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Vol. 3 (2014), pp. 28-36, ISSN 2362 7409
Shallow landslide susceptibility mapping for selected areas in the Philippines severely
affected by Supertyphoon HaiyanI
ML Rabonzaa,∗, RP Felixa,b , IJG Ortiza,b , IKA Alejandrinoa , DT Aquinoa , RC Ecoa,b , AMFA Lagmaya,b
a Nationwide
b National
Operational Assessment of Hazards, Department of Science and Technology, Philippines
Institute of Geological Sciences, University of the Philippines, Diliman, Quezon City
Abstract
Situated in the humid tropics, the Philippines will inevitably be a locus of climate-related disasters. Super Typhoon Haiyan,
considered as one of the most powerful storms recorded in 2013, devastated the central Philippines region on 8 November 2013.
In its wake, Haiyan left 6,190 fatalities, 28,626 injured and 1,785 missing, as well as damage amounting to more than USD 823
million. To mitigate damage from similar events in the future, it is imperative to characterize hazards associated with tropical
cyclones such as those brought by Haiyan, with detailed studies of storm surges, landslides and floods. Although strong winds and
powerful storm surges up 15-17 feet were the primary causes of damage, landslides studies are also vital in the rehabilitation of
typhoon damaged areas. In order to delineate areas susceptible to rainfall-induced shallow landslides and generate a worst-case
scenario hazard map of the two provinces, Stability INdex MAPping (SINMAP) software was used over a 5-meter Interferometric
Synthetic Aperture Radar (IFSAR)-derived digital elevation model (DEM) grid. SINMAP has as its theoretical basis in the
infinite plane slope stability model. Topographic, soil-strength and hydrologic parameters (cohesion, angle of friction, bulk
density and hydraulic conductivity) were used for each pixel of a given DEM grid to compute for the corresponding factor of
safety. The landslide maps generated using SINMAP are found to be highly consistent with the landslide inventory derived from
high-resolution satellite imagery dated 2003 to 2013. The landslide susceptibility classification found in the landslide hazard maps
are useful to identify no-build, areas that can be built upon but with slope intervention and monitoring as well as places that are
safe from shallow landslides. These maps complement the debris flow and structurally-controlled landslide hazard maps that are
also being prepared for rebuilding Haiyan’s devastated areas.
Keywords: landslide mapping, Haiyan, SINMAP
1. Introduction
Typhoon Haiyan, a category-5 super typhoon, made landfall
in central Philippines on 8 November 2013 leaving 6,190 fatalities, 28,626 injured and 1,785 missing, as well as damage
amounting to more than USD 823 million [1]. Haiyan recorded
a maximum sustained winds of 235 km/h near its center and
gustiness of up to 275 kph [2]. Its powerful winds and heavy
rains triggered widespread flooding and landslides leaving the
central Philippines under a state of calamity. Although strong
winds and storm surges up 15-17 feet were the primary causes
of damage, landslides studies are also vital in the rehabilitation
of typhoon damaged areas [2].
Occurences of shallow landslides depend on local terrain
conditions such as slope-materials, topography, groundwater,
and land cover in addition to triggering events, like torrential
rainfalls and earthquakes which modify those charateristics and
produce changes that cause slope instability [3].
Slope instability is a geo-dynamic process that naturally
shapes up the geomorphology of the earth; however, it becomes
I Published
Figure 1: Track of super typhoon Haiyan across the Philippine Area of Reponsibility
a major concern when those slopes affect the safety of people
and property. Elevation of the ground water table due to prolonged or heavy rainfall also naturally change the hydro-static
and dynamic forces at the slope. This often initiate most of the
online on 4 June 2014
author
∗ Corresponding
28
slope failures in the Philippines.
Existing slopes that have been stable for years can still experience significant movement when natural or man-induced conditions change their slope stability. Other natural conditions
that can change slope stability are earthquakes, surface erosion,
development of shrinkage or tension cracks followed by water
intrusions, and bedrock weathering [4].
With the growth of population, the demand for suitable and
necessary infrastructure and other services increases. Therefore, in a country that is mostly hilly and mountainous, utilization of land on slopes is inevitable. It is therefore very important to map out unstable areas in order to ensure the safety of
the people and delineate suitable areas for development.
plane parallel to the ground surface. It is implemented to shallow translational landsliding phenomena controlled by shallow
groundwater flow convergence. The slope stability theory does
not apply to deep-seated instability including rotational slumps
and deep earthflows [5].
