Through the recent technological developments of radar
and optical remote sensing in (i) the areas of temporal, spectral, spatial,
and global coverage; (ii) the availability of such images either at a low
cost or free of charge; and (iii) the advancement of tools developed in
image analysis techniques and GIS for spatial data analysis, there is a vast
potential for landslide studies using remote sensing and GIS as tools.
Hence, this study aimed to assess the efficacy of using radar-derived factors (RDFs) in identifying landslide susceptibility using the bivariate
information value method (InfoVal method) and the multivariate multi-criteria
decision analysis based on the analytic hierarchy process statistical analysis.
Using identified landslide causative factors, four landslide prediction
models – bivariate with and without RDFs as well as multivariate with and without RDFs – were generated. Twelve factors such as topographical, hydrological, geological,
land cover and soil plus three RDFs are considered. The weight of index for
landslide susceptibility is calculated by using the landslide failure map, and susceptibility regions are categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With the integration of
RDFs, boundary detection between high- and very-low-susceptibility regions are
increased by 7 % and 4 % respectively.
Introduction
Landslides are one of the major types of geo-hazards in the world as
approximately 9 % of global natural disasters are recorded as landslides
(Chae et al., 2017; Chalkias et al., 2014). The recent statistics on
landslide disasters per continent, from year 2000 to 2017, are summarized in the Emergency Events Database (EM-DAT, 2016). The database indicates that landslides
cause around 16 500 deaths and affect 4.5 million people worldwide, with
property damages of about USD 3.5 million (OFDA/CRED, 2016). The
spatial prediction of landslide disasters, incorporating statistical
analysis to identify areas that are susceptible to future land sliding, is
one the important areas of geo-scientific research. These studies are based
on the knowledge of past landslide events, topographical parameters,
geological attributes, and other possible environmental factors
(Park et al., 2013).
Presently, remote sensing has been used extensively to provide
landslide-specific information for emergency managers and policy makers in
terms of disaster management activities in the world (Baroň et
al., 2014; Martha, 2011). The spatial resolution of space-borne optical data
is now less than 1 m in panchromatic images, and at the same time synthetic
aperture radar (SAR) sensors and related processing techniques have also
increased. Radar is considered to be unique among the remote sensing
systems, as it is all-weather, independent of the time of day, and is able
to penetrate into the objects. Additionally, radar images have been shown to
depend on several natural surface parameters such as the dielectric constant
and surface roughness. The dielectric constant is highly dependent on soil
moisture due to the large difference in dielectric constant between dry soil
and water (Kseneman et al., 2012). It is accepted in the scientific
community that remote sensing techniques do offer an additional tool for
extracting information on the causes of landslides and their occurrences.
Especially for deriving various parameters related to the landslide
predisposing and triggering factors at global and regional scales, remote
sensing plays a vital role (Corominas et al., 2014; Muthu et
al., 2008; Pastonchi et al., 2018). Most importantly, landslide
susceptibility analysis has greatly aided the prediction of future landslide
occurrences, which is important for humans who reside in areas surrounded by
unstable slopes. It is therefore identified that remote sensing techniques
are significant in order to extract the landslide susceptibility regions by
providing most suitable landslide predisposing factors at a smaller scale.
There is massive potential for research by applications in the area of
disaster management if conventional remote sensing data and radar are
integrated. This is because each method has its inherent disadvantages and
shortcomings, as well as advantages, and integrating the two potentially
complement each other. As such, this study combines the predisposing factors
derived from both optical and radar satellite data for landslide
susceptibility analysis. Furthermore, significant landslide predisposing
factors like soil moisture, surface roughness, and forest biomass are
derived from radar images, and the impacts of these factors on landslide
susceptibility are examined.
Methods for landslide susceptibility analysis
There are inherent limitations and uncertainties in landslide susceptibility
analysis, and yet several methods have been utilized and successfully
applied in the past (Kanungo et al., 2009). These methods have
been of both a qualitative and quantitative nature. Generally, qualitative
methods are based on expert opinions while the quantitative approaches, such
as statistical and probabilistic approaches, depend on the past landslide
experiences.
