1. ii
DELINEATION OF LANDSLIDE AREA
USING SAR
INTERFEROMETRY: A CASE STUDY OF
SINDHUPALCHOWK DISTRICT
Prepared by:
Sudin Adhikari
Siddhant Chaudhary
Sujan Ghimire
Aroj K.C
Utsav Regmi

2. iii
ABSTRACT
Surface displacement is critical component in understanding geohazards and implementing effective
mitigation strategies. This study focuses on the utilization of Synthetic Aperture Radar (SAR) imagery
and Differential Interferometric SAR (D-InSAR) techniques to delineate landslide area and analyze
surface displacement in the Sindhupalchowk district of Nepal.
The Sindhupalchowk district is known for its complex geological and topographical characteristics,
making it highly susceptible to surface displacements and landslides. The study utilized SAR data from
satellite missions, particularly Sentinel-1, to perform D-InSAR analysis. D-InSAR involves the
comparison of SAR images acquired from different satellite passes to measure ground deformation
accurately.
The study found that the surface displacement between 2016 and 2020 is in the range of -0.16m to
0.10m and between 2020 and 2022 is in the range of -0.18m to 0.15m. It revealed significant
movements in various parts of the district. Both gradual and sudden ground movements were observed,
indicating potential surface displacement activity. The D-InSAR technique provided precise
measurements of surface displacement, allowing for the delineation of landslide areas.
The results of this study contribute to a comprehensive understanding of surface displacement dynamics
in the district. The integration of D-InSAR and SAR imagery analysis enables the identification of
high-risk areas prone to hazards. This information is crucial for local authorities and disaster
management agencies in developing effective early warning systems and implementing appropriate
mitigation measures.
The findings of this study provide valuable insights into surface displacement in the Sindhupalchowk
district using SAR imagery and D-InSAR techniques. The combination of these advanced remote
sensing tools offers a powerful approach for monitoring geohazards and mitigating risks. The outcomes
of this research can aid in land-use planning, infrastructure development, and disaster risk reduction
strategies, ultimately contributing to the safety and well-being of the local population.

3. 1
1 Introduction
1.1 Background
Landslides are abrupt hazards involving slope failures, debris flow and rock movement due to
soil erosion and cause a significant devastation in resources and human life (Kalaranjini &
Ramakrishnan, 2020). Globally, it is a disaster causing deaths and injuries of thousands of
lives and destruction of billions of properties (Petley, 2012). Landslides in tropical countries
where heavy monsoon rains are common form a risk to people, infrastructure and properties.
Surface displacement refers to the movement of the Earth's surface, either vertically or
horizontally, due to natural or human-induced factors (Tomás et al., 2014). It can lead to a
wide range of hazards such as landslides, earthquakes, and subsidence, which can cause
significant damage to infrastructure and property, as well as threaten human lives (Papathoma-
Köhle, Kappes, Keiler, & Glade, 2011).
Surface displacement analysis plays a crucial role in understanding the behavior of geohazards
such as landslides and ground subsidence (Tomás & Li, 2017). It provides valuable insights
into the dynamics and magnitude of these phenomena, aiding in the development of effective
mitigation strategies and hazard assessment. Traditional ground-based monitoring methods
have limitations in terms of spatial coverage, accessibility, and cost, particularly in rugged and
remote terrains (Tarchi et al., 2003). To overcome these challenges, remote sensing techniques
have emerged as powerful tools for assessing surface displacement on a larger scale.
Synthetic Aperture Radar (SAR), which is an active microwave sensor, is particularly useful
for areas with cloud cover or heavy rain (Mondini et al., 2021). By comparing two SAR
images taken at different times and with prior knowledge of the topography, it is possible to
detect small changes in the ground's position with millimeter accuracy. This technique is
known as Differential Interferometry SAR (D-InSAR) and is commonly used for monitoring