Slope failures occur frequently during or following period of
heavy rainfall. The mechanism leading to rainfall-induced landslides can be summarized in general terms as follows: when
rainwater infiltrates a soil profile that is initially in an unsaturated state, a decrease in negative pore pressure (or matric
suction) occurs. This causes a decrease in the effective normal stress acting along the potential failure plane, which in turn
diminishes the available shear strength to a point where equilibrium can no longer be sustained in the slope [6]. Therefore, a
slope stability modelling considering hydrologic wetness, soilstrength properties of soil and topography in a certain region
can possibly predict zone of failure initiation in slopes.
The input data required to implement the methodology include topographic slope, specific catchment area and parameters quantifying material properties (such as soil strength) and
climate (hydrologic wetness parameter). The topographic variables are computed from digital elevation model (DEM) data.
SINMAP does not require numerically precise input and accepts ranges of values to account uncertainty. Other input parameters are specified to SINMAP in terms of upper and lower
bounds on the ranges they may take. The methods implemented
in SINMAP software rely on grid-based data structure. Each of
the input parameters is delineated on a numerical grid over the
study area. The accuracy of the output is therefore heavily reliant on the accuracy of the input DEM [7].
The primary output of this modeling approach is a stability
index (SI), the numerical representation of the stability of terrain and hence the possibility of landslide occurrence at each
pixel in the study area. The indices are not to be interpreted as
a numerically precise and are most appropriately interpreted as
indications of ’relative hazard’.
The stability index is defined as the probability that a location is stable assuming uniform distributions of parameters over
the uncertainty ranges. It does not predict that shallow translational slope movements will occur, but it forecasts that if they
do, where they are more likely to initiate given the assumptions
and input parameters used in the analysis. This value ranges between 0 (most unstable) and 1 (least unstable). Where the most
conservative parameter ranges still results in stability, the stability index is defined as the factor of safety (ratio of stabilizing
to destabilizing forces) at a location. This case yields a stability
index value greater than 1. If the computed stability index value
is less than 1, it is then defined as the probability that a location
is stable given the range of the parameters used [8].
Table 1 presents the definition of terms of the SI based on
broad stability classes. The selection of breakpoints (0.0, 0.5,
1, 1.25, 1.5) is subjective which requires interpretation. According to the model, regions that should not fail given the most
conservative parameter used are classified as ’stable’, ’moderately stable’, and ’quasi-stable’. For this cases, the SI is defined
as the factor of safety. Moreover, the terms ‘lower threshold’
and ‘upper threshold’ are used for regions where, as computed
1.1. Objectives
The purpose of this study is to generate a shallow landslide
susceptibility map for Samar and Leyte. The map will then
be compared to a landslide inventory derived from satellite imagery dated 2003-2013. Unlike delineating hazard zones using
historical data, maps generated using a deterministic slope stability GIS model, considers pixel-specific soil-strength parameters and hydraulic characteristics. Prior to field validation, the
generated maps can provide a reasonable estimate of the hazard
zones for shallow landslide susceptible areas.
The shallow landslide susceptibility maps when complemented with debris flow and structurally controlled hazard
maps are useful to identify no-build, areas that can be built upon
but with slope intervention and monitoring as well as places that
are safe from shallow landslides.
Figure 2: Location of Samar and Leyte
1.2. The SINMAP (Stability INdex MAPping) Approach to Stability Index Modelling
SINMAP is an ArcView GIS extension and an objective terrain stability mapping tool that complements other types of terrain stability mapping methods. This methodology presented
in this paper is based upon the infinite slope stability model
which balances the resisting components of friction and cohesion and the destabilizing components of gravity on a failure
29
by the model, the probability of instability is less than or greater
than 50% respectively. To induce instability in these areas, external factors are not required. Failure may simply occur due to
a combination of parameter values within the specified range.
The term ’defended slope’ is used to classify regions where
the model is not appropriate, or such slope are held in place
by forces not represented in the model (e.g. bedrock outcrops,
man-made slope protection) [7].
permits patterns of slope instability to be mapped at the scale of
the DEM.
The approach implemented in this paper combines steady
state hydrologic concepts with the infinite slope stability model
and incorporates grid-based DEM methodology. Parameter uncertainty is incorporated through the use of uniform probability
distributions within upper and lower limits of the parameters.
Moreover, in substitute to dynamic modeling over a range of
rainfall events, the range of uncertainty of the hydrologic wetness parameter can be applied. In an approximate sense, this
capability enables to predict the zones of landslide susceptibility without the additional input of weather data and analysis.
