Landslide Susceptibility Mapping using Frequency Ratio, a case study of Vythiriblock in Wayanad, the northern part of Kerala, India
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Disaster Advances Vol. 15 (1) January (2022)
Landslide Susceptibility Mapping using Frequency
Ratio, a case study of Vythiriblock in Wayanad,
the northern part of Kerala, India
Vinayan Midhuna, Grurugnanam B.* and Bairavi S.
Centre for Applied Geology, Gandhigram Rural Institute Deemed to be University, Dindigul, Tamilnadu, INDIA
*gurugis4u@gmail.com
Abstract structure, geomorphology, rock type, drainage, slope,
Landslides are common and disastrous events in vegetation, soil cover and land use/land cover are the natural
India's Western Ghats during the rainy season and factors that impact landslides. Anthropogenic factors are
various factors influence the occurrence of landslides. urbanization, deforestation, road construction, heavy vehicle
movement and mining etc. Generally, landslides are
The present research aims to establish landslide
classified into two types: one is based on the type of
susceptibility zones to control landslide hazards and movement (slides, topples or fall and flows) and the other
risk effectively. Landslides occurred in the entire area one is based on the material (debris or earth materials and
of the Wayanad district. The study was carried out in rock). The Himalayas, the Western Ghats and the Nilgiris are
vythiri taluk to do detailed surveys using remote the landslide-prone area in India. 14 These are severe
sensing and GIS, GPS and field investigation. A total landslides who can also alter topography, especially in
of 162 landslide locations are used to develop a mountainous regions, modify the course and pattern of
landslide susceptibility map of this area. Landslide rivers.19
susceptibility map of this area was done by using the
frequency ratio (FR) model. Landslides may occur everywhere in the world. During the
monsoon season, landslides are common in the Himalayas
and the Western Ghats. These landslides are mainly
The landslide susceptibility map is based on factors triggered by intense rainfall and earthquake. According to
that affect directly or indirectly land subsidence Studies, the Western Ghats of India are most vulnerable to
process. There are twelve landslide influencing factors landslide occurrences followed by the Himalayas. 18 In India,
taken for this study: Slope, Aspect, Land use/land some of the enormous and catastrophic landslides occurred
cover, Rainfall, Vegetation, Soil, Distance to in the last three decades: In July 1991, 300 people were
lineament, Distance to the road, Elevation, Curvature, killed, roads and buildings were damaged in Assam. In July
Geomorphology and Lithology. The resulting landslide 1993, 25 people were buried in Itanagar. In August 1993,
susceptibility map shows that 3.6 % of the region is in 500 people died and 200 houses collapsed in Nagaland. In
a low susceptible zone, 40.5 % is in a moderate August 1998, 69 people were killed in Okhimath. In
susceptible zone, 49 % is in a high susceptible zone and November 2001, 40 people were killed in Amboori, Kerala.
37 On 26th July 2005, more than 74 people died in Sakinaka,
6.9 % is in a very high susceptible zone to landslides.
Mumbai; on the same day, 54 people were killed in Raigad.
According to this model, the area under the curve On 27th July 2007, more than 50 casualties were reported in
(AUC) for the success rate is 75.3 %. This study would Dasalgaon, Maharashtra. On 24th September 2012, 27 people
support the relevant authorities in identifying potential died in Sikkim. On 6th June 2013, 5,700 casualties were
risks in particular parts which could be used for reported in Kedarnath, Uttarakhand (https://www.dnaindia.
landslide mitigation activities as construction com/india/report-india-s-worst-landslides-and-why-these-
development in the region increases. might-not-be-the-last-ones-2006836).
Keywords: Landslides, Vulnerability, Susceptibility, In Kttippara, Kerala June 2018, 14 people died (https://
Frequency ratio, Receiver Operating Characteristic. english.mathrubhumi.com/news/kerala/kattippara-landslide
-one-more-body-recovered-death-toll-reaches-14-kerala-1.2
Introduction 899498). In Puthumala, Kerala on 8th August 2019, 17
Landslides are sudden and deadly natural hazards when a people were killed. On the same day in Kavalappara, Kerala,
mountain's slope changes from stable to unstable.8 The in August 2019, 59 people were killed and in Pettimudi,
landslide is a severe natural disaster that can be disastrous to Kerala, in August 2020, 63 people died
human life and property. 39 It is the downslope movement of (https://www.onmanorama.com/news/kerala/2019/08/20/ka
earth materials along the steep or gentle slopes due to valappara-puthumala-landslide-reasons-trigger.html).
