) Kayastha et al. (2013), evaluating the Tinau watershed
defined the concavity axes as the wettest part of the slope (western Nepal), also attributed less weight to the curvature
system, being able to present channeled flow, especially during (0.0496) of the slope than to other geomorphological
rainy periods. This convergence of flows contributes to the parameters, such as slope angle (0.1703) and aspect
development of saturation conditions in soils (Anderson & (0.0965). The work by Meirelles et al. (2018), developed
Burt, 1978). Nseka et al. (2019), in studies in the Kigezi region in the Paquequer River basin (Teresópolis-RJ), points to
(southwest Uganda), point out that flow convergence zones the shape of the curvature (13.8%) as the third variable in
(concave shapes), associated with moderately steep slope importance to trigger landslides, preceded by slope angle
angles, medium/low slope, high humidity index and flow (22.7%) and land use (16.3%). In their turn, Catani et al.
power index interact and induce the occurrence of landslides. (2013) highlight the importance of curvature, as the only
Research carried out in the southeastern region of Brazil variable that is not discarded in the scales evaluated for
also state that the concave slopes have a dynamic associated defining susceptibility models.
not only with the occurrence but also with the recurrence of The inclusion of the curvature variable in the construction
landslide on the high slopes and accumulation of materials of the hydro-geomorphological conditions map proved to be
towards the axis of the concavities (Fernandes et al., 2001; relevant. The relationship between the forms and processes
Coelho Netto et al., 2016). Coelho Netto et al. (2016) showed that gave rise to them constitutes an important precept in
this recurrence pattern in a colluvial cone located at the base understanding the geomorphological evolution of the landscape
of a rocky step (height = 10m), in the axis of a concave and (Gilbert,1877). Any alteration in the process will be reflected
steep slope (32º); through a thick sequence of colluviums rich in the forms of the landscape through the readjustment of
in organic matter (3.5 meters), with radiocarbon ages ranging the parameters, categories, or synthesis indices in search
from 8,990 ± 100 years BP (10,374 to 9,779 cal years BP) of a new balance. Changes in the forms also influence the
at the base to 3,860 ± 100 (4,321 to 3,837 cal years BP) in regulation of processes and the way they occur.
a layer close to the surface. Studies in the Massif of Tijuca
(Rio de Janeiro, RJ) by Fernandes et al. (2004) indicate that 3.4 Topography Position Index (TPI)
the concave shape of the slope presents a potential for failure
about three times greater than that obtained in the convex The results were distributed into five TPI classes: ridge
and rectilinear features. (12.07%), upper slope (13.18%); middle slope (48.45%),
The concave-convergent areas were classified in the lower slope (21.05%), and valley bottom (5.24%). The
paired comparison matrix as being highly susceptible to middle slope class concentrated most of the landslide area,
shallow landslide detonation (0.75). The rectilinear-planar about 57% (0.55 km2) of the total, representing an incidence
areas assumed a weight of 0.18 since they are the shapes of 1.18 of the landslide scars; followed by the upper slope
that precede the concavities; therefore, they are subject to (1.04), lower slope (0.87), ridge (0.76) and valley bottom
the occurrence of shallow landslides (Table 6). The convex- (0.29), as shown in Table 7. In Figure 9, it is possible to
divergent areas, in their turn, were given the lowest weight observe the spatialization of the TPI classes in the basin, in
(0.07) as they disperse water flows, favoring lower pore addition to the landslide area.
pressures and terrain stability. Coutinho (2015) also attributed a higher concentration
The complexity associated with establishing thresholds of landslides to the middle slope (49.58%) for the D’Antas
adjusted to the study area may explain the low weights Creek basin. Studies conducted by Coelho Netto et al. (2014),
attributed to curvature in landslide susceptibility models. in the central area of Angra dos Reis, also indicate that areas
Catani et al. (2005), in their landslide susceptibility model with middle slopes, associated with slope angles greater
(Arno River basin, Italy), state that the curvature, together than 35°, are the most susceptible to landslides. Nseka et al.
with lithology and slope gradient, influences the volume and (2019) also point out that in southeastern Uganda landslides
velocity of landslides. However, in the five evaluated areas, are also concentrated (58%) on the middle slope.
