open access freely available online journal of geology

1J Geol Geophys, Vol. 10 Iss. 3 No: 986

OPEN ACCESS Freely available online

Journal of Geology & GeophysicsResearch Article

Correspondence to: Ayele NA, Department of Geodesy and Geodynamics, Ethiopian Space Science & Technology Institute, Addis Ababa, Ethiopia, Tel:+251940433362; E-mail: [email protected]

Received: March 30, 2021, Accepted: April 13, 2021, Published: April 20, 2021

Citation: Ayele NA, Cherie SG (2021) Landslide Susceptible Mapping Using InSAR and GIS Techniques: Overview of Debresina Area, Ethiopia. J Geol Geophys. 10:986

Copyright: © 2021 Ayele NA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

INTRODUCTIONLandslide is used to describe various processes that result in the detectable downslope and outward flow of a slope material (soil, rock, and vegetation) under gravitational influence [1]. The materials may move by: falling, toppling, sliding, spreading, or flowing [2]. Landslides can be triggered by both natural and manmade changes in the environment. The main causes of landslide are classified in to two:

• Inherent reasons of slope failure such as; weakness in the geological composition or geological structures within the rock or soil.

• External triggering mechanisms such as; rainfall, earthquake or volcanic activity, snowmelt and change in ground water level and human activity [3,4].

The survey area located in central Ethiopia is frequently affected by natural hazards such as flash floods and geomorphic process

by which soil, sand, regolith, and rock move downslope typically as a solid, mass, largely under the force of gravity. Additionally, landslides of different types, which frequently lead to eviction of populations and damage to housing, infrastructures and vast area of land suitable for growing crops and death of human lives are recurrently observed [5]. Most of the landslides in this area, including the major ones, are caused by substantial rainfall and springs due to groundwater level rise [6,7]. In addition, earthquake triggering slope failures, which resulted in rock slides, toppling and rock falls are reported by various workers [8]. Even if various investigations have been carried out in Debresina area, these authors utilized InSAR and GIS techniques to investigate landslide occurrences in the region.

Based on local terrain situations, the likelihood of a landslide occurring in a region is referred as landslide susceptibility (LS). To predict future landslides of an area, it is very essential to map the previous and current landslides occurring in the project area. In this case we conducted susceptibility investigation of the known

ABSTRACTLandslide is the major geohazard in the Ethiopian highlands. We used InSAR and Arc-GIS techniques to map landslide susceptibility. SAR images generated from sentinel 1A are utilized for landslide displacement investigation using InSAR method. GIS analyses result showed that it is a powerful technique to landslide zoning in to low, moderate, high and very high, susceptibility whereas wrapped Interferogram shows a displacement of -12 to 12 mm/yr. Thus, very high and high classes of landslide zoning overlapped on the highest displacement regions. Results were validated with GPS data using R-index method. Accordingly, 79.45% of sample point’s fall on the displacement map (Interferogram) and the same percent of those points overlapped to very high and high landslide zones. Landslide triggered deformation and landslide susceptibility mapping can be well studied using InSAR, which has a capability to give constant coverage and accuracies better than that of classical studies. To interpret and integrate different InSAR results GIS is utilized as a value-adding technique throughout the InSAR process. This means InSAR and GIS method can be utilized for landslide vulnerability mapping but for more accuracy and quantification of landslide displacement.

Keywords: Landslide susceptibility; InSAR; GIS; Overlay

Landslide Susceptible Mapping Using InSAR and GIS Techniques: Overview of Debresina Area, EthiopiaSolomon G. Cherie, Natnael Agegnehu Ayele*

Department of Geodesy and Geodynamics, Ethiopian Space Science & Technology Institute, Addis Ababa, Ethiopia

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Ayele NA, et al. OPEN ACCESS Freely available online

landslides happening in the Debresina area.

Rendering to the degree of actual or potential threat, LS zonation indicates to the division of the land in homogeneous regions or domains.

Currently, assessing a disaster phenomenon is presumed to be an important technique for LS mapping. To developing mitigation plans and pick the more suitable locations for construction, the cartographic results of LS modeling can be used. Geographic Information Systems (GIS) technique has a powerful capability to deal with enormous volume of spatial data. LS is believed to be analyzed by using effective spatial analysis tools. Many GIS-based approaches have been developed for LS assessment [9].