For mapping purposes in the Philippines, these 6 classes are
reduced to 3 major hazard ratings with corresponding interpretations. Class 3 (1.25 ¿ SI 1.0) in Table 1are classified as zones
of ‘Low Susceptibility’. These regions must be built with slope
intervention. Class 4 (1.0 ¿ SI ¿ 0.5) are classified as zones of
‘Moderate Susceptibility’. Such zones require slope intervention, protection and continuous monitoring. Class 5 (0.5 ¿ SI
¿ 0.0) are ‘No Build Zones’ and categorized as ‘High Susceptibility’ areas. The following color scheme is selected for each
landslide hazard rating.
Stability Index Definition
Figure 4: Visual Representation of Infinite Slope Model, [9]
Figure 3: Legend for Stability Index Mapping
SINMAP uses the following formula to calculate the stability
index based on the infinite-slope equation proposed by [10].
h
i
a
C + cosθ 1 − min TR sinθ
, 1 r tanφ
FS =
sinθ
Background
Multiple approaches are widely used to assess landslide hazards and slope stability [5]:
Where:
1. Field inspection using a check list to identify zones of
landslide susceptibility
2. Estimate future occurrences based on historical data
3. Stability ranking based on criteria for slope, land form,
lithology or geologic structure
4. Probability analysis of failure based on slope stability
models with stochastic hydrologic simulations
FS = factor of safety
a = topographic catchment area
C = dimensionless cohesion = (Cr + C s )/hρ s g
Cr = root cohesion; C s = soil cohesion;
h = soil thickness;ρ = soil density; g = gravity constant
Each one is significant for certain applications. It is desirable to take full advantage of the fact that landslide source areas are, in general, strongly controlled by surface topography
through shallow subsurface flow convergence, increased pore
water pressure, reduction of shear strength and increased soil
saturation. Recently, the availability of DEM data has prompted
the development of methods that take advantage of the geographic information system (GIS) technology to calculate topographic attributes related to slope instability. GIS technology
hw = height of water;
R = recharge
r = water density (ρw ) to soil density (ρ s ) ratio
T = soil transmissivity = soil hydraulic conductivity x h
φ = soil internal angle of friction
30
θ = slope
are also present but covers only relatively smaller portions of
Leyte. Mountainous areas are located in places identified only
in the soil map as rough mountainous land [11].
The variables theta and a are obtained from the DEM topography. Other parameters, C (cohesion), phi (soil angle of friction), R/T (recharge divided by transmissivity), and r (the ratio
of water and soil density) are manually entered into the model.
These are the more uncertain parameters and are set in terms of
lower and upper bound values. The smallest C and tan phi together with the largest R/T define the most conservative (worst
case) scenario within the assumed variability in the input parameters [8].
3.2. Samar Soil
The soil type in Samar varies from hydrosol, clay, clay loam,
loam, loamy sand, sandy loam to beach sand. Majority of the
area on the west is covered by clay loam (Fig. 6). Faraon clay
type extends from north to south of Samar’s central area. However, on the eastern side, soil is only described as undifferentiated [11].
2. Geology of the Study Areas
2.1. Leyte
Leyte Island has three major tectonostratigraphic terranes:
Northwestern Leyte (part of Visayan Sea Basin), Leyte Central
Highlands (has arc/ophiolite affinity), and northeastern Leyte
(ophiolitic association in the Leyte Gulf).
The northeastern segment of the Visayan Sea Basin is represented by the sedimentary sequence found in northwestern
Leyte. This has a north-north-west trending anticlinorium.
Southwest, on the other hand, is represented by ophiolitic basement and Paleogene sedimentary rocks. Central Highland is
dominantly underlain by igneous rocks. Its topmost layer is
the Late Pliocene- Recent Leyte volcanic arc complex which
is composed of andesitic volcanic cones and flows with minor basalt. This covers the old volcanic rocks of the Central Highland. The eastern Leyte has ophiolite complex overlain by three formations wherein the topmost layer is the Early
Miocene Bagahuin formation which consists of conglomerate,
sandstone, shale and fine tuffaceous sequences with intercalations of volcanic flows [11].
Figure 5: Leyte soil map. (Modified from Philippine Soil Map of Bureau of
Soils and Water Management (BSWM), 2013)
2.2. Samar
Samar Island has two stratigraphic groupings namely the
Samar Block and the Leyte Gulf. Samar block constitutes the
whole island except for southwestern Samar since it is part of
the Leyte Gulf together with the islands of Bucas Grande, Dinagat, Homonhon and Siargao.
