District Ghizer is a rugged mountainous territory which experiences several landslides each year. There are 16 major landslide areas and 53 villages that are at high risk to hazards. Keeping in view the severity of natural hazards, the present study was designed to generate landslide susceptibility map based on twelve causative factors viz., slope, aspect, elevation, drainage network, Stream Power Index (SPI), Topographic Wetness Index (TWI), lithological units, fault lines, rainfall, road network, land cover and soil texture. Soil texture was determined by particle size analysis and data for other factors were acquired from freely available sources. Analytical Hierarchy Process (AHP) was employed to identify major landslide causative factors in the district Ghizer. Further, a temporal assessment from 1999 till 2015 was generated to assess the impact of land cover change on landslides. It indicated that the barren soil/ exposed rocks and glaciers have reduced while the vegetation and water classes have shown increment. The total area that lies in moderate to very high landslide susceptible zones was 74.38%, while slope is the main landslide causative factor in the district Ghizer. Validation of the susceptibility map showed 88.1% of the landslides in the study area had occurred in the moderate to very high susceptible zones.
Most of the northern part of Pakistan is located in snow-covered mountains. The steep relief, snow, and glaciers, in the region, are exceptional but strong precipitation and a high seismicity contributes to the origin of widespread natural processes like debris flow, flash floods, earthquakes, rockfall or landslides [
The Landslide is a consequence of multifarious interaction within various factors, such as meteorological, geomorphological and geological. The spatial information associated with mentioned factors is able to extract from remote sensing facts, land-based information, along with quite a lot of other data resources. Landslide susceptibility maps illustrate the comparative possibility of future landslide based exclusively on the fundamental properties of a setting or site. The AHP helps in generating the weight of every factor, which is then used in Geographic Information System (GIS) to create landslide susceptibility maps.
Even though Gilgit Baltistan is highly susceptible to landslides, merely a few studies have been carried out in the region [
The district Ghizer lies in Hindu Kush region of Pakistan in the northern part of Gilgit-Baltistan, between latitude 36.0˚N to 37.0˚N and longitude 73.0˚E to 74.0˚E covering an area of 12,042 km2. The estimated terrain elevation above sea level is 3661 meters and the habitat is arid to semi-arid. The region has four Tehsils i.e. Gupis, Ishkoman, Punial and Yasin (
are frequent in the area [
The road from district Ghizer to district Gilgit is highly susceptible to landslides because of erosion and rock fall as the slopes are made of muddy dust and loose sediments. There exists sixteen major landslide areas and thirteen small villages prone to rock fall, so as a whole 53 villages are considered to be at high risk to hazards in district Ghizer [
In this study, twelve factors were used to generate the landslide susceptibility map. The factors were totally selected on the basis of their effectiveness and availability. According to Oh and Pradhan [
The slope angle ranges from 0˚ - 73.76˚ for the study area (
weights to southwest, west, and northwest facing slopes. The lowest point of district Ghizer is at elevation of 1662 m and the highest point of elevation is at 6789 m (
From the geological map of scale 1:5,000,000 fourteen rock types were digitized, which was acquired from Geological Survey of Pakistan (
Lithological Unit | Formation Group | Description | Stability Class | Category |
---|---|---|---|---|
Ca | Northern Karakorum Terrane | Black slate, phyllite, arkose, Lun Shale, Gircha and Misgar Slate. | Highly Stable | D |
Cv | Kohistan Terrane and Shyok Suture Zone ―Kohistan Arc Sequence | Calc-alkaline andesites, high Mg tholeiites and boninites | Very Highly Stable | E |
Sv | Kohistan Terrane and Shyok Suture Zone ―Kohistan Arc Sequence | Basaltic andesite, rhyolite, pryroclastic flows, ignimbrite and volcanic breccias | Very Highly Stable | E |
