IDENTIFICATION OF LANDSLIDE HAZARD …topographic sheet Nos. 58 C/13, 58 C/9, 58 F/4. Several...
Transcript of IDENTIFICATION OF LANDSLIDE HAZARD …topographic sheet Nos. 58 C/13, 58 C/9, 58 F/4. Several...
IDENTIFICATION OF LANDSLIDE HAZARD ZONATION IN
IDDIKI USING REMOTE SENSING
Dr. S. Vidhya Lakshmi*, Mohammed Harif.H**
* Associate Professor ** Final year, B.E., Civil Engineering
Department of Civil Engineering, Saveetha School of Engineering
Saveetha Institute of Medical and Technical Science, Chennai - 602 105, India
Email –[email protected], [email protected]
ABSTRACT:
Landslides are one of the most
common natural disasters which cause big
damage to our environment and properties in
terms of direct and indirect risk. The
expression "landslide" fundamentally implies
an ease back to fast descending development
of instable shake and flotsam and jetsam
masses under the activity of gravity which
can be sorted into different kinds based on
disappointment qualities Increase in
population and rapid urbanization has led to
increase of construction works in mountain
and hilly terrain has catapulted more number
of landslides to most area in recent decades.
Dangerous landslide area was identified in
Munnar, Iddiki, India. Landslide zonation in
Idukki, Kerala, India has been done using
GIS and Remote Sensing. Factor maps of
different territory parameters, for example,
slope, land use, relative relief, drainage
pattern, drainage density, landform, and
surface material were readied and their
joining did on a GIS platform. The delicacy
idea utilized as a part of this examination is a
quick and savvy demonstrate for
distinguishing avalanche inclined zones,
particularly in the Western Ghats.
KEYWORD: Landslide, slope, landuse,
relative relief, drainage pattern,
landform, drainage density, envi 4.5,
qgis 2.6.0.
1. INTRODUCTION:
Landslides is described as huge
movement, which is sudden down slope
moving of soil sediments and rocks due to
gravity. These can also cause due to intense
rainfall, earthquakes, Water-level changes,
storm or sudden stream erosion which have
cause a rapid increase in stress or decrease in
strength of slide-forming materials. These
include rainfall, earthquakes, water-level
increase, storm waves which cause a fast
increment in shear pressure or abatement in
shear quality of slant shaping materials. As
one of the major natural hazards, landslides
claim people’s lives almost every year and
cause huge property damage in mountainous
areas. The evolution of increase landslide
hazard has given greater attention from the
geological scientists, engineering, local
communities and all government in
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numerous country. Traditionally,
susceptibility assessment or probability
mapping were carried out using laborious and
time-consuming because of the time required
and effort for the human handling and
processing of the data. As of late, geographic
data frameworks (GIS) have turned into an
essential apparatus for avalanche risk
evaluation. GIS is a PC based innovation
intended to catch, store, control,
examinations and show differing sets of
spatial or geo referenced information.
2. STUDY AREA:
The study area forms part of the
Western Ghats in Idukki district, Kerala
State. The study area is located around
Munnar within latitude 9° 46' & 10°
05’N and longitude 76° 45 ' & 77° 05' E
falling in the Survey of India
topographic sheet Nos. 58 C/13, 58 C/9,
58 F/4.
Several workers have investigated
slope failures in Western Ghats and
occurrence landslides in the Idukki
district (Seshagiri et al., 1982,
Kandaswamy et al., 1985 Krishnanath.et
al., 1985, 1993, 1996, Biju
Abraham.et.al., Krishnanath and
Sreekumar, 1996, Sreekumar, 1998
Sreekumar and Krishnanath, 2000,
Sankar, 1991; Earnestetal., 1995;
Thampi et al., 1997, Thampi 2006, 2009
and Sajinkumar et al., 2011, Biju
Abraham, 2011). Studies by Seshagiri et
al., 1982, Thampi et al., 1997, Thampi
2006 and Sreekumar et al, 2010,
Sreekumar and Arish Aslam (2011) have
proved that the slope failures in Western
Ghats are generally confined to the
overburden. Krishnanath et al., 1996,
Sreekumar and Krishnanath 2000, have
studied number of profiles within Idukki
and Pathanamthitta districts and
identified a number of profiles which are
at the geotechnical threshold.