Qualitative methods simply make use of landslide inventories to identify
areas with similar geological and geomorphologic properties that show
susceptibility to land failures. These methods can be divided into two
groups: geomorphologic analysis and map combination. In geomorphologic
analysis, the landslide susceptibility is determined directly either in the
field or by the interpretation of images through geomorphologic analysis
(Bui et al., 2011). Map combination is based on combining a number
of predisposing factor maps for landslide susceptibility analysis. However,
map combination analysis comprises a semi-quantitative nature by
integrating the ranking and weighting of landslide susceptibility (Ayalew
et al., 2004; Kavzoglu et al., 2014; Saaty, 1980). The analyses based on the
quantitative approaches depend on numerical data and statistics, expressing
the relationship between instability or predisposing factors with landslides
(Reis et al., 2012). These methods are categorized into two
groups: bivariate and multivariate statistical analysis. The popular information value method (InfoVal) is used as bivariate and multi-criteria decision analysis (MCDA) based on analytic hierarchy process (AHP) used as multivariate. Within the context of this work, these two methods are compared with respect to its performance in landslide susceptibility analysis.
Landslide predisposing factors
It is understood that landslides may occur as consequences of complex
predisposing and triggering factors. Topographical and geological factors,
together with local climatic conditions, lead to landslide occurrences. The
selection of these factors and preparation of corresponding thematic data
layers are vital for models used in landslide susceptibility analysis
(Jakob et al., 2006; Lee et al., 2017). There are no
universal guidelines regarding the selection of predisposing factors in
landslide susceptibility analysis. Some parameters may be important factors
for landslide occurrences in a certain area but not for another one.
Scientists (van Westen, 1997, 2003; van Westen and Getahun, 2003) show that every study area has its own particular set of
predisposal factors which condition landslides. Determination of appropriate
causal factors is a difficult task, and no specific rule exists to define
how many factors are sufficient for a specific landslide susceptibility
analysis. Hence, the selection of predisposing factors is dependent on the
nature of the study area, opinions of the experts, and the availability of
data for generating the appropriate spatial and thematic information
(Kavzoglu et al., 2015; Shahabi and Hashim, 2015).
Study area
Koslanda in Sri Lanka is located at the geographical coordinates of
06∘44′00′′ N and 81∘01′00′′ E, and the
elevation is around 700–1000 m a.s.l. It is a remote, hilly area with
harsh weather conditions, where the monthly rainfall ranges from 60 to
200 mm, and average temperature is 20 ∘C. The area has rains for most
of the year, with a very short, dry period during the months of February to
April. The population is around 5000 people, and the study area has an
extent of 19 km2 within the Koslanda area. Koslanda has been the site
of several massive landslides over the years, and both the Naketiya
landslide in the year 1997 and Meeriyabedda landslide in the year 2014 are
very distinct in Fig. 1, and within a span of 2 years major landslides
have occurred three times at the same location. When considering the
typology of landslides in this study area, falling, toppling, subsidence,
lateral displacements, and debris flows are prominent (NBRO, 2016).
The geomorphology of the area is described as a gently inclined talus slope,
with a thick, loosely compacted colluvium deposit at the foot of the near-vertical rocky scarp. Koslanda is situated at the middle part of the slope,
with the lower area showing a fairly steep surface as well. The composition
of the colluvium deposit in the area includes a randomly arranged mixture of
weathered clayey and sandy materials, with the organic matter making the
deposit act as a highly absorbing entity with high water content. The study
area was an abandoned tea land in which the properly maintained surface
drainage system has been neglected (Somaratne, 2016). Geology refers
to the physical structure and the substance of the Earth. The study area
consists mainly of undifferentiated charnockitic biotite gneisses and
quartzites, according to the 1:10000 geological map from the Geological
Survey and Mines Bureau (GSMB), Sri Lanka. Such a geomorphological and geological
formation, together with improper land use management practices, has made
the area extremely vulnerable to landslide events.
Data
The most important phases in landslide prediction analyses are the
collection of data from different sources and the construction of a spatial
database on a common platform (Lan et al., 2004). The data
utilized for the landslide susceptibility analysis include the
topographical, hydrological, geological, soil, and land cover factors. All
factors are derived from optical images (Landsat-8, Sentinel-2), radar
images (Sentinel-1, TerraSAR-X), the digital elevation model (DEM) derived from
aerial triangulation, and other available data sources (geology, rainfall).
Stereo aerial photographs from 1993 are used to generate the 7 m resolution
DEM using aerial triangulation (Copernicus Open Access Hub, 2018; USGS Earth Explorer, 2018). The landslide inventory map for the study
area was constructed by integrating the interpreted multi-temporal aerial
photographs, satellite images, and some temporal images from Google
Earth (Fig. 2). Verifications are carried out through field
investigations. In this research, the predisposing factors were selected
from among the most widely considered factors in the literature and opinion from
the experts.