Table 6. AHP matrix of paired comparison between the standard curvature classes.
Classes Concave-convergent Rectilinear-planar Convex-divergent Weight
Concave-convergent 1 3 7 0.75
Rectilinear-planar 1/3 1 3 0.18
Convex-divergent 1/7 1/3 1 0.07
Consistency ratio = 0.03 (< 0.10 consistent).
Although the landslides inventory used for crossing with geomorphological conditions (Table 9). It is an index that
the terrain conditioning parameters focused on the erosive synthesizes a set of parameters from a functional reading
domain of the scar and did not consider the depositional of the landscape concerning the concentration and direction
part (< 10°), there was a concentration of landslides in the of flows and humidity on the slopes, which influence the
lower slope class. Regarding this point, Coelho Netto et al. potential for detonation of landslides.
(2012) call attention to the variation of the geomorphological
aspects involved in the rainfall events of 1996 and 2010,
which occurred in the city of Rio de Janeiro. While in 1996
the landslides were concentrated from the ridge zone and
intermediate interfluves to the valley bottoms, in the 2010
event, detonation prevailed on middle and lower slopes
(approximately 60%) (Coelho Netto et al., 2012).
The authors showed that the detonation of landslides
can occur in topographically different positions depending
on the characteristics of the rainfall event. While in 1996
landslides were associated with steep and elongated slopes,
which favored the propagation of mobilized materials and
convergence towards valley bottoms, feeding debris flows;
in the 2010 event, the high number of occasional landslides
(45%) associated with the roads stands out, whether due to
high and steep cuts or drainage outlets close to the road’s
curvatures (Coelho Netto et al., 2012).
These results guided the attribution of weights to the
topography position classes (Table 8). The middle slope
assumed greater importance (0.54), followed by the upper
slope (0.24) and lower slope (0.13) classes. Ridge (0.06)
and valley bottom (0.03) had the lowest weights in the
paired comparison matrix, due to their little influence on
the triggering of shallow landslides.
3.5 Hydro-Geomorphological Map Figure 9. Topography position index map indicating the area (%)
that each class occupies in the basin (adapted from Coutinho, 2015)
A synthesis analysis was carried out, attributing and the landslide area (%) per class from the inventory of landslides
the DEI (0.38) the greatest importance among the hydro- that occurred during the extreme rainfall event in January 2011.
Table 7. Total area in the basin and slid area of each class used in the construction of the Topography Position Index (TPI).
Class area Landslide area
Classes Incidence
km2 % km2 %
Ridge 6.42 12.07 0.09 9.20 0.76
Upper slope 7.01 13.18 0.13 13.72 1.04
Middle slope 25.77 48.45 0.55 57.15 1.18
Lower slope 11.20 21.05 0.18 18.42 0.87
Valley Bottom 2.79 5.24 0.01 1.50 0.29
Total 53.19 100 0.96 100 -
Table 8. AHP matrix of paired comparison between Topography Position Index classes.
Classes Middle slope Upper slope Lower slope Ridge Valley Bottom Weight
Middle slope 1 4 6 8 9 0.54
Upper slope 1/4 1 3 5 7 0.24
Lower slope 1/6 1/3 1 3 5 0.13
Ridge 1/8 1/5 1/3 1 3 0.06
Valley Bottom 1/9 1/7 1/5 1/3 1 0.03
Consistency ratio = 0.071 (< 0.10 consistent).