Synthetic Aperture Radar (SAR) images contain both the intensity and phase information of the return signals from the earth surface. Whereas SAR Interferometry (InSAR) is a technique in which two SAR images of the similar area at the surface of the earth taken from slightly diverse satellite positions are used to generate an Interferogram representing the phase difference among the return signals in two images [10]. Over the past 20 years, space-borne Interferometric Synthetic Aperture Radar (InSAR) has developed rapidly and has proven to be an important tool for topographic mapping and surface deformation measurements [11]. Because of its detail spatial coverage and competitive accuracy, InSAR has now turn out to be one of the utmost favored geodetic methods to study surface deformation associated with slow-moving landslides [12,13]. The operational space born SAR sensor that makes the availability of SAR data currently for professionals across industries in this study was Sentinel-1. This is due to the fact that Sentinel-1 is relatively good SAR image, which can be utilized for land deformation analysis including landslide mapping.

SARPROZ software is powerful software used to process and analyze SAR data and generate products of Digital Elevation Model (DEM) or surface deformation maps, which gives the choice to integrate this information with other geospatial products. This unique data analysis capability of SARPROZ converts the data to meaningful and contextual information

In this work, a robust geodetic method of SAR interferometry (InSAR), which has not been used in the Debresina area as well as GIS technique of higher spatial resolution were utilized. Therefore, landslide hazard and vulnerability of the lives, infrastructure and property of the community living in the area calls immediate attention to conduct detail investigation using the above mentioned techniques and recommend immediate remedial measures. The major objectives of this research were to

• Process multi temporal SAR image using InSAR techniques for landslide mapping,

• Investigate and delineate landslide susceptibility areas using GIS technique and

• Compare landslide resulted from InSAR and GIS techniques.

Description of the study area

The survey area is situated between 39°29’00’’ to 39°55’00’’ longitude and 9°44’40’’ to 10°10’20’’ latitude of Amhara Regional State, central Ethiopia and has area coverage of 1760.1 sq.km (Figure 1). Located at the western escarpment of the MER, Debresina area depicts a rugged topography shaped by easterly facing and moderately steeply sloping mountain chains, rift parallel normal faults and lineaments, and easterly draining deeply dissecting rivers. As described by Debresina area grossly have 1400-3200 m altitude range and an average annual rainfall of 1850 mm. It covers major landslide localities such as Mafud, Kewet, Lalomeder and Mamamidr and to some extent localities north of Ankobre Sub district. Closest cities including Debresina are Mezezo, Shewarobit, Sela dingay, Armanya, Tarmaber and Gudoberet.

DATA AND METHODOLOGYData sources and acquisition techniquesIn this study, all pertinent data were collected from primary and secondary sources (Table 1). By using direct and intensive field observations, GPS primary data using hand held GPS were collected whereas secondary data such as soil, DEM and Landsat image for GIS and Sentinel images for InSAR purposes were collected from various sources and are shown in (Table 1).

Figure 4a: Low power shows a cystic lining epithelium and tumour islands in the stroma. Hyperchromatic basal cell layer and loosely arranged suprabasal layers are also appreciated.

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Software and instruments to be utilizedTo undertake this study, the following software and instruments were utilized. These are SARPROZ-to process and analyze SAR data and generate landslide displacement map using InSAR (PS-InSAR); ArcGIS-to produce landslide map by taking controlling factors; Handheld GPS-to collect landslide inventory data for landslide validation.

Methods of landslide mappingThere are different methods of landslide mapping among which InSAR and GIS techniques are widely used ones [14,15]. In this study, InSAR and GIS techniques are employed to investigate deformation triggered by landslide. The complete methodology and flowchart of this work is presented in Figure 2, which depicted all data types and produced maps including software used to accomplish this investigation.

InSAR method: SAR interferometry is used for the detection of Earth´s surface displacements through Differential Interferometry (DInSAR), which has shown to be a tool of great potential over the last decades. Initial single interferogram techniques, commonly referred to as conventional DInSAR has changed to advanced DInSAR technique which provides information on the temporal evolution of the ground displacement, with a theoretical millimeter precision under favorable conditions. Persistent Scatters (PS-InSAR) is also one of the advanced DInSAR technique methods that work on localized targets.

Such technique has been used to ground displacements such as landslide. The basic concept of DInSAR technique is to monitor an area through time on a regular basis. In order to produce a set of differential interferograms that comprises of information on the interferometric phase, SAR images acquired in various dates are combined in pairs.

Possibly, differential interferograms would comprise only the ground displacement part amid the acquisition times of the two SAR images. However, in reality, it is presumed to be other terms contributing to the interferometric phase that can mask the anticipated surface displacement information, e.g. phase contributions from atmospheric water vapor.