gravity. It is created by a combination of both natural and
anthropogenic causes. Heavy rainfall, earthquake, lithology, Between 2018 and 2020, the number of landslide
occurrences in Vythiri taluk increased significantly. Since
* Author for Correspondence 2018, the number of people who have lost their life and
property has increased. Monitoring the distribution and
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Disaster Advances Vol. 15 (1) January (2022)
frequency of historical landslides helps to identify the from Indian Meteorological Department (IMD).
regions at high risk for landslides.34 Remote sensing and GIS Geomorphology, soil, distance to lineament and distance to
provide various applications for the Landslide susceptibility the road map of this area were extracted from Bhukosh (GSI)
mapping (LSM) studies.3-5,11,12,34 Landslide factor (https://bhukosh.gsi.gov.in/Bhukosh/Public). Landslides
identification and inventory mapping are easily made with were mapped using images from Sentinel-2, Landsat 8,
remote sensing images.34 Several researchers have attempted landslide points from GSI websource (https://
to quantify landslide susceptibility employing innovative bhukosh.gsi.gov.in/Bhukosh/Public) as well as field visits.
methodologies and GIS applications.
Landslide influencing factors: The present research work
Study area consists of twelve landslide influencing factors such as
The selected area of the present study is Vythiri taluk, Slope, Aspect, Land use/land cover, Rainfall, Vegetation,
Wayanad district, northern Kerala, India (Fig. 1), Soil, Distance to lineament, Distance to the road, Elevation,
geographically located from 11°32′59″N latitude and Curvature, Geomorphology and Lithology. They were
76°02′11″E longitude and altitude is 700 meters above sea measured in the field for Landslide Susceptibility Mapping
level with a total area of around 616 km2. This region (LSM) of the study area. The effect of each factor on
consists of 18 villages, a small town and a popular tourist landslides in this area is used to compute the frequency ratio
destination on the Western Ghats. Mountain ranges, hills, of each factor (Table 1).
valleys and plains are the major landforms situated in this
area. The average annual rainfall is about 3840 mm. The Slope: The slope is a significant influencing factor in
region is well-known for its high-yielding crops like tea, landslides and it is frequently used to create a susceptibility
coffee, pepper and others. The majority of the area is covered map. 2,14,20,30 The majority of landslides on slopes is caused
with charnockite and granitic rocks. by the direct or indirect effects of factors such as rainfall,
hydrology, geology, lithology, land use/ land cover.9,23 The
Material and Methods slope map was created using SRTM DEM data in a GIS
The surface slope, aspect, elevation and curvature of the environment and categorized into five equal classes: very
research region were determined using the Shuttle Radar low, low, moderate, high and very high10 (Fig.2).
Topography Mission (SRTM) generated Digital Elevation
Model (DEM) by NASA and NGA, with a 30 m spatial Slope Aspect: Aspect does not directly influence the
resolution. Sentinel 2 imagery with a spatial resolution of 10 occurrence of landslides; it just indicates the slope's
m was used to create vegetation maps. Land use/land cover orientation. The aspect map of this area was prepared by
map was created using Landsat 8 imagery with a spatial using SRTM DEM data. The aspect map is categorized into
resolution of 30 m. The annual rainfall data was collected nine classes: flat, north, northeast, east, southeast, south,
southwest, west and northwest (Fig. 3).