Pm | Northern Karakorum Terrane | Permian Sedimentary Rocks: Permian massive limestone | Less Stable | B |
Kb | Karakoram Batholith | Trondhjemite, calc-alkaline gabbo-diorite, hornblende cumulates, Plutons. Biotite ± muscovite ± garnet leucogranite | Less Stable | B |
Ssm | Kohistan Terrane and Shyok Suture Zone | Suture mélange (limestone, quartize and serpenite in a shely matrix | Less Stable | B |
Skm | Southern Karakoram Metamorphic Complex | Paragneisses including interhanded pelite, marble, amphibolite with rare ultramafic lenses (Panmah unit). Pelites containing micas, garnet, staurolite, kyanite, sillimanite + muscovite and sillimanite + K-feldspar assemblings metamorphosed. Low P-T pelites aroun the Chinkiang valley contain chloritoid + chlorite + biotite. | Moderately Stable | C |
Tr | Northern Karakorum Terrane | Triassic Massive Limestone and Dolomite with distinct conglomerate horizon; Slate | Moderately Stable | C |
Y | Kohistan Terrane and Shyok Suture Zone ―Kohistan Arc Sequence | Limestone containing Orbitolina sp. and Radist sp. Sandstone, shale and meta-volcanic rocks | Moderately Stable | C |
HPU | Karakoram Batholith | Plagioglacase + Quartz + horneblende + biotite ± garnet ± K-feldspar | Very Highly Stable | E |
GB | Karakoram Batholith | Plutonic unit (k-feldspar + quartz + plagioclase + bioite + garnet) | Very Highly Stable | E |
GL | Glacier/snow | Least Stable | A | |
Gm | Kohistan Terrane and Shyok Suture Zone ―Kohistan Arc Sequence | Meta-sedimentary rocks, greenschist facies slate, phyllite and psammite; protoliths; Peshmal schists; granties | Highly Stable | D |
Ec | Besal Eclogites | Omphacite-garnet + Quartz + rullite ± amphibolite ± metamorphosed phengite eclogites, Protoliths | Highly Stable | D |
Categories contribution in the hazard in the study area on the basis of their stability. A (Very Highly Stable), B (Highly Stable), C (Moderately Stable), D (Less Stable), E (Least Stable). |
rock formation is categorized into very highly stable, highly stable, moderately stable, less stable and least stable class; based on the geotechnical properties of the rock units present in the formation. Less stable rock units are highly prone to slope failures which cause landslides. Geological map of scale 1:5,000,000 was used to digitize the fault lines of the study area which were acquired from Geological Survey of Pakistan (
Soil sampling for soil texture was performed by taking twelve composite samples from each tehsil of the study area. The samples were air-dried and sieved through 2 mm size sieve. Forty ml of 1% sodium hexa meta-phosphate and 150 ml of distilled water was added to soil sample (40 g) and was kept overnight. The mixture was stirred for almost 10 minutes and was put in a graduated cylinder for readings, which was recorded with Boyoucos Hydrometer method [
The Analytical Hierarchy Process (AHP) is an adaptable tool which is created by [
In the comparison matrix, the numerical value for each factor was between 1 and 9 (
Scale | Degree of preference | Explanation | |
---|---|---|---|
1 | Equal importance | Contribution to objective is equal | |
3 | Moderate importance | Attribute is slightly favored over another | |
5 | Strong importance | Attribute is strongly favored over another | |
7 | Very strong importance | Attribute is very strongly favored over another | |
9 | Extreme importance | Evidence favoring one attribute is of the highest possible order of affirmation | |
2, 4, 6, 8 | Intermediate values | When compromise is needed | |
Source: Saaty 1977.
The average of the hierarchically arranged factors was used to calculate the weights and rating value/eigenvalue along with the Consistency Ratio (CR), based on the prepositions of [
CI = Consistency Index which is as follow:
C I = λ max − n n − 1 (1)
The consistency of the comparison matrix is checked through CR (Saaty 1977).
C R = C I / R I (2)
where RI = Random Consistency Index.
[
The calculated CR from the comparison matrix for the 12 factors was 0.028. This value demonstrates that the matrix of the factors is acceptable. The result of AHP showing weights of causative factors (Wj) and the factor rating values (wij) are given in the (
N | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.2 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.53 |
Source: Saaty 1977.