The goal of the different processing techniques is to accurately isolate the displacement term from the remaining components [16].

For this work, 23 Sentinel-1A data of single look complex (SLC) images in interferometry wide swath mode (IW) with VV polarization and descending direction for two acquisition were registered as master and slave, respectively, in SARPROZ software. SARPROZ is MATLAB-written software designed for Multi Temporal Synthetic Aperture Radar processing providing modules for SAR and InSAR data analysis. Licensed software which is developed by Dr. Daniele Perissin for running InSAR data was utilized. Although it supports most existing satellite formats and imaging modes, it has no focusing module. As a result, Single Look Complex (SLC) data are a must [17].

GIS method (Weighted linear combination): Weighted Linear Combination (WLC) is a hybrid amid qualitative and quantitative methods and is extensively used for landslide susceptibility and hazard mapping due to its application flexibility and expertise knowledge dependability [18]. WLC is commonly used among researchers and landslide specialists, but lacks clear refining steps to derive higher degree of accuracy probability maps.

Based on combining weighted averages of a number of constraints, which are carefully chosen by the expert, WLC technique is applied. There are no criteria set but clearly depends on skilled knowledge and the result can significantly differ among experts. Each parameter is categorized and multiplied with its given weight and within a GIS overlay environment; the weighted averages are further added to obtain the ultimate output map [19].

No. Types of data Source Purpose

1 DEM SRTM(30 m Resolution)To produce slope and elevation maps of the study;

To generate stream feature to determine the distance from the stream

2 Landsat-8/OLI-TIRS USGS To prepare NDVI map;

3SAR images

(Sentinel-1A C-band SAR mission)Copernicus hub

To conduct InSAR particularly PS-InSAR techniques(to generate landslide displacement map)

4 Soil data Minister of Water and Resource (MoWR) To prepare soil map

5 Road feature Ethiopian road network website (Eth_trs_

roads_osm) To produce road map showing relative distance from

road

6 GPS data Field observation using hand held GPS For validation of landslide results

Table 1: Types of data used and their sources.

Figure 2: Flow chart of methodology of the study.

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landslide susceptibility of a slope [24]. The elevation map of this work was produced from 30 m resolution ASTER DEM data set by reclassifying techniques (Figure 3). Please provide sufficient background for this factor as you did for other factors in the current study.

Soil type: It is among the landslide controlling factors to determine which type of soil and the tendency of soil particle to resist sliding across each other. The nature of the movement is, controlled by the earth materials involved. Soil type based on their color can affect landslide. Accordingly, eight type of soil were generated from 1:250,000 digital soils and these are chromic cambisols, chromic vertisols, eutric cambisols, eutric nitisols, eutric regosols, leptosols, pellic vertisols and vertic cambisols. Chromic vertisols and pellic vertisols depict black color; chromic cambisols, eutric cambisols, eutric regosols, leptosols and vertic cambisols show brown color whereas eutric nitisols has red color. The black soils, brown soils and red soils have 0.15, 0.20 and 0.25 erodibility k factors, respectively [25,26]. These imply that in high landslide areas red colors which have 0.25 erodibility are highly affected by erosion whereas black colors with 0.15 erodibility are less affected by erosion obviously showing less landslide prone area (Figure 3). From this argument one can observe that erosion aggravates landslide; clearly indicating that greater erosion can be resulted in greater landslide.

Normalized difference vegetation index (NDVI): Vegetation covers as well as land use patterns, which relate to the anthropogenic interference on hill slopes, are mainly found to be of great influence in landslide existence [27] .Vegetation cover plays a significant role in reducing soil erosion. Extensive network of root system provides natural interlocking anchorage of the soil layer along slopes. Highly vegetated and sloppy area normally decreases the effects of soil loss along the slopes and this plays a major role in minimizing susceptibility of landslides and mass movements in the region whereas unfertile area with fewer or no vegetation is extremely prone to erosional activities and then to various landslides. In this work, NDVI map was produced from Landsat-8 image (OLI-TIRS sensor) with acquisition date of May 08, 2020 using ArcGIS environment particularly raster calculator (Figure 3).

Distance from the road and stream: It is argued that the nearest distance to the roads and drainage network show the most susceptibility to landslide occurrence due to the natural slope disruption by roads construction and slope-foot erosion by rivers [28]. For this research, proximity to road map (m) was generated by clipping from Ethiopian Road Network and proximity to stream map (m) was also generated according to the distance from stream feature which was produced from aster DEM data set at ArcGIS environment particularly using Euclidean distance tool.