4. 2
large-scale ground deformations, including landslides, earthquakes, volcanic activity, and
mining operations (Liao, Tang, Wang, Balz, & Zhang, 2012).
Synthetic Aperture Radar (SAR) is a system capable of obtaining complex, high-resolution
images from large areas of terrain, usually located on board an orbital or aerial platform, but
which can also be used in ground-based deployments (Yerro, Corominas, Monells, &
Mallorquí, 2014). D-InSAR, which uses the phase information of 2 SAR images at different
times in the same area by satellite-loaded radar sensors and the DEM of this area to monitor
surface deformation, is Differential Interferometry Synthetic Aperture Radar monitoring
technology (Delacourt et al., 2007).
SAR has several advantages over other remote sensing technologies, such as optical sensors.
It can operate in all weather conditions, day or night, and it is not affected by cloud cover (Jha,
Levy, & Gao, 2008). It can also penetrate through vegetation and detect features beneath the
surface, such as the height of the forest canopy or the moisture content of soil. SAR can provide
high-resolution images with pixel sizes ranging from a few meters down to less than a meter,
depending on the operating frequency and the size of the antenna (Brenner & Roessing, 2008).
1.2 Problem Statement
The Sindhupalchowk district in Nepal is known for its complex geological and topographical
features, which make it susceptible to frequent landslides and surface displacements.
Accurately analyzing surface displacement is essential for understanding the behavior of these
geohazards and implementing effective measures to mitigate their impact. However,
conventional ground-based monitoring methods face limitations in terms of spatial coverage
and feasibility in rugged terrains. Consequently, there is a requirement to employ advanced
remote sensing techniques, specifically D-InSAR, to comprehensively assess surface
displacement and delineate landslide area in the Sindhupalchowk district.

5. 3
1.3 Objectives
The primary objective of our study is:
To delineate landslide area of Sindhupalchowk District from 2016 to 2020 and 2020 to
2022.
The secondary objectives of our study are:
To estimate the surface displacement of Sindhupalchowk District from 2016 to 2020
and 2020 to 2022.
To process SAR data using interferometry technique and create phase difference and
coherence map
1.4 Scope
The study aims to estimate the surface displacement and delineate landslide area of
Sindhupalchowk district. The study tends to overcome the demerits of traditional disaster
identification method as traditional method are time consuming, costlier and has limited
coverage. This study is based on data obtained by Remote Sensing photography, thus field
visits and primary data gathering are not within the scope of this project. The image processing
and analysis are done using SNAP software.

6. 4
2 Literature Review
2.1 D-InSAR for deformation detection
InSAR is a useful tool for accurately mapping earth surface movement (Rott, 2009). D-InSAR
has become increasingly popular for monitoring ground surface displacements caused by
natural disasters like earthquakes, landslides, mining activities, and avalanches. In the past,
displacement estimation was done through time-consuming and hazardous field measurements,
which collected data only from a few point locations. However, regional-level information is
necessary for planning, management, and monitoring after a natural disaster. In recent years,
the use of D-InSAR has gained momentum due to its ability to estimate surface displacements
with millimeter-level accuracy. To generate informative interferograms, SAR data pairs with
good coherence are necessary. Displacement maps generated through InSAR can be analyzed
with the help of high-resolution optical satellite imagery (Farina, Colombo, Fumagalli, Marks,
& Moretti, 2006). The displacement maps produced using this technique provide valuable
spatial information, which can assist in efficient and cost-effective planning and developmental
work in the affected regions. This paper aims to provide an overview of the use of D-InSAR
technology for studying surface displacements.
D-InSAR can give relative measures of movement in the vertical direction of the order of few
centimeters or even less (Frattini, Crosta, Rossini, & Allievi, 2018).
Due to side looking geometry of radar, three or more SAR images are sufficient to generate
differential interferogram or any of the interferogram can be simulated via registering a DEM
(e.g. SRTM DEM) with respect to the to the geometry of the SAR image pair. Hence, quality
of DEM influences the result(Liao, Jiang, Wang, Wang, & Zhang, 2013).

7. 5
The major steps in D-InSAR data processing are as follows:
1. Input of master and slave images,
2. Subtraction of the Flat-Earth effect and phase unwrapping.
A study conducted in the Italian Alps used D-InSAR to detect landslides that occurred in a
mountainous region (Tessari, Floris, & Pasquali, 2017). The study showed that D-InSAR was
able to detect the landslides and monitor their movement over time, providing valuable
information for landslide risk assessment and mitigation.
2.2 Basics of SAR Interferometry
The range direction information in synthetic aperture radar only conveys information on the
distance to the sensor at a certain azimuth time. The distance measurement follows from the
time observations of the pulse returns by the local oscillator, resulting in a relative accuracy of
half the posting in range direction, typically 4 to 5 meters. As the phase information of a
resolution cell has a uniform probability density function (Maître, 2013).
The random variable φ(x) at location χ = (x, y) in an image has an expectation value
𝐸{𝐸(𝐸)} = 𝐸(𝐸) = ∫ 𝐸𝐸𝐸𝐸(𝐸)𝐸𝐸
𝐸
−𝐸
…………………………… Equation 1.1
Where, pdf (φ) is the probability density function of φ(x). The expectation value E {φ (x)}
can be a function of the location x. So it does not contain any useful information.