Samar ophiolite constitutes the basement rocks overlain by
different formations. Balo, Hagbay and Catbalogan formations
are distributed in areas of Samar. Late Cretaceous Balo formation, found in Bagacay and San Jose de Buan, consists of limestones, conglomerate, sandstone, mudstone and shale. Middle
Miocene Hagbay formation, distributed in Barrio Hagbay, San
Jose de Buan, consists of reefal limestone and siltstone. Middle Miocene - Early Pliocene Catbalogan formation is found in
Catbalogan, Loguilocan and Bassey. It is composed of marl,
siltstone, sandstone, pebble and conglomerate [11].
Figure 6: Samar soil map. (Modified from Philippine Soil Map of Bureau of
Soils and Water Management (BSWM), 2013
4. Methodology
The following figure outlines the methodology for creating a
landslide susceptibility map using the coupled hydrological and
deterministic slope stability model (SINMAP).
4.1. Digital Elevation Model Grid Data
DEM data were acquired from the National Mapping and Resource Information Authority (NAMRIA) of the Philippines in
September 2013. These are Interferometric Synthetic Aperture
Radar (IFSAR) – derived digital terrain models (DTM) with 5meter resolution and 0.5 meter vertical accuracy.
3. Soil Type
3.1. Leyte Soil
Leyte province has an extensive cover of clay soil (Fig. 5).
Clay loam, fine sand, hydrosol, sandy loam, silt loam and silt
31
Figure 9: Digital Elevation Model of Samar Province overlain with landslide
inventory
4.2. Geotechnical Data
Parameters not determined by the DEM, i.e. the geotechnical
data, are considered more uncertain and are specified in terms
of upper and lower boundary values [8].
The following text rationalizes the specific input values and
distributions used in this study.
Figure 7: Schematic Diagram of Methodology
4.2.1. Cohesion
The equation used to determine the dimensionless cohesion
combines root and soil cohesion. Theoretically this is the ratio of the cohesive strength of the roots and soil relative to the
weight of a saturated thickness of soil.
C=
(Cr + C s )
hρ s g
Cr = root cohesion
Cs = soil cohesion
Figure 8: Digital Elevation Model of Leyte Province overlayed with landslide
inventory
h = soil thickness
ps = soil density
g = acceleration due to gravity
4.2.2. Internal Friction Angle
Internal angle of friction is the measure of the shear strength
of soil due to friction. It can be determined in the laboratory using Direct Shear Strength or Triaxial Stress Test. Although no
independent soil analysis was completed, the 31 to 37 degrees
soil-friction angles used to calibrate the model were considered
realistic for the study area since the study requires that parameters be generalized over large areas with variation encompassing the properties of several different soil types. This is more
realistic than providing a small range of input values.
32
Marginal unstable zones covers 5.80% of the area. This type of
zone becomes unstable when minor destabilizing forces are applied such as surface erosion, development of shrinkage or tension cracks followed by water instrusions, and bedrock weathering.
The accuracy of the model is verified by overlaying the inventoried landslides over the simulated model. Table 3 shows
the summary of the landslide inventories under different stability class. Under the unstable zones predicted by the model,
85.82% of the total inventoried landslides fall under it. Another
1.4% is predicted as part of marginal unstable zones. However,
12.68% of the inventoried landslides fall under stable zones and
hence not predicted by the model. Figure 11 shows a close up
view of an area with identified landslides. Three of them is
located in areas identified as unstable and 1 in marginally unstable.
4.2.3. T/R (Ratio of Transmissivity to Effective Recharge)
The ratio of transmissivity of the soil (m2 ) to the effective
recharge (m/hr), when multiplied by the sine of the slope, the
T/R value can be interpreted as the length of the hillslope (in
meters) necessary to develop saturation (Pack et.al, 1998b).
The recharge rates used in this study have been derived from a
lower and upper precipitation values or limits, i.e., 50 mm/day
and 200mm/day. 50 mm/d rate was chosen as minimum rate
and 200 mm/day is used as a maximum, i.e., an extreme example of the precipitation that can produce shallow translational
movement. For comparison, most municipalities affected by
Haiyan experienced an accumulated rainfall of 60-100 millimeters over a three-hour period (Project NOAH, 2013).