A geological map of the entire study
area is prepared using topographic sheets
(scale 1:50,000), regional geological maps of
Geological Survey of India and from the data
generated in this study. The slope forming
material exposed along the road cuttings
consists predominantly of crystalline rocks,
and their highly weathered equivalents. The
main rock types include biotite gneiss
(granitic gneiss), hornblende biotite gneiss
(composite gneiss), granite gneiss,
charnockites and pyroxene granulite. These
rocks are intruded by basic, acidic and
alkaline intrusives . Pink granites are seen in
the Munnar area.
FIG.1 STUDY AREA
3. DATABASE
From the current study two satellite
imageries are downloaded from the United
States Geological Survey web site
(http://glovis.usgs.gov) over the twenty two
years of time period (1989 - 2011). the main
points of the satellite imageries, acquisition
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date and resolution has shown in below table.
each the information sets are projected in
UTM projection with zone number 43 and
WGS 84 datum. Satellite image of 1989 has
been thought of because the base knowledge
and image of 2011 is co-registered
victimization initial order polynomial model
with that base information with 0.5 picture
element (RMSE) accuracy.
Sensor
type
Acquisition
date
Spatial
resolution
Landsat
TM 5
18 May
1989
30 m
Landsat
TM5
5 March
2011
30m
TABLE :1 Database
Fig 2:Landsat ETM+(2017) Satellite Image.
4. METHODOLOGY
Landslides are very unpredictable events that
take place in the hilly regions. In India,
annual losses due to landslides are estimated
at $1 billion or more. The fatalities that are
caused by landslides cannot be counted in
monetary form. A landslide depends upon
various factors like slope, type of soil,
geology of area, construction activities, land
use of the area etc. One can forecast the
landslide potential of an area with the help of
remote sensing data. The remote sensing data
about the different factors on which the
occurrence of landslide events depend upon
can be incorporated in Geographical
Information System environment and the
degree of these factors can be estimated.
Satellite imagery and remote sensing data can
be used to know the effect of different factors
on which the occurrence of a landslide event
depends upon. This data is processed in GIS.
Each factor responsible for the occurrence of
landslide is taken into account and then their
combined effect can be calculated in GIS
environment.
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Fig 3: Methodology
5. LANDSLIDE HAZARD
MAPPING
Over the past twenty five years, government
and analysis establishments have invested
with resources in assessing landslide hazard,
and in trying to provide maps depiction its
abstraction distribution. Over the past twenty
five years, government and analysis
establishments have invested with resources
in assessing landslide hazard, and in trying to
provide maps depiction its abstraction
distribution. many entirely alternative ways
and techniques for evaluating landslide
hazard and risk square measure projected or
tested. Work of the literature reveals that a
handful of reviews of the ideas, principles,
techniques and methodologies for landslide
hazard analysis square measure projected.
amazingly, little or no work has been done on
the systematic comparison of assorted
techniques, outlining blessings and
limitations of the projected ways that or to the
necessary discussion of the elemental
principles and underlying assumptions of
landslide hazard analysis. Likewise,
exclusively few tries square measure lliade to
stipulate, conceptually or operationally,
landslide risk.
the bulk of papers discuss specific tries at the
analysis of landslide hazard in restricted
areas. exclusively a handful of authors report
on long comes on the analysis of slope
instability conditions, and additionally the
connected hazard and risk, over big regions.
Notable examples square measure drawn by
the work carried out in city County, CA, by
the U.S. natural science Survey by the
proposal created by the French Bureau des
Recherches Geologiques et Minieres for a
geomorphologically primarily based analysis
of landslide hazard by the work carried out at
the Geotechnical Engineering geographic
point, in metropolis and by the appliance of
variable mathematics techniques in pilot
areas of Southern and Central European
nation. Operational and abstract variations
include: general underlying assumptions; the
sort of mapping unit elect for the
investigation.