RESULTS AND DISCUSSIONLandslide hazard zonation

Landslides broadly are presumed to be one of the greatest destructive and widespread geohazard occurrences resulted

Natural hazard prone areas, such as landslide can be mapped by assigning equal importance (weight) for all parameters [20]. Accordingly for this study, all factors that control the result were given equal importance. In WLC, the weight of each parameter considered is added by means of overlay as shown below.

Where S is landslide susceptibility, Wi weight factor and Xi criterion score of factor. The different factors are combined by adding their weight to obtain the final output.

Landslide-influencing factorsThere are no standard guidelines to select important factors for landslide mapping. However, the nature of the survey area, the scale of the analysis, the data availability and the general literature guidelines were taken into account [21]. The conditions for landslide to happen are slope, elevation, soil, NDVI, distance from road and stream. These factors used to determine the landslide susceptibility map using overlay analysis in GIS environment.

Slope: Slope aspect that relate to sunlight exposure and drying winds govern the soil moisture and is accepted as an important element. The slope angle is an important controlling factor for landslide hazard analysis in landslide survey. The probability of slope failure increases, as the slope becomes steeper and landslide frequency is expected to be minimal in gentle slopes [22]. The slope map of the project area was generated from ASTER data of 30m resolution and is done by producing DEM using GIS environment (Figure 3).

Elevation: For the assessment of landslide susceptibility, elevation, which may be associated to various environmental settings such as vegetation types and rain fall, is broadly used [23]. Elevation is considered to be important causative factor that affects the

Figure 3: Landsline controlling factors.

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30 m

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in loss of life and property as well as severe damage to various infrastructures. Major landslide triggering factors observed in Debresina area are slope, elevation, soil, NDVI and distance from steam and road features. Thus, for the study area landslide susceptibility map was produced based on these triggering features. However, heavy precipitation and associated numerous springs related to groundwater, rugged morphology, unplanned farming as well as reactivation of old landslides are also believed to be landslide causing factors in the region.

The sloppy area is mainly devoid of vegetation and is covered by weathered and disintegrated basalts and colluvial deposits of very high porosity and permeability. Besides, frequent agricultural activities are depicted in gently sloping areas covered by disintegrated basalts and colluvial deposits. These deposits are therefore characterized by very low shear strength that resulted in high susceptibility for slope instability [29]. Several scholars believed that elevation is one of the major landslide influencing factors [30]. In Debresina area, most elevated places are characterized by numerous agricultural activities of unwise nature where old as well as fresh landslide activities are clearly observed. Additionally, elevation may be ascribed to degree of weathering and intensity of erosion, strongly suggesting that higher elevation will result in higher intensity of erosion and weathering. Therefore, such elevated areas become more exposed for slope instability.

Spatial relationship between the occurrence of landslides and each landslide causative factor class was derived using overlay analysis (WLC) in GIS environment. The resulted landslide map was categorized in to four susceptibility zones i.e. low, moderate, high and very high. Such classification supported with various research findings [31]. According to the landslide result map (Figure 4), the survey area is covered with 280.5 sq.km (15.9%), 754.2 sq.km (42.9%), 544.2 sq.km (30.9%) and 181.2 sq.km (10.3%) of low, moderate, high and very high susceptibility, respectively, which needs serious attention to protect present and future loss of life, infrastructure and property.

Displacement map (Interferogram)

The landslide deformations in Debresina area is analyzed by using PS-InSAR (advanced D-InSAR) technique, which is a powerful technique to observe displacement in the survey area especially in very high and high hazard zones depicted in the landslide hazard zonation map. Therefore, the main difference between previous studies and this research is a combination of landslide susceptibility maps extracted from InSAR and GIS data.

The analyzed deformation time-series revealed presence of displacement from 9 to -27 mm/year in the velocity of landslide movement along slopes. The PS-InSAR results depicted that the process of surface displacement in the area is still active. Unwrapped phase applied to get continuous deformation phase value. The time-based phase difference for each PS pixel are calculated and then unwrapped spatially from reference PS pixels by means of an iterative least square method. PS pixel is also filtered using Goldstein phase filter before performing unwrapping. Wrapped interferograms from descending orbit data are acquired over Debresina landslide area and the color represents 12 and -12 mm of displacement, which can then be estimated using the phase values of the individual PS pixels (Figure 5).

A coherence map is generated to clearly show the correlation between master and slave images, and it determines the quality of the resulted interferogram [32]. In this study, 0.3 coherence level was used to generate coherence map shown in (Figure 6). Multi look process was applied for unwrapped, wrapped and coherence map to reduce the speckle appearance and to improve the image interpretability.