Most data are derived as primary data from remote sensing techniques for a
large area with up-to-date information. As such, 15 predisposing
factors are selected for the landslide susceptibility analysis by using
bivariate and multivariate statistical techniques. Of these, 12 factors
(elevation, slope, aspect, planar curvature, profile curvature,
topographical wetness index (TWI), land use, lineament density, distance to
water bodies, soil moisture, geology, and rainfall) are derived from optical
images, DEM, and auxiliary data, while three more factors (soil moisture from
delta index, surface roughness, and forest biomass) are derived from radar
images. These factors were then combined in order to analyse the performance
of this integration for landslide susceptibility analysis. Additional graphical information is available in Figs. S1, S2, and S3.
Topographical factors
The topographical factors include elevation, slope, aspect, planar
curvature, profile curvature, and surface roughness of the terrain. The first
four factors are derived from the 7 m resolution DEM, and surface roughness
is derived using a Sentinel-1 radar image. The elevation is important to study
the local relief of the terrain and ranges from 446 to 1537 m above mean sea level in the
study area. Since the area contains high mountains, more than a 1000 m
difference in elevation can be observed. The basic parameter for the slope
stability analysis is the slope angle. The slope angle of the study area
ranges from 0∘ to 80∘, showing a significant increase in slope
within a relatively small area. Additionally, the area with steep slopes
ranging from 60∘–80∘ can be seen in the northern part of
Koslanda. Aspect is defined as the direction of maximum slope of the terrain
surface, or the compass direction of a particular slope. The curvature is
theoretically defined as the rate of change of slope (or slope) of the
focused slope. Planar curvature describes convergence and divergence of the
flow across a surface, while the profile curvature refers to acceleration or
deceleration of the flow across a surface.
Under radar configuration, the magnitude of radar backscatter is defined as
a function of surface roughness and moisture content. Similar studies from
Rahman et al. (2008) and Septiadi and Nasution (2009)
emphasized the extraction of surface roughness from radar data using
textural analysis. Hence, to estimate the surface roughness without the use
of any ancillary field data, a Sentinel-1 radar image on 12 March 2015 under dry climatic conditions was used to reduce the effect of the
moisture component from the radar backscatter. The texture is the structure,
or appearance, of the surface and, as such, describes the coarseness or the
homogeneity of the image structure. One of the most prominent methods for
texture analysis is the grey-level co-occurrence matrix (GLCM), which is based
on the second-order probability density function. The GLCM describes how
often a grey level occurs at a pixel located at a fixed geometric position
relative to its neighbourhood pixels. The surface roughness is normally a
measure of finer surface irregularity in the surface texture. These texture
features extracted from the GLCM would be the best descriptors for
quantifying the state of surface roughness (Septiadi and
Nasution, 2009). Hence, the GLCM texture analysis is performed using a
window size of 9 pixels ⋅ 9 pixels, and the homogeneity or dissimilarity criterion is
used to determine the surface roughness of the study area.
Hydrological factors
Distance to hydrological features, rainfall, and TWI defined by Eq. (1) are
selected as the hydrological factors for this landslide susceptibility
analysis. Proximity to the hydrological features is an important factor when
considering the landslide susceptibility analyses (Sar et
al., 2016; Shahabi and Hashim, 2015). TWI is a solid index that is capable
of predicting areas susceptible to saturation or wetness of land surfaces,
as well as the areas that have the potential to produce an overland flow. Within
the Sri Lankan context, heavy and prolonged rainfall is the main triggering
factor for the landslides. The monthly average rainfall data for the years
2014 to 2016 from 10 nearby stations to Koslanda were used in this study.
Monthly rainfall data from 10 rain gauge stations are averaged, and the
average rainfall map for the study area is generated using the inverse
distance weighting (IDW) interpolation method within the ArcGIS environment.
TWI has been used to study the spatial scale effects, or topographic
control, on hydrological processes. This index was developed by Beven and
Kirkby (1979) and can be defined in Eq. (1) as
TWI=ln∝/tanβ,
where ∝ is the local upslope area draining through a
certain point per unit of contour length, and β is the gradient of
the local slope in degrees. The applicability of the TWI in the calculation
and validation of landslide susceptibility analysis has been shown by
Kavzoglu et al. (2014) and Sørensen et al. (2006) among
others.