8. 6
Figure 1: A and B shows that distance measurements are not capable of distinguishing two points P' and P at
the same slant range, but displaced horizontally in ground range(Maître, 2013)
Although the backscatter intensity can give an indication for the presence of topography, this
information cannot be made quantitative to a high accuracy. It is evident that this problem
could be solved by the observation of angular differences between a point P' at a reference body
and a point P at a height HP above this reference body, with the same range R1 to the sensor.
This is equivalent with measuring cumulative angular differences between neighboring
resolution cells. In fact, this is what SAR interferometry provides by observing both points
from a slightly different geometry, as shown in Figure 1 B. The effective distance between the
two sensors, measured perpendicular to the look direction, is referred to as the perpendicular
or effective baseline B𝐸. Because the instrument is not capable of directly measuring the small
angular differences, this information needs to be derived from the distance measurements
between both sensors and the resolution cell on earth, applying some simple trigonometry as
indicated in Figure 1 A and Figure 1 B. Thus, the basic problem of SAR interferometry is the
determination of these distance differences. The required accuracy for measuring the distance
differences between sensors and resolution cell is in the mm-range. As the ranging information
determined by the range resolution is three orders of magnitude worse, it is not applicable for
this type of accurate observations. The phase observations of the received echo provide the
solution for this problem. Disregarding atmospheric propagation delay for the moment, the

9. 7
phase observation for a single resolution cell can be regarded as the sum of two contributions:
the phase proportional to the distance and the phase due to the scattering characteristics of the
resolution cell. Although the scattering phase component is unpredictable, it is a deterministic
quantity, i.e., if the phase measurement would be repeated under exactly the same conditions,
it would yield the same result. Under these circumstances we state that the imaging is coherent.
The degree of coherence is a direct measure for the similarity between the two observations.
As a consequence, the phase difference between two sensors for a coherent system is only
dependent on the difference in range, as the scattering phase contributions cancel.
This method imposes high demands on the geometric configuration parameterized by the
spatial baseline and the interferometric processing of the SAR data. Moreover, for repeat pass
interferometry the temporal separation of the two acquisitions referred to as the temporal
baseline. It can result in changing scattering characteristics due to weathering, vegetation, or
anthropogenic activity.
2.3 Interferometric SAR Data Structure
Interferometry data are supplied in complex formats for example single look complex (SLC
format) to facilitate the extraction of phase as well as amplitude information (Hanssen, 2001).
Each pixel in SLC format can be mathematically represented as a+ib.
Amplitude= (a2
+b2
)1/2
, phase=tan-1
(b/a).
2.4 Radar bands and designation
Microwave radiation can be classified into seven bands. Here is a table illustrating these seven
bands with their corresponding wavelength.

10. 8
Table 1: Microwave bands utilized in Radar with their wavelength (Semwal, 2013)
Band Wavelength (cm)
Ka 0.75-1.10
K 1.10-1.67
Ku 1.67-2.40
X 2.40-3.75
C 3.75-7.50
L 7.50-15.0
P 15-30
2.5 Expression for Differential Interferometry
Differential radar interferometry is a useful method for measuring surface changes with a high
degree of accuracy. It can provide relative measurements accurate up to a few centimeters or
even less. The interferometric phase and its derivative can be derived through a simplified
process (Zebker et al., 1994). Differential radar Interferometry is based on a multiple
observation Interferometry.

11. 9
Figure 2: Three observation of a scene from three locations separated by baselines a, b and c (Semwal, 2013)
The geometry of three observations of a scene is represented here in figure 2, the phase
difference of a given resolution element from the first two observations is
𝐸φ12= (4π/λ) a Cos (θ)
Now by using parallel ray approximation (Zebker and Gabrial, 1986), if aII , bII are the parallel
components of baseline then the ratio of the two phases is equal to the ratio of the components
of baselines parallel to the look direction
𝐸 φ 12= (4π/λ) aII
𝐸 φ 13= (4π/λ) bII
There ratio is 𝐸 φ 12 / 𝐸 φ 13 = aII / bII ………………………Equation 1.2
Consider that the ground has changed for the third observation. Hence for second
interferogram, in addition to phase shift dependence of topography, there is an additional phase
change due to radar line of sight component of displacement 𝐸r (Mirbagheri et al., 2001)
Therefore,
𝐸 φ 13 = (4π/λ) (bII+𝐸r) ………………………………………Equation 1.3