Validation of landslide susceptibility map

The produced landslide map was validated using Relative landslide density index (R-index, shown in equation 2) method, which helped to assess the relationship between the landslide susceptibility and landslide inventory map or ground control map (GCP). The sample points were collected in field investigation using handheld GPS. The validation was carried out by using overlay GCP points on susceptible map. In this technique, susceptibility zones are correlated with inventory data (Figure 7).

Figure 4: Landslide susceptible zoning map.

Figure 5: Interferogram landslide deformation map.

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Where ni and Ni are the number of landslides occurrences and the number of pixels respectively in the i sensitivity class [33].

Among 11 GCP that showed landslide occurrences, 10(n) points coincide on very high and high susceptibility zones(N=805,993 pixel numbers),and 1 point overlapped on low susceptibility (=311652 number of pixels). Based on R index formula, validation value gives 79.45% [((10/805,993)/((10/805,993) +(1/311,652))] *100 [34-37].

In addition, these points also overlapped on Interferogram which depicted landslide occurrences in the survey area. It is believed that landslide susceptibility could be mapped using InSAR and GIS techniques, but for better detection of terrain movements caused by these phenomena and characterizing affected areas to reduce their impact, InSAR is thought to be more powerful than GIS. Even though GIS technique is not very powerful to quantify the amount of landslide displacement, it can also be alternatively utilized for landslide zonation. That is why a number of scholars strongly suggest that slide susceptibility mapping at GIS environment can be validated by InSAR methods.

As described by a number of scholars [38-41] landslide in Debresina area were very large and affected human activity starting from 13 September 2005. In addition, it is believed that landslides of different types normally affected slopes of the Ethiopian Highlands including Debresina area, which sometimes lead to eviction of residents, destruction to housing, infrastructure and

agricultural land and even death of human lives [42-44]. Thus, research results of this work clearly shows that landslide in the project area has caused tremendous damages in living houses, roads, arable lands, and other infrastructures and proves that it is in a good agreement with previous works in Debresina and surrounding areas.

CONCLUSIONLandslide susceptibility mapping provides fundamental information for hazard assessment and monitoring strategies. In mountainous area like Deberesina, direct ground based landslide triggering factor mapping and evaluation is expensive and virtually impossible within a short period of time. To resolve such limitations, InSAR and GIS techniques provide powerful alternatives for detecting, identifying and monitoring landslides and related triggering factors.

Six control factors (slope, elevation, soil, NDVI, distance from stream feature and road feature) were used to map landslide zoning. Aster DEM data was used to produce slope, elevation and proximity map from stream and Landsat 8 OLI image was used to generate NDVI map. Eth_trs_roads_osm website and digital soil data from the Minister of Water and Resource (MoWR) were used to get proximity from road and soil map, respectively. Consequently, vector data were converted to raster data of 30 m resolution.

The All causative factors when overlaid produced landslide susceptibility zone by weighting linear combination techniques at GIS environment. Nonetheless, the weight of each factors were given according to literatures. Each factor were classified in to 9 class by natural breaks (Jenks) classification method. Among the classified classes, highly slopes and elevations, red color soils which show high erosion, less vegetation (bare lands), very close proximity to the road and stream were the most susceptible to landslide. The final landslide susceptibility map clearly depicts 4 classes of low, moderate, high and very high, landslide vulnerable areas. Since Debresina area is characterized by steep slope and high elevation, approximately 41% of the survey area is strongly affected with landslide geohazard.

PS-InSAR (advanced D–InSAR) is also the most important techniques for landslide displacement mapping in this project. SARPROZ software (which is installed by permission) is utilized for analyses of SAR images. As a result, -9 mm /yr. to 27 mm/yr. clearly showing significant displacement was observed.

The resulting landslide map was validated using R index method which is simple, and this was prepared using GPS points as inventory data. Unfortunately most of selected sample points were put on places which are very high or highly prone to landslide. In most cases, researchers argue that InSAR and GIS could be used for landslide mapping together or separately. From this work, we strongly suggest that for real/quantifying mapping, InSAR method is more robust whereas for simple susceptibility zoning, GIS is dependable technique.

Figure 6: Coherence map of the survey area.

Figure 7: Validation of InSAR and GIS results by GCP points.

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ACKNOWLEDGEMENT We greatly thank to Ethiopian Space Science and Technology Institute for financing this work and Deresina area municipality offices for their helping during reconnaissance and taking sample GCP points.

DECLARATION OF INTEREST STATEMENTNo conflict between authors.

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