Soil factors
The soil moisture index (SMI) defined in Eq. (2) and the delta index defined in
Eq. (5) are the soil factors focused upon in this research. Surface soil
moisture is one of the most important parameters in land susceptibility
analysis (Carlson et al., 1994; Zhan et al., 2002). Several
methods have been proposed to estimate the surface soil moisture conditions
accurately with in situ measurements. However, these methods are time-consuming and costly when the area of interest is large and the scale of
work is small. Hence, this research uses the universal triangle relationship
between soil moisture, the normalized difference vegetation index (NDVI), and
land surface temperature (LST) derived from Landsat-8 image bands as an
optical remote sensing approach, as well as the delta index derived from two radar
images, as wet and dry conditions, as a radar remote sensing approach. Band
5 (near-infrared, NIR; 30 m resolution), band 4 (red, 30 m resolution), and
band 11 (thermal, TIR-2, 100 m resolution) of the Landsat-8 image of 3 July 2015 are processed for extracting the soil moisture index in the
thermal-NDVI space. The SMI is “0” along the dry edge and “1” along the wet
edge. According to the studies from Wang and Qu (2009) and Zenga et
al. (2004), SMI can be defined in Eq. (1) as
SMI=Tmax-TTmax-Tmin,
where Tmax and Tmin are the maximum and minimum surface temperature
for a given NDVI, and T is the remotely sensed derived surface temperature
at a given pixel for a given NDVI. The simple regression relationship
between T and NDVI is formulated in Eqs. (3) and (4) as
3Tmax=a1⋅NDVI+b1,4Tmin=a2⋅NDVI+b2,
where a1=-5.2362, b1=300.14, a2=2.9254, and b2=289.11. Radar remote sensing provides advantages for
extracting near-surface soil moisture (0–5 cm), including timely coverage
with repeat passes during day and night, under all weather conditions. Radar
imagery from space can provide broad-scale information on near-surface soil
moisture as radar signal return is responsive to changes in soil moisture.
Technically, the surface roughness and vegetation affect radar backscatter
much more than soil moisture. Hence, both the surface roughness and
vegetation have to remain unchanged during the image acquisition for soil
moisture estimation (Thoma et al., 2006). The delta index is a
modified, image-differencing technique, and many studies (Barrett et al.,
2009; Sano et al., 1998; Thoma et al., 2004) have proven it to be a good
predictor for near-surface soil moisture extraction. This index describes
the change in wet scene backscatter relative to the dry scene backscatter
and is defined by Thoma et al. (2004) in Eq. (5) as
Delta index=σwet0-σdry0σdry0,
where σwet0 is the radar backscatter (decibels) from a pixel
in the radar image representing wet soil conditions, and σdry0
is the radar backscatter (decibels) from a pixel in the same geographic
location representing dry soil conditions at a different time. Sentinel-1
images with 10 m spatial resolution and VV polarization are used in the
presented study. The dry reference image was acquired on 12 March 2015 and the wet image was acquired on 24 November 2014 after the
landslide in Meeriyabedda, Sri Lanka. Therefore, the topographical changes
like roughness and vegetation density showed no significant changes during
these 4 months.
Land use
The major land uses existing in this study area are identified as tea,
scrub, forest, rock, rice, water, and residential. The Sentinel-2A image
from 10 October 2016 is used to extract the desired land uses from
the study area by applying supervised classification. Scrub areas are
typically the tea estates that are in abundance, while the residential areas
are the rooms of tea workers. It is noted that most of the devastating
landslides in this area had occurred within the extensive tea estates.
Hence, the main reason for the continuous occurrence of these landslides can
be identified as the lack of proper land use management in the area.
Forest biomass is a significant factor that can control the landmass
failures or landslides. The main limitations of using optical remote sensing
for forest biomass estimation are the near-constant tropical cloud cover and
the insensitivity of reflectance to change in the biomass in older and mixed
forests. Radar has the potential to overcome the above limitations due to its
all-weather, day and night capability, with the positive relationship of
radar backscatter and forest biomass. The spatial, spectral, temporal, and
polarization characteristics of radar backscatter have a known influence on
the forest biophysical properties. Kuplich et al. (2005) and Caicoya
et al. (2016) related the radar image texture derived from GLCM to
the forest biomass. An experiment was conducted by Kuplich et al. (2005) with seven texture measures, but only the GLCM-derived
contrast increased the correlation between the backscatter and the log of
biomass in Eq. (6) as
Log of biomass=2.24+0.33b+0.0001c,
where b is the radar backscatter and c represents the GLCM contrast texture
for the particular radar image. A TerraSAR-X spotlight image from 2 November 2014, with 3 m resolution and dual polarization (HH and VV), was
used to estimate the forest biomass in this research.