12. 10
On removing the effect of topography by subtraction of two interferograms, hence the solution
depends only on 𝐸r. Hence, we can neglect bII. Using Equation 1.2 and 1.3. we get, 𝐸 φ 13
= - (aII / bII) 𝐸 φ 12 = (4π/λ) 𝐸r
Hence,
𝐸 φ 13 = (4π/λ) 𝐸r ……………………………………………. Equation 1.4.
Hence, a change in the height of a feature over the terrain can be calculated more accurately
from an interferogram generated from SAR data pair with low base line.
2.6 Terms used in SAR processing
1. TOPSAR-split
TOPSAR Split is a technique used to split a TOPS (Terrain Observation with Progressive
Scans) mode SAR image into smaller sub-swaths (Mastro et al., 2020). This is done to
facilitate the processing of the large TOPSAR image, as processing the entire image in one go
can be computationally intensive and time-consuming.
The TOPSAR Split technique involves dividing the TOPSAR image into smaller sub-swaths
along the azimuth direction. The sub-swaths are typically overlapped to ensure continuity and
to reduce the effect of edge artifacts. The sub-swaths can then be processed independently or
in parallel to speed up the processing.
2. Apply Orbit File
In SAR image processing, applying an orbit file is an important step to georeferenced the SAR
data and correct for the effects of satellite motion (Tzouvaras et al., 2019). The orbit file
contains information about the satellite's orbit and position at different times, which is used to
calculate the position of the SAR sensor at the time of data acquisition.

13. 11
3. Back Geocoding
Back geocoding is a process used to convert SAR data from the slant range domain (i.e., the
range direction) to the ground range domain. This process is also known as image focusing
or image formation.
Back geocoding involves two main steps: range compression and azimuth compression (Prats
et al., 2005). Range compression is used to compress the radar pulses received from the SAR
sensor in the range direction, while azimuth compression is used to focus the radar pulses in
the azimuth direction.
4. Co-registration
Co-registration is a process used to align two or more SAR images so that they can be compared
or combined (Kelany et al., 2020). Co-registration is necessary because SAR images acquired
at different times or with different sensors may have different geometrical distortions and
misalignments due to variations in platform motion, imaging geometry, and terrain topography.
Co-registration involves finding the transformation parameters that relate the pixel coordinates
of one SAR image to the pixel coordinates of another SAR image (Ye et al., 2021). The
transformation parameters include translation, rotation, scaling, and skewing, and are typically
determined by identifying common features or tie points in the two images and optimizing the
transformation parameters to minimize the differences between the tie points.
5. Topsar debrust
Topsar deburst is a technique used to remove burst noise in TOPS mode SAR data. The burst
noise is caused by the switching of sub apertures during the data acquisition process, which
can lead to streaks or diagonal lines in the SAR image (Kumar & Kumar, 2020). The Topsar
deburst technique involves filtering the SAR data to remove the burst noise in the range
direction, which is perpendicular to the flight path. This technique is important for improving

14. 12
the quality of the SAR image and reducing the noise that can interfere with further analysis,
such as change detection or feature extraction.
6. Topo phase removal
Topo phase removal is an important technique in SAR image processing that is used to correct
for the phase shift caused by topographic variations in the imaged scene (Zebker et al., 1997).
The technique involves estimating the phase shift caused by the topography and removing it
from the SAR data to obtain an accurate representation of the imaged scene.
Topographic variations in the scene, such as mountains, hills, and valleys, can cause changes
in the distance between the SAR sensor and the ground, resulting in a phase shift in the SAR
data. This phase shift can interfere with the interpretation of the SAR data and needs to be
removed to obtain an accurate representation of the imaged scene.
7. Multilooking
Multilooking is a technique used in SAR image processing to improve the signal-to-noise ratio
(SNR) and reduce speckle in the image. Speckle is a type of noise that can obscure details in
SAR images and make interpretation difficult (Li & Goldstein, 1990).
Multilooking works by averaging multiple adjacent pixels in the SAR image, which results in
a smoother image with less noise. The number of adjacent pixels that are averaged together is
called the "looks". The more looks that are used, the greater the reduction in speckle and the
higher the SNR. However, increasing the number of looks also results in a loss of resolution.
8. Goldstein filtering
Goldstein filtering is a technique used in SAR image processing to remove phase noise and
improve the coherence of SAR images for interferometric applications (Chen & Xu, 2014).
Phase noise is a common problem in SAR images that can be caused by a variety of factors,
such as atmospheric disturbances, temporal decorrelation, and speckle.