Fig. 1: Location map of the study area
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Disaster Advances Vol. 15 (1) January (2022)
Table 1
Spatial relationship between each factor and landslide and frequency ratio values
Factors Classes Number Number of Number Number of Frequency Normalization
of pixel pixel in of landslide Ratio (FR)
in class class(%) (a) landslide pixel in (a/b)
pixel in class(%)(b)
class
Slope Very low 51 31.48 348953 53.40 0.58 0.37
Low 92 56.79 233092 35.67 1.59 1
Moderate 19 11.7284 65531 10.02 1.16 0.73
High 0 0.00 5616 0.85 0.00 0.00
Very high 0 0.00 241 0.03 0.00 0.00
Aspect Flat 24 14.81 88825 13.48 1.09 0.72
North 28 17.28 84835 12.87 1.34 0.88
Land use/ Agriculture 65 40.12 13820 37.87 1.05 0.91
Land cover land 3.70 1167 3.19 1.15 1
Bare land 6 36.41 14443 39.58 0.92 0.79
Forest land 59 19.75 6388 17.50 1.12 0.97
Urban land 32 0.00 688 1.83 0.00 0.00
Waterbody 0
Rainfall 1-1.875 6 3.70 2710 7.65 0.48 0.17
1.875-2.75 22 13.58 3563 10.06 1.34 0.49
2.75-3.625 20 12.34 4268 12.05 1.02 0.37
3.625-4.500 9 5.55 3426 9.67 0.57 0.21
4.500-5.375 15 9.25 2871 8.10 1.14 0.41
5.375-6.250 21 12.96 3219 9.09 1.42 0.52
6.250-7.125 47 29.01 3758 10.61 2.73 1
7.125-8 22 13.58 11587 32.72 0.41 0.15
Waterbody 2 1.23 16989 2.47 0.49 0.24
Vegetation Low vegetation 52 32.09 108321 15.80 2.03 1
Moderate 78 48.14 327348 47.77 1.00 0.49
vegetation
High 30 18.51 232485 33.93 0.54 0.26
vegetation
Soil Humic Acrisols 0 0.00 23749 66.88 0.00 0.00
Orthic Acrisols 6 100 5705 16.06 1.49 1
EutricNitosols 0 0.00 6054 17.04 0.00 0.00
Distance to 0-100 37 22.83 2449203 39.71 0.57 0.48
Lineament (m) 100-500 76 46.91 2461404 39.91 1.18 1
500-1000 39 24.07 961187 15.58 0.60 0.51
0.13
1000-1500 10 6.17 294523 4.77 0.15
Distance to road 0-100 122 75.30 2615173 42.41 1.77 1
(m) 100-500 25 15.43 1700672 27.58 0.36 0.20
500-1000 3 1.85 1131895 18.35 0.04 0.02
0.09
1000-1500 12 7.40 718401 11.65 0.17
Elevation (m) 1-1.87 0 0.00 224 0.88 0.00 0.00
1.87-2.75 0 0.00 256 1.01 0.00 0.00
2.75-3.62 73 45.06 13085 51.94 50.67 1
3.62-4.50 64 39.50 5667 22.49 44.42 0.87
4.50-5.37 25 15.43 2316 9.19 17.35 0.34
5.37-6.25 0 0.00 1746 6.93 0.00 0.00
6.25-7.12 0 0.00 1343 5.33 0.00 0.00
7.12-8 0 0.00 552 2.19 0.00 0.00
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Disaster Advances Vol. 15 (1) January (2022)
Curvature (º) 1 2 1.23 4604 0.69 1.77 0.05
1.0-2.0 7 4.32 26117 3.93 6.22 0.18
2.0-3.0 28 17.28 78226 11.78 24.90 0.73
3.0-4.0 38 23.45 124887 18382 33.80 1
4.0-5.0 28 17.28 172951 26.06 24.90 0.73
5.0-6.0 30 18.51 128353 19.34 26.68 0.78
6.0-7.0 21 12.96 91512 13.79 18.68 0.55
7.0-8.0 7 4.32 31744 4.78 6.22 0.18
8.0-9.0 1 0.61 5109 0.77 0.88 0.02
Geomorphology Dam and 2 1.23 2020 5.68 0.21 0.05
reservoir
Highly 0 0.00 182 0.51 0.00 0.00
dissected hills
and valleys
Moderately
dissected hills 2 1.23 1422 4.00 0.21 0.05
and valleys
Pediment
Pediplain
Residual 3 1.85 1662 4.68 0.32 0.07
mound 6 3.70 1088 3.06 0.65 0.15
Rolling plain 2 1.23 1243 3.50 0.21 0.05
Highly
dissected hills 9 5.55 385 1.08 0.97 0.23
and valleys 4 2.46 669 1.88 0.43 0.10
Valleys
Valley fill
Pond
River 2 1.23 283 0.79 0.21 0.05
0 0.00 533 1.50 0.00 0.00
93 57.40 18171 51.17 10.09 2.38
39 24.07 7850 22.10 4.23 1
Lithology Acid to 78 48.14 12563 35.38 1.36 0.45
intermediate
charnockite
Banded iron 0 0.00 13 0.03 0.00 0.00
formation
Fuchsite 0 0.00 0 0.00 0.00 0.00
quartzite
Granite 3 1.85 2299 6.47 0.28 0.09
Granite gneiss 48 29.62 12817 36.09 0.82 0.27
Graphite-biotite 2 1.23 146 0.41 3.00 1
schist
Hornblende- 30 18.51 7212 20.31 0.911 0.30
