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JAXA Sponsored Mini Project on Utilization of Space Technology for Disaster Mitigation 2005/2006

Application of Remote Sensing & GIS Technology for Landslide Susceptibility Analysis

Introduction Most of the reported severe natural disasters in Sri Lanka are rain induced landslides and flooding. Recently, in May 2003, torrential rains accompanied by heavy winds had left an estimated 247 dead and over 200,000 families displaced due to landslides and floods. The losses from landslides are termed vulnerability. Other component is landslide hazard. Hazard assessments are estimations of an areas susceptibility to landslides based on seven inherent physical factors distribution of old landslides, combined soil cohesion, slope steepness, hydrology, landuse, human interventions (road buffer) and type of bedrock and its structure. Development of Landslide Susceptibility Map for the Kalawana District Secretariat Division in the Ratnapura District of Sri Lanka of a scale 1:50,000 developed as a part of the main research on Application of Remote Sensing and GIS Technology for Landslide Risk Assessment in Sri Lanka. The study was made to develop multi-temporal GIS database assessment including deterministic slope stability model SINMAP (Stability Index MAP) and statistical approach of Weighted Analysis Method for the identification of landslide susceptibility rating. In SINMAP model accuracy significantly depended with the DEM and input soil parameters. The factor overlay criteria or the weighted average probabilistic model indicated various conveniences in incorporation of uncertainty of combined weighted factors like human intervention in road construction in hill slopes.

Study Area The area covers 387 km2 and extents from latitude 60 35 30 N to 60 22 20 N and longitude 800 38 25 E to 800 17 23 E in the Ratnapura district. The area selected for the study is belongs to the Kalawana district secretariat division and is having relatively low population of about 48,201 due to undulating terrains, steep slopes and dense forests.

Objectives The main objective of the study is to identify the area susceptible to landslides with the use of a GIS database. The specific objectives are fall in to two major categories. One, is to use deterministic slope stability model SINMAP (Stability Index MAP) with due consideration of intrinsic triggering factors of slopes in the region. Secondly, number of extrinsic triggering factors also creates instability of slopes which is difficult to address in analytical modeling. Therefore, Weight Average Analysis Method was used to incorporate a number of intrinsic and extrinsic factors for the evaluation of areas susceptible to landslides. Final outcome is to develop more reliable model for the landslide susceptibility assessment using GIS.

Methodology Two types of approaches were considered in this study for landslide susceptibility assessment. First, deterministic modeling approach consists of number of intrinsic variables such as soil strength parameters, root cohesion and wetness index. Second, weighted average analysis considers a number of extrinsic factors which covers human intervention, landuse change, rainfall and intrinsic factors of geology, slope and stream order. Figure 1 shows a flowchart of methodologies for both methods.

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The first method was analytical approach, the evaluation of stability index determined using the ArcView extension, SINMAP. The model uses the formula for the factor of safety (FS) for the infinite slope stability model (ratio of stabilizing to destabilizing forces). In the FS formula, terrain stability model SINMAP can compute all variables from the topography, except for combined cohesion; C, tan of effective angle of internal friction of soil; tan() and wetness index, T/R. The DEM and appropriate soil parameters were the fundamental input parameters for the model. For the purpose of detailed calibration of the model, initially the Kalawana division divided in to 10 number of sub watersheds using hydrology extension of the ArcView 3.2. The analysis was further extended by sub dividing to more sub watersheds in old landslide proven areas. The evaluation of the T/R ratio and the combined cohesion ( i.e. soil cohesion and the root cohesion) was determined considering supportive landuse pattern of a watershed. The uncertainty of these parameters is incorporated through the use of uniform probability distributions with lower and upper bounds. The stability index (SI) is defined as the probability that a location is stable, calculated by considering the most and least favorable situations (i.e. either lower or upper bounds) and the probability that a certain factor of safety may be attained.

In the second method, statistical approach, a number of factor maps were used for the study. These were slope map derived from the DEM, bedrock geology map, landuse map derived from the aerial photos, soil map derived from the average combined cohesion concept (i.e. root cohesion and soil cohesion), derived map of road buffer from slopes, rainfall map and stream density map. Rainfall is the most triggering factor in most slope instability problems in hill slopes. Therefore, hydrological parameters (rainfall and stream density) were considered to obtain reasonably accurate weight for the analysis. Three day cumulative rainfall distribution pattern in a particular watershed and total number of 1st order streams in the watershed were considered. The evaluation of weighted average was made through the Analytical Hierarchy Process (AHP). Firstly, factor preference from 1 to 9 was allocated for each factors map convert them in to quantitative value through the model calibration using known landslide data base.

After the valuing of area with regard to seven factors, at present, the values of seven factors classes X are multiplied by derived individual weights for each factor (w1..w7) and then are summed together. Then the total value M1 for each pixel and the regional model will be derived:

M1 = w1X1 + w2X2 + w3X3 + w4X4 + w5X5 + w6X6 + w7X7 And with replacing the combined weights (w1..w7) that had been earned previously, the final model was derived: M = w1X1 + w2X2 + w3X3 + w4X4 + w5X5 + w6X6 + w7X7

Where: M = Susceptibility coefficient X1X7 = orderly are related to slope, soil, geology, landuse, hydrology 1(rainfall), hydrology 2(1st order streams) and road buffer and, w1. w7 = are the weights related to each X1. X7 factors. M variations from 0 to 1, thereafter 5 number of susceptibility classes were assigned as high values attain high susceptibility landslides.