The slope angle (0.27), which is normally assigned Figure 10 provides an integrated view of the parameters
as the main geomorphological parameter in the analysis of and synthesis indexes that were used to build the hydro-
terrain susceptibility (Dias et al., 2021a), assumed a similar geomorphological conditions map.
weight to the curvature (0.22), as both reflect the shape of The results obtained highlight the importance of using
the slope, based on the gravitational component and the parameters and synthesis indexes of a geomorphological
concentration of water flows, respectively (Table 9). The TPI nature in the analysis and classification of the terrain
was assigned a lower weight (0.13) because it constitutes an susceptibility. Digital Terrain Models and derived datasets
approximate measure and delimitation, with a greater degree (slope angle, curvature, aspect, surface area, hydrographic
of generalization of the information. pattern, among others) have been highly exploited to obtain
The terrain conditions adopted and the weights assigned geomorphological parameters. However, these parameters
to each of them proved to be adjusted to the conditions of the need to be inserted in the mappings functionally; that is,
basin. The data obtained from the inclusion of the curvature allowing one to understand how the processes contribute to
in the definition of the erosion potential of the study area the evolution of the landscape and to evaluate how the forms
were promising, since 88.74% (0.85 km2) of the landslide condition the processes at different spatial scales.
area was associated with the class of high erosive potential It is also important to point out that for each model,
(Table 10), against 80.05% (0.77 km2) of landslide area at the values (or ranges of values) of the input parameters
the intersection between slope angle, DEI, and TPI, without should be adjusted to allow agreement between field
including slope curvature (Table 11). observations and automated operations. When adjusted
The medium class (22.88%) of erosive potential to the conditions of the study area and scale of analysis,
concentrated 7.86% (0.08 km2) of the landslide area, the geomorphological parameters are highly promising
while the low class (21.03%) erosion potential presented for the classification of terrain susceptibility to shallow
a concentration of 3.40% (0. 03 km2) of the landslide area. landslides.
Table 10. Total area in the basin and landslide area of each class used in the construction of the map of hydro-geomorphological conditions.
DEI, Slope angle, Curvature, TPI
Class Area Landslide Area
Classes Incidence
km2 % km2 %
Low (0.05795–0.188692) 11.18 21.03 0.03 3.40 0.16
Medium (0.188693–0.376064) 12.17 22.88 0.08 7.86 0.34
High (0.376065–0.662033) 29.84 56.10 0.85 88.74 1.58
Total 53.19 100 0.96 100 -
Table 11. Mapping of hydro-geomorphological conditions without using the curvature parameter.
DEI, Slope angle, TPI
Class Area Landslide Area
Classes Incidence
km 2
% km 2
%
Low (0.034660–0.142448) 11.66 21.91 0.04 4.01 0.18
Medium (0.142449–0.263714) 17.9 33.64 0.15 15.94 0.47
High (0.263715–0.513428) 23.64 44.44 0.77 80.05 1.80
Total 11.66 21.91 0.04 4.01 -
Authors’ contributions Catani, F., Casagli, N., Ermini, L., Righini, G., & Menduni, G.
(2005). Landslide hazard and risk mapping at catchment
Roberta Pereira da Silva: conceptualization, methodology, scale in the Arno River basin. Landslides, 2, 329-342.
formal analysis, writing – original draft. Ana Luiza Coelho http://dx.doi.org/10.1007%2Fs10346-005-0021-0.
Netto: conceptualization, methodology, formal analysis, Catani, F., Lagomarsino, D., Segoni, S., & Tofani, V.
supervision, writing – review & editing. Willy Alvarenga (2013). Landslide susceptibility estimation by random
Lacerda: conceptualization, methodology, formal analysis, forests technique: sensitivity and scaling issues. Natural
supervision, writing – review & editing. Hazards and Earth System Sciences, 13(11), 2815-2831.
http://dx.doi.org/10.5194/nhess-13-2815-2013.
Data availability Cevasco, A., Pepe, G., & Brandolini, P. (2013). The influences
of geological and land use settings on shallow landslides
The data generated and used for the analyzes presented triggered by an intense rainfall event in a coastal terraced
throughout this article are available for scientific use upon environment. Bulletin of Engineering Geology and the
request to the authors. Environment, 73(3), 859-875. http://dx.doi.org/10.1007/
s10064-013-0544-x.
Coelho Netto, A.L. (1985). Surface hydrology and soil
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