Geological factors
Geology refers to the physical structure and the substance of the Earth. In
order to investigate the land mass failures, the geological structure of
that particular area has to be analysed carefully. In addition to the
geology of the area, lineament density has also been considered as a factor.
The geological information of the particular area is obtained from the
geological map available at the Geological
Survey and Mines Bureau, Sri
Lanka, at a 1:100000 scale, and seven types of different geological structures
are contained in the selected study region. Primarily the undifferentiated
charnockitic biotite gneisses and quartzites are prominent with
garnet-sillimanite and garnetiferous quartzofeldspathic gneiss in the study
area. Lineaments are extractable linear features which are correlated with
the geological structures of the Earth. When considering the analysis of
lineaments with respect to the landslide potentiality, lineaments exhibit
the zones of weak surfaces such as faults, fractures, and joints
(Adiri, et al., 2017; Kati, et al., 2018; Mandal and Maiti, 2015).
This study uses the Sentinel-2 optical satellite image, with 10 m
resolution, for the extraction of lineaments of the study area.
Methodology
The InfoVal method determines the susceptibility at each point or pixel,
jointly considering the weight of influence of all predisposing factors. The
weight of influence is based on the landslide inventory map of the
particular area. When constructing a probability model for landslide
prediction, it is necessary to assume that the landslide occurrence is
determined by landslide-related factors and that future landslides will
also occur under the same, or almost similar, conditions as past landslides
(Remondo et al., 2013; Saha et al., 2005). Hence, at the beginning of the
analysis, the landslide inventory map is divided in to two samples –
training and validation – enabling the use of these data for landslide
susceptibility analysis and validation of results respectively as in Fig. 2.
The log function is used to control the large variation of weights in
calculations. The larger the weight of influence, the stronger the relationship
between landslide occurrence and the given factor's attribute.
This method overlays all individual predisposing factors such as thematic maps
with the landslide inventory map to calculate the density of the landslide
detachment zones for each class of the selected factors. The density of
landslide pixels represents the weight of influence of each predisposing
factor in Eq. (7) as
Wi=LogDensclassDensmap,=LogNpixSi/NpixNi∑i=1nNpixSi/∑i=1nNpixNi,
where Wi is the weight given to the parameter class, Densclass is the
landslide density within the parameter class and Densmap is the landslide
density within the entire map. NpixSi is the number of
landslide pixels within parameter class i, and NpixNi
is the total number of pixels in the same parameter class. It means that, if
the parameter class contains no landslide occurrence, it will have no
correlation with the landslide inventory map (Bui et al., 2011; Kavzoglu et
al., 2015).
The MCDA method integrates all the independent predisposing factors with the
inclusion of relative contribution of each factor by putting more emphasis
on the predisposing factors that contribute to landslide occurrence. The
same predisposing factors with or without radar are used to investigate the
landslide susceptibility regions from the AHP technique within the GIS domain.
In AHP, each pair of factors in a particular factor group is examined at one
time, in terms of their relative importance. Relative weights for each
factor are calculated based on a questionnaire survey from experts in the
field (further information for the questionnaire is available in Fig. S4). These relative weights are then used to generate a pairwise
comparison matrix, which is the basic measurement mode when applying the AHP
procedure. The selected predisposing factors, and relevant relative weights,
are used to generate the normalized matrix with final average weights.
However, expert knowledge could be subjective at times, or may cause one to
assign different weights for each factor, when dealing with a large number
of causative factors. Hence, in order to avoid this inconsistency,
the consistency ratio (CR) is calculated. For better predictive models, the CR
should be less than 0.01, otherwise each factor has to be generated with the
proper pairwise comparison.