The transmissivity rate (m2 /hr) was calculated using basic
equation
T = Kb
Where K is the hydraulic conductivity of the soil and b is the
soil depth in meters. In the context of shallow landslide movement, soil thickness (b) of 1.5 meters is assumed to be uniform
in the simulation. Data on Hydraulic conductivity is obtained
from Hamazaki’s Database on Red-Yellow and Related Soils in
the Philippines Part 2 Visayas and Mindanao Soils.
Soil classifications (from the Soil Texture Map of Bureau of
Soils and Water Management, 2013), descriptions, and test results, along with literature values for soil properties given in
Hammond and others (1992) were used to constrain reasonable
ranges of soil input parameters for the stability index modeling.
Parameter values (primarily dimensionless cohesion, soil
thickness, internal friction angle, and hydraulic conductivity)
were then adjusted within reasonable ranges to maximize the
number of slope movement locations per area.
Based on the dominant soil classification in Samar and Leyte,
the lower and upper limit values for the parameters are estimated to correspond to a worst case scenario simulation.
4.2.4. Landslide Inventory
A landslide inventory has been completed for the study areas
using high- resolution satellite imagery. These landslide points
were later used to calibrate and validate the model.
The stability index map produced by SINMAP (6 classes)
was further reclassified into 3 classes (Low, Medium, High) to
obtain the landslide susceptibility map and was compared with
the landslide inventory locations. The relationship between
land use and landslide susceptibility classes was also obtained
to locate the vulnerable areas.
Figure 10: Shallow Landslide Susceptibility Map of Leyte Province Overlayed
with Landslide Points
5. Results and Discussion
5.1. Leyte
Figure 10 is the SINMAP simulation of Leyte province. It
shows that flat land areas are identified as stable and mountainous areas as zones with dominantly lower and upper thresholds. Stable zones account for 69.35% of Leyte’s total land
area whereas unstable zones account for 22.75% of the area.
33
Figure 11: Shallow Landslide Susceptibility Map of Leyte Province with close
up view in an area with identified landslides
5.2. Samar
SINMAP model for Samar province is shown in figure 12.
High hazard areas are mostly concentrated on the central mountainous part of the province. These high hazard areas together with areas classified as build only with slope intervention are considered as unstable areas which constitute 28.81%
of Samar’s total area. Another 7.48% of the area is part of
marginal instability zones. Stable areas which is dominantly
found near the coastlines cover 66.72% of the total land mass
area.
Table 4 shows the accuracy of the model with respect with
the landslide inventories. Landslide points found in predicted
unstable zones comprise 75.49% of the total inventoried landslides. No landslide point falls under the marginal unstable zone
but 24.51% of the points fall under the zone predicted as stable.
A zoomed in view in an area in the northern part of Samar is
shown in Figure 13 where most of the identified landslides are
located in the predicted unstable zone.
Figure 12: Shallow Landslide Susceptibility Map of Samar Province Overlayed
with Landslide Points
In both simulated models, there are no landslide points identified in some areas regarded as mostly unstable. This is due to
lack of high resolution images in those particular sites for landslide inventory to be done. On the other hand, those landslide
points found in predicted stable areas can be explained by the
fact that some of these are old landslides. There may be some
cases when their location became more stable after the landslide event so when recent DEMs are used, they are shown as
already stable areas. Another reason for this is that the calibration parameters used has its own limitations.
34
[5] D. R. Montgomery, W. E. Dietrich, A physically based model for the topographic control on shallow landsliding, in: Water Resources Research,
1994, pp. 1153–1171.
[6] R. Orense, Slope failures triggered by heavy rainfall, Philippine Engineering Journal.
[7] C. G. A. P. R.T. Pack, D.G. Tarboton, SINMAP 2 - A Stability Index Approach to Terrain Stability Hazard Mapping, Utah State University (August 2005).
[8] R. Pack, D. Tartabon, C. Goodwin, Terrain stability mapping with
SINMAP, Terratech Consulting Ltd., Salmon Arm, B.C., Canada, report number 4114-0 Edition, report and software available online:
http://moose.cee.usu.edu/sinmap/sinmap.htm (1998).
[9] A. Witt, Using a geographic information system (gis) to model slope instability and debris flow hazards in the french broad river watershed, north
carolina, Master’s thesis, North Carolina State University (2005).
[10] C. Hammond, D. Hall, S. Miller, P. Swetik, Level i stability analysis(lisa)
documentation for version 2.0: General technical report int-285, Tech.
rep., U. S. Department of Agriculture , Forest Service, Intermountain Research Station (1992).