Factors | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Weights | Factor Rating |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Slope (1) | 1 | 2 | 3 | 4 | 5 | 5 | 6 | 7 | 7 | 8 | 8 | 9 | 0.2598 | 9 |
Distance to fault (2) | 1/2 | 1 | 2 | 3 | 4 | 4 | 5 | 6 | 6 | 7 | 7 | 8 | 0.1916 | 8 |
Lithology (3) | 1/3 | 1/2 | 1 | 2 | 3 | 3 | 4 | 5 | 5 | 6 | 6 | 7 | 0.1397 | 7 |
Land Cover (4) | 1/4 | 1/3 | 1/2 | 1 | 2 | 2 | 3 | 4 | 4 | 5 | 6 | 6 | 0.1002 | 6 |
Elevation (5) | 1/5 | 1/4 | 1/3 | 1/2 | 1 | 1 | 2 | 3 | 3 | 4 | 4 | 5 | 0.0696 | 5 |
Distance to Roads (6) | 1/5 | 1/4 | 1/3 | 1/2 | 1 | 1 | 2 | 3 | 3 | 4 | 4 | 5 | 0.0696 | 5 |
Distance to Drainage (7) | 1/6 | 1/5 | 1/4 | 1/3 | 1/2 | 1/2 | 1 | 2 | 2 | 3 | 3 | 4 | 0.0476 | 4 |
Soil (8) | 1/7 | 1/6 | 1/5 | 1/4 | 1/3 | 1/3 | 1/2 | 1 | 1 | 2 | 2 | 3 | 0.0319 | 3 |
Rainfall (9) | 1/7 | 1/6 | 1/5 | 1/4 | 1/3 | 1/3 | 1/2 | 1 | 1 | 2 | 2 | 3 | 0.0319 | 3 |
TWI (10) | 1/8 | 1/7 | 1/6 | 1/5 | 1/4 | 1/4 | 1/3 | 1/2 | 1/2 | 1 | 1 | 2 | 0.0212 | 2 |
SPI (11) | 1/8 | 1/7 | 1/6 | 1/6 | 1/4 | 1/4 | 1/3 | 1/2 | 1/2 | 1 | 1 | 2 | 0.0212 | 2 |
Aspect (12) | 1/9 | 1/8 | 1/7 | 1/6 | 1/5 | 1/5 | 1/4 | 1/3 | 1/3 | 1/2 | 1/2 | 1 | 0.0157 | 1 |
CI (consistency index) = 0.0439 RI (random consistency index) = 1.53 CR (Consistency ratio)= 0.028, <0.1 acceptable |
Weighted Linear Combination (WLC) is comprised of both subjective and quantitative strategies and depends on the qualitative map combination approach (heuristic analysis) [
L S I = ∑ j = 1 n W j w i j (3)
where LSI is Landslide susceptibility index, Wj is weight value for parameter j, wij is rating value or weight value of class I in parameter j and n is no. of classes.
The weights of the factors; slope, aspect, elevation, drainage network, SPI, TWI, lithology, fault lines, rainfall, roads, land cover land use and soil were derived using AHP by Prioritized Factor Rating Value (PFRV) (
Based on the above categorization, the area and percentage of the five susceptibility classes were also determined (
The topographic, geologic, and hydrologic factors causing landslides are considered as stationary, while land cover is the factor that can change within a short time; therefore it is in a direct relation to landslide occurrence [
land cover have been observed. The changes are visible in the classified maps (
There is number of methods to validate a susceptibility map. One such method is computing landslide frequency/density in the susceptibility classes [
The observed landslides in the very high susceptible zone were 38.2% with a landslide frequency of 0.0059, which was found to be the largest among other
susceptibility classes. The high, moderate, low and very low classes showed frequencies of 0.0035, 0.001477, 0.001471 and 0.00096 respectively. The overall validation result shows that 88.1% of the landslides have occurred in the moderate to very high susceptibility zones (
The weight values of each factor in AHP shows the level of in the landslide. Results showed that slope, distance from fault lines and lithology of the study area have the greatest impact on landslide hazard. It is evident from the results that most of the landslides occur in the gentle to moderate slopes. It has been observed that 20˚ to 40˚ slope angles are considered very susceptible to landslides [
Moreover, the finding demonstrated that the weaker rocks which are loosely held are more prone to falling. It is widely recognized that geology of an area, greatly influences the occurrence of landslides and rock falls in that particular area. Because every rock type has different composition and that leads to a difference in permeability [
Land cover has been considered an important factor in the study because barren slopes are widespread as the vegetation is mainly around the villages and few rangelands are present in the high mountains [
In the presented study, GIS techniques and AHP were applied to create landslide susceptibility map. Based on the achieved results, a large area in the district consists of moderate and high landslides prone zones. The produced susceptibility map was compared with randomly selected landslides for validation; landslide frequency/density was computed from observed landslides in the study area, which also indicated that highest frequency of landslides is in the very high susceptibility zone. Besides producing the landslide susceptibility map for the study area, temporal assessment of land cover change in the district Ghizer for the years 1999 and 2015 was investigated to study the impact of land cover on landslide susceptibility. Based on the results it can be stated that vegetation and water class has increased within the sixteen-year time span while the glaciers and barren soil/exposed rock classes have reduced. This approach can be applied to the landslide susceptibility mapping in other regions in the world. However, it is important to assign appropriate weights to the specific landslide-controlling factors, because it is mostly attributable to the nature of the terrain and type of landslide.
Rahim, I., Ali, S.M. and Aslam, M. (2018) GIS Based Landslide Susceptibility Mapping with Application of Analytical Hierarchy Process in District Ghizer, Gilgit Baltistan Pakistan. Journal of Geoscience and Environment Protection, 6, 34-49. https://doi.org/10.4236/gep.2018.62003