The calculated final weights for 12 landslide predisposing factors
without RDFs such as elevation–slope, aspect, planar curvature, profile
curvature, TWI, land use, lineament density, distance to water bodies, soil
moisture, geology, and rainfall were 0.030, 0.172, 0.022, 0.018, 0.014,
0.074, 0.149, 0.052, 0.045, 0.094, 0.185, and 0.145, respectively. The CR is
0.089, making it less than 0.1, which is the value shown to be the reasonable level of consistency in the pairwise comparison. The final weights for 15
predisposing factors with RDFs – elevation, slope, aspect, planar curvature,
profile curvature, TWI, land use, lineament density, distance to water
bodies, SMI in NDVI-T domain, geology, rainfall, soil moisture (delta
index), surface roughness, and forest biomass – are 0.022, 0.145, 0.016,
0.013, 0.011, 0.053, 0.126, 0.039, 0.033, 0.065, 0.153, 0.124, 0.088, 0.088,
and 0.027, respectively. When considering the 15 predisposing factors,
the CR is calculated as 0.092, which is less than 0.1, thereby showing a
realistic level of consistency in the pairwise comparison matrix.
After decisive analysis of the types of predisposing factors, the presented
work proceeded to consider 15 predisposing factors that are derived
from optical, radar, and other available auxiliary data sources. Three
significant causative factors – surface roughness, soil moisture from the delta index, and forest biomass – were estimated by using radar satellite images.
Thus, this work investigated the performance of landslide susceptibility
analysis using bivariate and multivariate methods with the inclusion of RDFs
and described the processing steps in Fig. 3.
Workflow of the landslide susceptibility analysis using bivariate
and multivariate approaches.
The weight of influence of all predisposing factors such as thematic maps is
added in a bivariate and multivariate manner to obtain the contribution of all
predisposing factors for landslide susceptibility analysis. After
calculating the cumulative percentage of failures of the weighted
susceptibility maps, value ranges for each percentage of failure are
obtained from quantile classification for 10 classes. The entire study area
of each landslide susceptibility map is then discretized in to four classes
as 0 %, 10 %, 30 %, and 60 % of failure regions for very-low-, low-,
moderate-, and high-susceptibility classes, respectively.
Results
Four landslide prediction models, (i) bivariate without RDFs (BiNR), (ii) bivariate with RDFs (BiWR), (iii) multivariate without RDFs (MNR), and (iv) multivariate with RDFs (MWR), are discussed. The region has been analysed and
classified into four (04) landslide susceptibility regions: high,
moderate, low, and very low.
Bivariate analysis with and without radar-derived factors
Susceptible regions are identified from the bivariate InfoVal method without
RDFs as 12 % for high, 45 % for moderate, 38 % for low, and 5 % for
very low as shown in Fig. 4a. Hence, 57 % of areas from the total study area
are predicted to have high and moderate susceptibility to landslide
hazards. Very steep slope mountains in the north, north-west, and east
regions are identified as very-low-susceptibility areas, given that the area
was free from historical landslides. The middle regions with
30∘–50∘ slope are detected as having a high probability of
landslide occurrences. The bivariate InfoVal method with RDFs identified
19 % of failure regions for high-susceptibility, 39 % for moderate-susceptibility,
33 % for low-susceptibility, and 9 % for very-low-susceptibility regions as presented in
Fig. 4b. Therefore, 58 % of the total study area is predicted to have
high and moderate susceptibility to landslides. Very steep slope mountains
in the north, north-west, east, and south-east regions, the area near the
Eruwendumpola Oya, are identified as having very low susceptibility to
landslides. Similar to the bivariate analysis without RDFs, the middle
regions with 30∘–50∘ slope are detected as having a high probability of landslide occurrences, and the reason for this is mainly because of past
experiences from the Naketiya and Meeriyabedda landslides that took place in
the same area.
Landslide susceptibility maps from bivariate and multivariate
analysis with and without RDFs. (a) Bivariate without RDFs, (b) bivariate
with RDFs, (c) multivariate without RDFs, and (d) multivariate with RDFs.
Multivariate analysis with and without radar-derived factors
All 15 weighted predisposing factors were grouped as with and without
RDFs, and the weighted overlay is performed separately in order to obtain the
landslide susceptibility regions.
Figure 4c illustrates the landslide susceptibility map from the
multivariate method without RDFs and is able to identify 18 % for high-,
44 % for moderate-, 36 % for low-, and 2 % for very-low-susceptibility
regions. Hence, 62 % of areas from the total study area are predicted to
be of high and moderate susceptibility to landslide hazards. In the
landslide susceptibility map from the multivariate method with RDFs, from the
total area, 21 % of the area shows a high susceptibility to landslides,
with 40 % of the area as moderate, 34 % of the area as low, and 5 % of the area as
having very low susceptibility as shown in Fig. 4d. Hence, 61 % of areas
from the study area are predicted to have high and moderate susceptibility to landslide hazards. In a similar manner to the InfoVal method, the top
of the mountains in the north, north-west, east, and south-east regions,
the area near to the Eruwendumpola Oya, are identified as having a very low
susceptibility to landslide hazards, while the middle regions with
30∘–50∘ slopes are detected as having high and moderate
probability of landslide occurrences.