Field Survey & Laboratory Findings Laboratory tests of soil reveled that liquid limits were in between 27% to 44%. Water content and the plasticity index decreases with the depth. The fine content of soils ranges from 4% to 60%. The dry density increases marginally with depth due to in-situ and completely weathered rock with an average value of 1.5~1.6 and 1.89 Mg/m3, respectively. The specific gravity of the soil averages from 2.6 to 2.9. Laboratory test for shear strength showed an average effective cohesion, C, of 6.5 kPa to 15.5 kPa with effective angle of internal friction, from 26 to 35. At shear boundary effective cohesion, C = 0 kPa and effective angle of internal friction, = 22.

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Results & Discussion

The deterministic method of approach was based on the use of the terrain stability model SINMAP. The analysis fairly well defines areas that intuitively appear to be susceptible to landslides. It was noted that few landslides occurred on slope that would not normally be recognized as susceptible to landsliding area. This means that methodology missed classifying several of these sites as being landslide-prone due to the site-specific geologic conditions and inaccurate input parameters of soil. The wetness index is indicated more inaccuracy during preliminary modeling of the area and therefore, re-calibration was made by further subdividing of landslide proven watershed areas of the main Kalawana watershed. The out put results were shown in the Table 1 and 2 before and after recalibration.

Table 1: Comparisons of SINMAP results of the model after re- calibration of the watershed number 5 of the main watershed (Kalawana Division)

Results of SI of the sub watershed number 05 of Kalawana main watershed before re-calibration of results

Results of the SI after re-calibration of watershed number 05 in to further 07 sub watersheds

Stability Index (SI) and Class of

stability against landslides occurrence

% Area fall in the stability

class

No. of Landslides fall in the category

Density of Landslides

LS/km2

% Area fall in the stability

class

No. of Landslides fall in the category

Density of Landslides

LS/km2

Stable 50.1% 4 0.2% 45.6% 4 0.1% Moderately

Stable 7.9% 1 0.5% 6.4% 0 0.0% Quasi-stable 12.6% 7 0.2% 10.4% 2 0.3%

Lower Threshold 28.1% 3 0.2% 35.0% 8 0.4%

Upper Threshold 1.3% 1 1.4% 2.8% 2 1.3%

Total 16 0.2% 16 0.2%

Table 2: Comparisons of SINMAP results of the model after re- calibration of the watershed number 08 of the main watershed (Kalawana Division)

Results of the SI of sub watershed number 08 of Kalawana main watershed before re-calibration of results

Results of the SI after re-calibration of watershed number 08 in to further 07 sub watersheds

Stability Index (SI) and Class of

stability against landslides occurrence

% Area fall in the stability

class

No. of Landslides fall in the category

Density of Landslides

LS/km2

% Area fall in the stability

class

No. of Landslides fall in the category

Density of Landslides

LS/km2

Stable 45.6% 3 0.1% 40.1% 2 0.04% Moderately

Stable 9.1% 2 0.2% 7.4% 0 0.0% Quasi-stable 12.9% 1 0.1% 11.2% 5 0.4%

Lower Threshold 29.1% 16 0.5% 35.4% 12 0.3%

Upper Threshold 3.1% 0 0.0% 5.0% 3 0.5%

Total 22 0.2% 22 0.2%

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Table 3: Results of the Landslide Susceptibility Analysis of the Kalawana Division % Area fall in the equivalent stability class according to the

susceptibility criteria

Sub-watershed number 05 of main

watershed

Sub-Watershed number 08 of main

watershed

Overall results of the Kalawana Main

Watershed

RESULTS

WAA Use of Weighted Average Analysis method of assessment model SINMAP- Use of terrain stability model of Stability Index Mapping (SINMAP) WAA SINMAP WAA SINMAP WAA SINMAP No Susceptibility 46.45 45.6 22.07 40.1 38.67 52.49

Low Susceptibility 8.09 6.4 25.53 7.4 18.99 8.90 Moderately Susceptibility 26.29 10.4 25.44 11.2 22.80 12.11 High Susceptibility 15.78 35.0 21.04 35.4 16.30 24.11 Very High Susceptibility 3.39 2.8 5.91 5.0 3.24 2.19

The above finding indicates the variability of conceptual models and their relative importance with the input databases. The detailed and comprehensive geographical information databases are required to obtained the statistical interpretation and calibration of the model. Similarly, soil saturation conditions and the validity of wetness indices within a watershed also create another avenue to calibrate the analytical model with the inputs of other soil parameters.

Conclusions & Recommendations Space technology using satellite and aerial remote sensing plays a very important role in terrain mapping and scientific assessment of the ground conditions. This technology is ideally suitable for inaccessible mountainous regions where majority of old landslides were identified. By using multi-temporal satellite data the areas of landslide every year can be compared with other prediction variables such as altitude, slope, nearness to settlements, road access and grave sites, and the areas most susceptible to landslide in a particular year can be flagged for extra precautionary measures to be taken. One of the critical observations of the study is a lack of complete data base of recent occurrences of old landslides on May, 2003. Only 39 landslides were used out of 78 case records at Kalawana to calibrate models due to inaccessibility at field. Therefore, multi-temporal satellite data may be only solution to overcome such problems and acquire more geographical information. In landslide study high spatial resolution satellite photo images are recommended. The satellite imagery data allowed generation of high-resolution Digital Elevation Models (DEM). The derived relief parameters can be analyzed in a GIS together with other information obtained from remote sensing data, thematic maps and field observation for a spatially differentiated terrain properties as a basis for further assessment of landslide hazard.

The further study should not be restricted to ALOS, LANDSAT TM and IKONOS data, but there would appear to be great potential in using this and other remotely sensed data, such as airborne radiometrics (Cranfield, 2003, pers. comm.) to map specific ground conditions including the identification of areas of alteration and deep weathering that may be additional predisposing factors for landsliding. In the longer-term, such work will serve to inform relevant sectors of local government of the potential risks associated with major land development projects.