biotite gneiss
Pegmatite 0 0.00 21 0.05 0.00 0.00
Quartz 0 0.00 233 0.65 0.00 0.00
vein/reef
Quartz- 0 0.00 18 0.05 0.00 0.00
feldspar-garnet
granulite
Talc tremolite
actinolite schist 1 0.61 186 0.52 1.17 0.39
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Disaster Advances Vol. 15 (1) January (2022)
Fig. 2: Slope map of the study area
Fig. 3: Aspect map of the study area
Land use/Land cover: Land use /Land cover (LU/LC) is Vegetation: The Normalized Difference Vegetation Index
one of the primary factors influencing slope stability. The (NDVI) map was created using Landsat 8 images 1 in a GIS
vegetation cover influences the strength of a slope to fall environment. Vegetation of this area is classified into four
short.36 Because of the same unstable conditions, the classes: high vegetation, moderate vegetation, low
possibility of slope failures in the future will be similar to vegetation and water body (Fig. 6). NDVI was calculated by
those in the past.24,28 LU/LC map was created using Landsat using the following equation 1:
8 satellite imagery. LU/LC of this area classifies into five
classes: agricultural land, bare land, forest land, urban land NDVI = (NIR − RED)/(NIR + RED) (1)
and water body (Fig. 4).
where NIR = Near-Infrared Spectrum and RED = Red range
Rainfall: Rainfall is the most important factor for of the spectrum.
landslides.6 The majority of landslides in this area occur
during periods of heavy rainfall. Landslides can occur as a The NDVI is a simple and reliable technique for detecting
result of continuous rainfall reducing slope stability. The the existence of vegetation and it is widely used to assess the
rainfall map of the study area is shown in figure 5. quality of the earth's exterior.17
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Disaster Advances Vol. 15 (1) January (2022)
Fig. 4: Land use/ Land cover map of the study area
Fig. 5: Rainfall map of the study area
Fig. 6: Vegetation map of the study area
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Disaster Advances Vol. 15 (1) January (2022)
Soil: The soil map of this area is generated using an FAO- Elevation: Elevation has a great influence on soil,
UNESCO world soil map. Soils of this area are mainly environment and vegetation.25 The elevation map of this area
constituted of humicacrisols, orthic acrisols and was derived from SRTM-DEM data. The derived map was
eutricnitosols (Fig. 7). Soil erosion and soil creep are very classified into eight classes of 1-1.87m, 1.87-2.75m, 2.75-
common in these soils.34 3.62m, 3.62-4.50m, 4.50-5.37m, 5.37-6.25m, 6.25-7.12m
and 7.12-8m (Fig. 10).
Distance to lineament: The distance to the lineament has a
significant impact on landslides along the slopes of this area. The majority of the research region lies between 2.75 - 3.62
The distance from the lineament of the study region was m on relatively flat terrain. Only 10% of the land area is in
categorized into four distinct buffer zones in the class range the high to very high elevation zone (6.25-8m).
of 0-100 m, 100-500 m, 500-1000 m, 1000-1500 m (Fig. 8).
Curvature: The SRTM-DEM was used to create the
Distance to roads: Landslides are caused by road curvature map for this area. 15 The resulting map are
development that disrupts the slope's stability. The distance classified into nine classes: 1, 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-
to the road of this area was divided into four different buffer 8, 8-9 respectively (Fig. 11).
zone, 0-100 m, 100-500 m, 500-1000 m and 1000-1500 m
(Fig. 9).