[11] MGB, Geology of the Philippines, 2nd Edition, Mines and Geosciences
Bureau, North Avenue, Quezon City, Philippines, 2010.
Figure 13: Shallow Landslide Susceptibility Map of Samar Province with close
up view in an area with identified landslides
Conclusion
The landslide maps generated using SINMAP are found to
be 81.40% accurate with the landslide inventory derived from
high-resolution satellite imagery dated 2003 to 2013. Accuracy
can be further improved through field detailed geological and
geotechnical assessment of the study areas with preference in
areas having satellite images with lower resolution and areas
with high count of landslide inventories.
The landslide susceptibility classification found in the landslide hazard maps are useful to identify no-build, areas that can
be built upon but with slope intervention and monitoring as well
as places that are safe from shallow landslides. These maps
complement the debris flow and structurally-controlled landslide hazard maps that are also being prepared for rebuilding
Haiyan’s devastated areas.
Acknowledgements
We thank National Mapping and Resource Information Authority (NAMRIA) for the Digital Elevation Model data. Also
to Bureau of Soil and Water Management (BWSM) for the soil
maps.
References
[1] NDRRMC, Ndrrmc situation report on the effects of typhoon yolanda,
january 22, 2014 (6:00 a.m.) @ONLINE (Jan 2014).
URL http://www.gov.ph/
[2] A. F.-P. KD Suarez, Typhoon yolanda weakens as it exits ph @ONLINE
(Nov 2013).
URL http://www.rappler.com/
[3] R. Soeters, C. VanWesten, Slope stability: recognition, analysis and
zonation, in: Lanslides: investigation and mitigation, National Academy
Press, Washington, D. C., 1996, pp. 129–177.
[4] J. F. L. S. K.M. Weerasinghe, H.V.M.P. Abeywickrema, Use of a deterministic slope stability predicting tool for landslide vulnerability assessment in ratnapura area, sri lanka, Geo Informatics Center (GIC)of the
Asian Institute of Technology (AIT), 2003.
35
Condition
Class
Predicted State
Parameter Range
Possible Influence of factors not
Modeled
SI >1.5
1
Stable slope zone
Range cannot model instability
Significant destabilizing factors are
required for instability
1.5 >SI >1.25
2
Moderately stable zone
Range cannot model instability
Moderate destabilizing factors are
required for instability
1.25 >SI >1.0
3
Quasi-stable zone
Range cannot model instability
Minor destabilizing factors could
lead to instability
1.0 >SI >0.5
4
Lower threshold slope
zone
Pessimistic half of range required
for instability
Destabilizing factors are not
required for instability
0.5 >SI >0.0
5
Upper threshold slope
zone
Optimistic half of range required
for stability
Stabilizing factors may be
responsible for stability
0.0>SI
6
Defended slope zone
Range cannot model instability
Stabilizing factors are required for
stability
Table 1: Stability Class Definitions, [7]
Soil
Density
(kg/m3 )
1954
Internal
Angle of
Friction
min
max
31
37
Cohesion
min
0
max
0.8
Hydraulic
Conductivity
(m/hr)
min
max
0.0582
0.282
Transmissivity (m2 /day )
[T = k ∗ b]
min
2.0952
max
10.152
Recharge
(m/day)
min
0.05
Approximate
T/R (m)
max
0.2
min
20
max
200
Table 2: Generalized parameter values used for SINMAP simulation corresponding to worst case scenario
Area (km2 )
% of the Area
Number of
Landslides
% of Slides
General
Stability
Stable
Build with Slope
Intervention
5025.67
69.35
420.15
5.80
Stability Class
Build only with Slope
Intervention and
Continuous Monitoring
1648.33
22.75
No Build Zone
Total
152.55
2.11
7247
100
17
2
101
14
151
12.69
1.49
75.37
10.45
100
12.69% Stable
1.49% Marginal
85.82% Unstable
100%
Table 3: Summary of land area of Leyte Province and landslide inventories under different stability class
Area (km2 )
% of the Area
Number of
Landslides
% of Slides
General
Stability
Stability Class
Build only with Slope
Intervention and
Continuous Monitoring
3281.07
25.01
Stable
Build with Slope
Intervention
8753.80
66.72
981.79
7.48
25
0
68
24.51
0
66.67
24.51% Stable
0% Marginal
75.49% Unstable
No Build Zone
Total
104.34
0.80
13121
100
9
102
8.82
100
100%
Table 4: Summary of land area of Samar Province and landslide inventories under different stability class
36

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