Preparation of thematic maps showing slope,
landuse, geological, drainage, lineament,
runoff and soil components of the landslide
house pattern remote sensing photos. These
square measure analyzed and square measure
numerically weighted supported their relative
importance. The study house has been
classified into four zones of instability and
landslide hazard zonation map is formed.
6. RESULTS AND DISCUSSION:
6.1. Preparation Of Thematic Maps
For the preparation of various type maps the
data have been collected from various
sources like Survey of India Toposheets
(SOI) No.58A/11 of scale 1:50,000 of 1972,
IRS1C LISS III +PAN incorporate satellite
image information, field information and
numerous different sources are used.
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6.2. Base Map
Base map was ready from the Survey of India
Toposheet. It offers the boundary of the study
space. There are more river, dam and
waterfall have been flown in iddiki
district.This map helps to find the watershed
on iddiki district.
Fig 4:Base Map
6.3. Slope
Slope is a vital consider the analysis of
landslide. Because the slope will increase the
chance of the incidence of landslide will
increase as a result of because the slope angle
will increase the shear stress of the soil. The
stability of a slope phase relies on numerous
factors like slope angle, slope form, slope
length, material of that it's fashioned,
antecedent wetness content. The slope angle
was thought-about the most parameter of the
slope stability it absolutely was ordinarily
utilized in getting ready landslide status
analyses because the shear stress will
increase with progressive inclination. Slope
is that the live of surface gradient and
measured in degrees. Slope angle is
incredibly oftentimes utilized in landslide
status studies since land sliding is directly
regarding slope angle.
Fig 5:Slope Map
6.4. Landuse
The Land use map shows the various sorts of
land cover pattern gift within the study space.
Vegetation cover is a vital issue that
influences the prevalence and movement of
the precipitation that triggers the landslide.
The watershed space is defined by the dense
forest, degraded forest, agricultural land,
forest plantation, grass land, husbandry
plantation, land with or while scrub, forest
and villages. The gardening planting is found
because the major landuse within the
watershed. Land use is additionally one
among the key factors chargeable for the
prevalence of landslides, since, barren slopes
area unit additional at risk of landslides. In
distinction, vegetative areas tend to cut back
the action of environmental condition agents
admire rain, etc., thereby preventing the
erosion because of the natural anchorage
provided by the tree roots and, thus, area unit
less at risk of landslides. The landuse
categories of {the area unita|the world|the
realm} are designed up land, Crop land,
Fallow land, Forest evergreen, Forest
deciduous, Forest plantation, Land with
scrub, Barren rock, River/water bodies, Grass
land, Rubber, Mixed crop, Cardamom, Tea
and occasional. Evergreen Forest and rubber
plantations area unit the most land cowl
categories within the study space.Natural
vegetation coverage is crucial in influencing
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slope stability owing to higher bonding of the
slope material.
Fig 6: Landuse Map
6.5. Geology Map
Geology map is prepared from the geological
survey of India mineral map on a scale
1:50,000. Structurally the area is highly
disturbed and subjected to faulting. The
charnockite covers all the watershed area.
Fig 7:Geology Ma[
6.6. Drainage Density Map
The drainage map shows the flow of water
through munnar drainage area. Because the
distance from the drain line will increase the
likelihood of prevalence of landslide
conjointly increase. The drain density for the
study area have been categorized in to four
zones (very high, high, medium and low).
Most of the watershed have been fall
underneath high density class.
Drainage
Density
(Km/Km 2
)
Classification
> 0.008 Very High
0.006 – 0.008 High
0.004 – 0.006 Moderate
< 0.004 Low
Fig 8: Drainage Density Map
6.7. Lineament Density Map
The lineament map shows the lineaments
shaped within the study space thanks to the
geologic conditions. Water flows through the
cracks and therefore the soil over this
lineament would slide and thence this
triggers the landslide. A lineament map was
ready by visual interpretation of the satellite
information by characteristic the fractures
and fault lines. The lineament density is
classified in to four categories as terribly
high, high, moderate and low as show within
the table below. Majority of the watershed
falls beneath high density class.