Fig. 7: Soil map of the study area
Fig. 8: Distance to lineament map of the study area
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Disaster Advances Vol. 15 (1) January (2022)
Fig. 9: Distance to road map of the study area
Fig. 10: Elevation map of the study area
Fig. 11: Curvature map of the study area
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Disaster Advances Vol. 15 (1) January (2022)
Lithology: In this area, lithology has an essential role in the Geomorphology: The geomorphology of this area is
occurrence of landslides. Lithology is a common factor in extracted by using the Bhukosh - Geological Survey of India
landslide susceptibility studies. The lithology of this area (https://bhukosh.gsi.gov.in/Bhukosh/Public) web source.
was classified into eleven different classes namely acid to Dam and reservoir, highly dissected hills and valleys,
intermediate charnockite, banded iron formation, fuchsite moderately dissected hills and valleys, pediment, pediplain,
quartzite, granite, granite gneiss, graphite-biotite schist, residual mound, rolling plain, highly dissected hills and
hornblende-biotite gneiss, pegmatite, quartz vein/reef, valleys, valley fill, pond and river are some of the
quartz-feldspar-garnet granulite and talc tremolite actinolite geomorphologic characteristics found in the study area (Fig.
schist (Fig. 12). The majority of the landslides happened 13). The rolling plain and highly dissected hills and valleys
along the acid intermediate charnockite rocks in this area. cover the majority of the study region. These features are
also responsible for a large portion of the landslides in the
area.
Fig. 12: Lithology map of the study area
Fig. 13: Geomorphology map of the study area
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Disaster Advances Vol. 15 (1) January (2022)
Landslide susceptibility mapping: We employed the was classified as a moderate susceptible zone, remaining 3.6
Frequency Ratio technique to map landslide susceptibility in % as a low susceptible zone landslide.
this study. For the landslide susceptibility mapping (LSM),
it is significant to mention that landslide influencing factors Validation of frequency ratio model: The frequency ratio
impact the geographic distribution of landslides and future (FR) in this study was analyzed using the landslide
landslides may occur under similar circumstances to susceptibility index (LSI). All landslide points (landslide
previous landslides.22,30 locations) in this study are classified into training and testing
sample points. Table 1 shows the twelve influencing factors
Frequency ratio method: Frequency ratio is the most and their FR in the study area. The FR ratio values are given
widely used statistical technique19,35 for identifying as weights to the classes of each influencing factor map to
landslide susceptible zones.9,40,41 This approach is a bivariate produce a weighted factor, 27 which was then used to
statistical analysis method,16,42 used to forecasting generate the LSI map using the raster calculator tool in Arc
landslides.21,40 This approach is easy to apply and effectively GIS. 36 Among the slope classification, high landslide FR is
deals with landslide susceptibility evaluation due to the observed in the low and moderate slope categories. The
transparency of the criteria and the convenience of landslide susceptibility value represents the actual
application in a GIS environment.21,33 In each variable and susceptibility to a landslide occurrence, with higher values
its classifications, the frequency ratio values are derived indicating higher susceptibility.
from the proportion of landslide pixels to the total area
pixels.29,31,32 This model was assessed using Landslide According to the findings of FR, the most important
Density Index (LDI). The landslide density, a ratio between elements influencing landslides are geomorphology, rainfall,
the percentage of landslide pixels and class pixels in each curvature, aspect, lithology and soil. LSI is the summation
class on a landslide vulnerability map, was used to approve of each factor FR values as in equation 3:
the model.7,13,26
LSI = Fr (Slope) + Fr (Aspect) + Fr (Landuse/
According to the method, the amount of the zone where landcover) + Fr (Rainfall) + Fr (Vegetation) + Fr (Soil) +
landslides occurred in the entire area, with the area with an Fr (Distance to lineament) + Fr (Distance to road) +
estimation of 1 is a normal value. If the value is >1, it implies Fr (Elevation) + Fr (Curvature) + Fr (Geomorphology) +
a higher correlation and if the value isDisaster Advances Vol. 15 (1) January (2022)
landslides (Fig. 16). The area under the ROC curve is used the AUC for the success rate curve is 75.3 %. The map
to evaluate the accuracy of a model. The area under the curve developed by the FR model provideds the best landslide
(AUC) is 0.753 (Fig. 17). According to the FR model results, susceptibility mapping results in the research area.