Discussions
Landslide prediction is of utmost importance in all phases of disaster
management and development activities in a country. In recent years
Koslanda in Sri Lanka has been found to be significantly prone to landslide disasters. Hence, this study investigated the efficacy of radar-derived factors for landslide susceptibility analysis of a bivariate and
multivariate nature. The main difference between bivariate and multivariate
analysis is that in multivariate analysis the predisposing factors are
weighted by considering how each of them affect landslide hazard. Four
landslide susceptibility maps are produced from bivariate and multivariate
analysis with and without radar-derived factors. The areas identified as
having high- and moderate-susceptibility classes in these four approaches
(57 %, 58 %, 62 %, and 61 % respectively in BiNR, BiWR, MNR, and
MWR) are close in value but show an increase in multivariate analysis when
compared with bivariate analysis as tabulated in Table 1. Moderate- and low-landslide-susceptibility areas show very small ((1–2) %) changes between
these four types of analysis. With the integration of RDFs such as surface
roughness, near-surface soil moisture from the delta index, and forest biomass
in bivariate and multivariate analysis, the high- and very-low-susceptibility
areas are increased significantly (high: 7 % – bivariate, 3 % –
multivariate; very low: 4 % – bivariate, 3 % – multivariate).
However, when comparing the high- and very-low-susceptibility areas from
bivariate and multivariate analysis, high-susceptibility areas show a
considerable increase (without radar: 6 %; with radar: 2 %) while
very-low-susceptibility areas have a noteworthy decrease (without radar:
3 %; with radar: 4 %).
Landslide-susceptible area comparison from bivariate and
multivariate analysis with and without RDFs. BiNR – bivariate analysis without
RDFs, BiWR – bivariate analysis with RDFs, MNR – multivariate analysis without
RDFs, and MWR – multivariate analysis with RDFs.
The landslide susceptibility maps derived from the bivariate and
multivariate analysis are validated using the selected validation samples
from the landslide failure map. The most commonly used and scientifically
recognized receiver operating characteristic (ROC) curves are used to
analyse the prediction and validation performances. The ROC curve is a graphical plot
that illustrates the performance of classification and is considered to be a
powerful tool for the validation of landslide susceptibility analysis for
many years (Neuhäuser et al., 2012). The areas under the curve (AUCs)
for the four different approaches – bivariate and multivariate with and without RDFs – are calculated and graphed in Fig. 5.
Success rate and prediction rate curves with AUC for the bivariate
and multivariate analysis with and without RDFs. The x axis denotes the
cumulative percentage of susceptibility regions, and the y axis denotes the
cumulative percentage of training samples. (a–d) BiNR – bivariate analysis without RDFs, BiWR – bivariate analysis with RDFs, MNR –
multivariate analysis without RDFs, and MWR – multivariate analysis with RDFs.
The areas under the success rate curves measure how the landslide prediction
analysis fit with the training data set, while the areas under the
prediction rate curves measure how well the landslide prediction models and
landslide causative factors predict the landslides. If the area under the
ROC curve is closer to 1, the result of the test is excellent and vice
versa, and when AUC is closer to 0.5, the result of the test is fair or
acceptable (Kamp et al., 2008).
The AUCs of all the success rates are more-or-less near 0.80, indicating good
prediction performances according to the definition. The AUCs of all the
prediction rates are having values above 0.50, thereby indicating that they
are within the acceptable range as per the definition. As such, they
indicate that the accuracy of the prediction rate of land susceptibility and the
selection of land causative factors are acceptable, but not excellent, even
though the fit between the landslide prediction and the training data set
is excellent as compared in Table 2. The incompleteness of the available
landslide inventory map, as well as an insufficient number of validation
samples in the study area, can be shown as reasons for the discrepancy. As a
whole, better prediction and validation capabilities are shown by the
bivariate analysis when compared with the multivariate approaches.
Comparison of area under success rate and prediction rate curves
for bivariate analysis without RDFs (BiNR), bivariate analysis with RDFs (BiWR), multivariate
analysis without RDFs (MNR), and multivariate
analysis without RDFs (MWR).