Table 2: Lineament Density Map
Lineament Density
(Km/Km 2 )
Classification
> 0.004 Very High
0.002 – 0.004 High
0.001 – 0.002 Moderate
< 0.001 Low
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6.8. Runoff Map
The runoff map shows the depth of runoff
that was obtained because of precipitation.
The depth of runoff was calculated from the
precipitation information for the year 2004.
This information was obtained from HADP.
The runoff is high for the whole watershed,
with terribly high runoff in agricultural land
that ends up in severe erosion. Absence of
low runoff is noted within the study space.
The runoff lineament density is categorised
in to four categories as terribly high, high,
moderate and low as show within the table
below.
Table3:Runoff map
Range mm Runoff Class
7501600 Very High
500750 High
250500 Moderate
0250 Low
6.9. Soil Map
The soil map may be a map a
geographical illustration showing diversity of
soil sorts and soil properties within the study
space. It usually the top results of a soil
survey inventory. Soil map area unit most
typically used for land analysis, spacial
designing, environmental protection and
similar project.
Fig 9: Soil Map
6.10. Landslide Hazard Zonation Map
Landslide hazard zonation map is
ready by desegregation the result of varied
triggering factors. Landslide hazard zonation
mapping at regional level of Associate in
Nursing outsized area provides a broad trend
of landslide potential zones. This is often the
ultimate output of the project it offers the
elaborate regarding landslide hazard zone in
study space from low to high.
Table 4: Landslide Hazard Zonation
Map
S.NO Theme map Measure Maximum
Weightage
1 Land Use Type of land
cover
20
2 Slope Degree of
slope
20
3 Soil Thickness of
soil
15
4 Runoff Depth of
runoff
15
5 Geology Type of rock 10
6 Lineament Lineament
density
10
7 Drainage Drainage
density
10
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7. CONCLUSIONS:
Based on the suitableness index the
landslide condition levels area unit classified
into four categories. The susceptibleness
level decreases once the quality index is high
for a specific space. The areas which are
inclined for landslide are calculated as given
within the Below Table. The eastern region
of the watershed is below high inclined for
landslide whereas the villages within the
eastern aspect are beneath moderate
susceptible for landslide. The dense forest
that falls below the western region of the
watershed is found to be having intensity
susceptibleness. The village is discovered as
having low inclined for landslide than
different villages. The inclined maps of the
study space area unit shown in below.
Fig 10: Final Map
Susceptibility Classification
Susceptibility
Index
Susceptibility Class
0 – 185 Very High Susceptible
Area
185 285 High Susceptible Area
285 315 Moderate
Susceptible Area
315 400 Low Susceptible Area
Table 5: Susceptibility Classification
Landslide susceptible area
Different
categories
Area (km 2 )
% of
area
Zone of Very High and high susceptibility
13.26 90
Zone of
Moderate
Susceptibility
1.18 8
Zone of Low
Susceptibility
0.3 2
Table 6: Landslide susceptible area
REFERENCE:
1. Abhijeet Bernard Chaves., Lakshum
anan, C., 2008: “ Remote Sensing an
d GIS based integrated study and ana
lysis for MangroveWetland Restorat
International Journal of Pure and Applied Mathematics Special Issue
3218
ion in Ennore Creek, Chennai,
South India,” Proceeding of Taal2
007:The 12 th
World Lake Conference, pp.68569
0
2. Bulmer, MH. (2002) Studies of
Landslides using Remote Sensing
Data CEOS Landslide Hazard Team
Report. 6p.
3. Carnec C, Massonnet D, King C.
(1996). Two examples of the use of
SAR interferometry on displacement
fields of small extent. Geophys. Res.
Letts., 23(24), 3579–3582.