Fig. 14: Landslide inventory map of the study area
60 14
12
Landslide %
40 10
Landslide%
8
20 6
4
0 2
0
Slope Aspect
(a) (b)
40 40 d
Landslide%
30 20
20
Landslide%
0
10
0
Land use/land cover Rainfall
(c) (d)
11Disaster Advances Vol. 15 (1) January (2022)
50 70
40 60
30 50
Landslide%
40
20
Landslide%
30
10
20
0 10
0
Humic Orthic Eutric
acrisols acrisols eitosols
Vegetation Soil
(e) (f)
40 60
30 40
20
20
Landslide%
Landslide%
10
0
0
Distance to lineament Distance to road
(g) (h)
60 30
50 25
40 20
Landslide%
30 15
Landslide%
20 10
10 5
0 0
Curvature
Elevation
(i) (j)
12Disaster Advances Vol. 15 (1) January (2022)
40 60
Landslide%
Landslide%
40
20
20
Talc tremolite…
Grapite-biotite-…
Acid to…
Banded iron…
Quartz-feldspar-…
Hornblend-…
0
Granite
Granite gneiss
Fuchsite quartzite
Pegmatite
Quartz- vein/reef
0
Highly…
Highly…
Dam and…
Moderately…
Rolling Plain
Valleys
Pond
Valley Fill
Residual Mound
River
Pediment
Pediplain
Lithology Geomorphology
(k) (l)
Fig. 15: Spatial relationship of landslide occurrences with the influencing factors, (a) slope, (b) aspect, (c) land
use/land cover, (d) rainfall, (e) vegetation, (f) soil, (g) distance to lineament, (h) distance to road, (i) elevation,
(j) curvature, (k) lithology, (l) geomorphology.
Fig. 16: Landslide susceptibility map of the study area
Fig. 17: Prediction rate curve of FR model
13Disaster Advances Vol. 15 (1) January (2022)
Conclusion landslides in udhagamandalam – mettupalayam highway,
As a result, a landslide susceptibility map was created for the Tamilnadu, International Journal of Recent Scientific Research,
5(8), 1506-1508 (2014)
Vythiri taluk of Wayanad district, northern Kerala. The
research region was chosen due to the wide range of 4. Arunkumar M., Gurugnanam G., Isai R. and Suresh M., One
landslides between 2018 and 2020. In this location, decade of rainfall precipitation variation with landslides in
landslides have the potential to affect lives and economic udhagamandalam – mettupalayam highway, Tamilnadu,
losses. This research effectively employed remote sensing International Journal of Current Advanced Research, 3(9), 34-37
and GIS techniques to create a landslide inventory, (2014)
susceptibility index and connection between landslides and
influencing factors. Thematic layers of landslide influencing 5. Arunkumar M., Gurugnanarn B. and Venkatraman A.T.V.R.,
factors are developed using SRTM DEM, Landsat 8 and Topographic Data Base for Landslides Assessment using GIS in
Sentinel 2 images. Between Mettupalayam Udhagamandalam Highway, South India,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), 2(5), 302-306 (2013)
As a result, creating a landslide susceptibility map is
preferable for proper planning and managing landslide 6. Beegam Feby, Achu A.L., Jimnisha K., Ayisha V.A. and Rajesh
affected areas. The landslide susceptibility map was Reghunath, Landslide susceptibility modelling using integrated
developed using a total of 162 landslide points. The twelve evidential belief function based logistic regression method: A
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rainfall, vegetation, soil, distance to lineament, distance to Applications: Society and Environment, 20, 100411 (2020)
road, elevation, curvature, geomorphology and lithology)
have been considered when evaluating the spatial 7. Binh Thai Pham, Dieu Tien Bui and Indra Prakash, landslide
susceptibility assessment using baggingensemble based alternating
relationship between these variables and landslide
decision trees, logisticregression and j48 decision trees methods: a
occurrences. comparativestudy, Geotech Geol Eng., 35(6), 2597-2611 (2017)
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influencing factors, it was exposed that rainfall, vegetation, Dwivedi R., Landslide Susceptibility Mapping in Darjeeling
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10. Emrehan Kutlug Sahin, Cengizhan Ipbuker and Taskin
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would support the relevant authorities for identifying
potential risks in particular parts which could be used for 11. Gurugnanam B., Arun Kumar M., Bairavi S. and Vasudevan
landslide mitigation activities as construction development S., Gis data base generation on landslides by tracing the historical
in the region increases. landslide locations in nilgiri district, south India, International
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Acknowledgement
The authors acknowledge the Centre for applied geology, 12. Gurugnanam B., Arunkumar M., Venkatraman A.T.V.R. and
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