This study focused on the applicability of remote sensing and GIS for rapid
landslide prediction analysis at a finer scale. Furthermore, by considering the
significance of radar data for landslide analysis, this study mainly
investigates the efficacy of radar-derived factors for landslide prediction
analysis, which is not well experimented in the current research. Most
significant predisposing factors such as surface roughness, soil moisture, and
forest biomass derived from radar are incorporated to examine the landslide
prediction analysis. The prediction analysis is performed by using bivariate
and multivariate statistical analysis.
The main difference between bivariate and multivariate analysis is that in
multivariate analysis selected predisposing factors are weighted by
considering how each of them are influenced for landslide susceptibility.
This study investigated 15 landslide predisposing factors: elevation,
slope, aspect, planar curvature, profile curvature, TWI, land use, lineament
density, distance to hydrology, SMI in NDVI-T domain, geology, rainfall,
soil moisture (delta index), surface roughness, and forest biomass. Most of
the factors are derived from radar and optical remote sensing techniques,
where smaller-scale studies with up-to-date information allow the work to
be conducted with metre-level accuracy and repeated analysis
simultaneously.
From the results obtained, it can be concluded that the bivariate and
multivariate statistical analysis, with and without RDFs, can be used for
landslide prediction analysis. However, with the integration of RDFs such as
surface roughness, near-surface soil moisture from the delta index, and forest
biomass, the detection of the boundary between the high- and very-low-susceptibility regions is increased. When comparing the bivariate analysis
with multivariate, the increase of high- and very-low-susceptibility regions
is higher in bivariate than multivariate. In landslide prediction analysis,
the most important susceptibility classes are the high- and very-low-susceptibility classes, as
they provide significant information about the danger from a disaster.
Hence, with the integration of radar-derived factors, by increasing the
accuracy of prediction for high-susceptibility regions, the possibility of
mitigating dangers can be considerably improved. When the accuracy and
prediction of very-low-susceptibility regions are increased, the use of such
lands can be encouraged for residential, community places, and safe areas
when a landslide occurs.
Successful prediction and validation of prediction analysis via ROC curves
are achieved. Even though this study was tested for a sample area, the same
methodology can be applied for any landslide-prone area to investigate the
landslide prediction analysis using radar-derived factors by using bivariate
and multivariate analysis. This is because the radar-derived factors can be
derived for any area, as long as the data are available, and at any time
under any weather conditions as radar is weather independent.
Additionally, the technology can be learned easily and anyone can be trained
to use this methodology to predict landslide susceptibility areas, and this
is especially helpful for developing countries which do not have up-to-date
data at fine resolutions. With the increasing availability of free data from optical sensors, radar sensors, and DEM, it is possible to derive more landslide predisposing factors such as thematic maps. Furthermore, there are many statistical analyses developed of a qualitative and quantitative nature for spatial data analysis. Hence, further investigations have to be performed for landslide
susceptibility analysis, even focusing on the changing nature of the
environments.
Data availability
Landsat-8 data can be freely downloaded from USGS Earth Explorer (2018), https://gisgeography.com/usgs-earth-explorer-download-free-landsat-imagery/.
Sentinel-1 and Sentinel-2A data can be freely downloaded from the Copernicus Open Access Hub (2018), https://scihub.copernicus.eu/dhus/\#/home.
The DEM, predisposing factors, and other auxiliary data can be obtained by contacting the corresponding author by email (nishamanie@geo.sab.ac.lk).
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-19-1881-2019-supplement.
Author contributions
AKRNR performed the conceptualization, data curation, formal
analysis, funding acquisition, investigation, methodology, validation,
visualization, and writing of the original draft; RB supervised the study and reviewed and edited the original draft of the manuscript. UGAP and TLD supervised the study.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors wish to acknowledge the Sabaragamuwa University of Sri Lanka for
offering an opportunity for this research and the HETC project, Ministry of
Higher Education, Sri Lanka, for providing financial support under the grant
number SUSL/O-Geo/N2 as well as the University of Siegen, Germany, for providing
their support in collecting and initial processing of the TerraSAR-X images
from DLR, Germany. The DLR, Germany, is acknowledged with appreciation for
providing radar images free of charge, and the GSMB, Sri Lanka, is acknowledged for
providing the geological data necessary for this research work freely.
Financial support
This research has been supported by the HETC project, Ministry of Higher Education, Sri Lanka (SUSL/O-Geo/N2).
Review statement
This paper was edited by Filippo Catani and reviewed by three anonymous referees.
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