4. Couture R, Riopel S, Hawkins R,
Poncos V, Murnaghan K, Singhroy
V. (2006) Coherent Targets for
Interferometric SAR to Monitor
Unstable Permafrost Slopes in the
Mackenzie Valley, Northwest
Territories. 34th annual Yellowknife
Geosciences Forum, Yellowknife ,
Canada. 2p.
5. Borges, R., HernandezGuerra, A., N
ykjaer, L., 2004: “Analysis of sea sur
face temperature time series of the so
utheastern North Atlantic”, Internati
onal J. of Remote Sensing 25(5), pp
869891.
6. Brahabhatt, V.S., Dalwadi, G. B., Ch
habra, S. B., Ray, S. S., Dadhwal, V.
K., 2000: “Landuse/land cover cha
nge mapping in Mahi canal comm
and area, Gujarat, using multitem
poral satellite data”, J. Indian Soc.
Remote Sensing. 28(4), pp 221232.
7. Capuzzo, J. M., Burt, B. V., Duedall,
I. W., Park, P. K., Kester, D. R.,198
5: “Near shore waste disposal”. New
York: Wiley.
8. Devi, V., Miranda, W. J., Abdul
Azis, P. K., 1996: “Deterioration
of water quality—
an overview on the pollution prob
lems of the Ashtamudi Estuary”,
Pollution Research. 15, pp 367–370.
9. Gautam, N. C., Narayanan, L. R. A.,
1983: “Landsat MSS data for land us
e/land cover inventory and mapping:
A case study of Andhra Pradesh”, J.
Indian Soc. Remote Sensing, 11(3),
pp1528.
10. Giri, C., Zhu, Z., Reed, B., 2005: “C
omparative analyses of the Global
Cover 2000 and MODIS land cover
data sets”, Remote Sensing of Enviro
nment, 94, pp123–132.
11. Hakan, A., 2005: “Perceptions of coa
stline changes in river deltas: southea
st Mediterranean coast of Turkey”, I
International Journal of Pure and Applied Mathematics Special Issue
3219
nt. J. Environment and Pollution. 23
(1), 102.
12. Zain, S. K., 1992: “Land use mappin
g of Tawi catchment using satellite d
ata”. Report No. CS72, National Ins
titute of Hydrology, Roorkee, 52.
13. Klemas, V., 1986: “Remote sensing
of coastal resources in developing co
untries”, Proceedings of the conferen
ce of Remote Sensing and its impact
on Developing Countries, Rome, 16
21 June 1986: Pontifical Academy of
Sciences, 122.
14. Klemas, V., Abdel Kader, A. M., 19
82: “Remote sensing of coastal proce
sses with ephasis on the Nile Delta, I
n: Proceedings of the International S
ymposium on Remote Sensing of En
vironments”, Cairo, Egypt, 27.
15. Konecny, G., 2003: “Geoinformation
: Remote Sensing, Photogrammetry,
and Geographic Information Systems
”, London: Taylor and Francis.
16. Li Xiubin., 1995: “A review of the in
ternational researches on land use/co
ver change”, Acta Geographica Sinic
a.51 (6), pp 553.
17. Muttitanon, W., Tripathi, N. K., 200
5: “Land use/cover changes in the co
astal zone of Bay Don Bay, Thailand
using Landsat 5 TM data”, Internatio
nal Journal of Remote Sensing, 26(1
1),pp 2311–2323.
18. Ferretti A, Prati C, Rocca F, Casagli
N,. Farina P, Young B. (2005)
Permanent Scatterers technology: A
powerful state of the art tool for
historic and future monitoring of
landslides and other terrain instability
phenomena Proc. Int. Conf. on
Landslide Risk Management,
Vancouver
19. Rama Devi, V., Miranda, W.J., Adhu
l Azis, P.K., 1996: “Deterioration of
water quality
An overview on the pollution pro
blems of the Ashtamudi Estuary,”
Pollution Res, 15, pp 367370.
20. Rupesh, G., Anjan, S., 2008: “Monit
oring physical growth of ranchi city
by using geoinformatics techniques”
, ITPI journal 5(4), pp 38 – 48.
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