15. 13
The Goldstein filtering algorithm works by estimating the local phase gradient of the SAR
image and using it to remove the phase noise. The algorithm first applies a 2D discrete Fourier
transform (DFT) to the complex SAR image to obtain its frequency spectrum. Then, the
gradient of the phase is calculated in the frequency domain by applying a spatial filter to the
frequency spectrum. The filtered frequency spectrum is then inverse transformed back into the
spatial domain to obtain the filtered complex SAR image.
The effectiveness of the Goldstein filtering algorithm depends on several factors, such as the
level of phase noise in the SAR image, the size of the spatial filter used in the algorithm, and
the type of interferometric application being performed. The algorithm is often used in
combination with other SAR image processing techniques, such as multilooking, to improve
the quality and accuracy of SAR images for various applications.
9. Phase unwrapping
Phase unwrapping is a crucial step in SAR image processing, especially InSAR data analysis
(Pu et al., 2021). It involves removing the phase ambiguities or "wraps" that can occur in the
phase image and obtaining a continuous and unambiguous phase field. There are various phase
unwrapping algorithms available, including the minimum cost flow (MCF) algorithm, the
branch cut method, and the region growing method, each with their own advantages and
limitations.
The MCF algorithm is a popular phase unwrapping technique that works by formulating the
phase unwrapping problem as a network flow optimization problem (Pepe et al., 2011). The
algorithm minimizes the cost of flowing a virtual fluid over the surface represented by the
phase image, subject to the constraint that the flow must be divergence-free. The branch cut
method and the region growing method are other common phase unwrapping techniques that
are based on local and regional constraints, respectively.
10. Coherence

16. 14
It is the measure of correlation or similarity between two or more radar signals acquired at
different times over the same area (Wei & Sandwell, 2010). It is a fundamental parameter
used to analyze the phase information in interferometric SAR data.
The coherence value represents the statistical correlation between the two SAR images, and it
ranges from 0 to 1. A coherence value of 1 indicates a perfect correlation between the two
images, meaning that the phase information is highly consistent and reliable. On the other
hand, a coherence value close to 0 indicates a poor correlation, implying that the phase
information is highly noisy or decorrelated.
High coherence values are desirable in D-InSAR because they indicate a stable and reliable
phase measurement, allowing for accurate deformation analysis (Blanco-Sànchez et al., 2008).
Areas with high coherence values are typically associated with coherent scatterers, such as
buildings, roads, or persistent natural features like rocks or trees.
Conversely, low coherence values are problematic as they can introduce errors and
uncertainties in the phase estimation (Niraj et al., 2022). Low coherence areas are usually
caused by decorrelation, which can be due to temporal changes in the scene (e.g., vegetation
growth or land cover changes) or geometric factors (e.g., layover or shadow effects).
2.7 Factors contributing Surface Displacement
Surface displacement in D-InSAR is caused by various factors, both natural and anthropogenic
(Pepe & Calò, 2017). These factors can result in changes in the phase of the radar signal, which
is then used to infer surface displacement.
Tectonic activity, including plate movements and faulting, can result in surface
displacement. Earthquakes, for instance, generate sudden and significant displacements
along faults, leading to observable changes in radar phase in D-InSAR data.
Volcanic deformation occurs due to the movement of magma beneath the Earth's
surface. Inflating or deflating magma chambers cause uplift or subsidence, respectively,

17. 15
resulting in measurable surface displacement. D-InSAR can capture these movements
and provide valuable information about volcanic processes.
Excessive groundwater extraction from underground aquifers can lead to subsidence.
When water is pumped out, the pore spaces in the soil or rock get compressed, causing
the ground surface to sink. D-InSAR is capable of detecting and quantifying such
subsidence, enabling the monitoring of groundwater extraction impacts (Caló et al.,
2017).
Mining and extraction activities can induce surface displacement. Subsurface mining
can cause subsidence as underground materials are extracted, while open-pit mining
may lead to localized uplift or subsidence, depending on the excavation and filling
processes(Wang et al., 2022).D-InSAR can detect and monitor these movements, aiding
in assessing the effects of mining operations.
Natural subsidence can occur due to various factors, including the compaction of loose
sediments, consolidation of clay-rich soils, or the dissolution of underground caverns
(Waltham, 2009). Anthropogenic subsidence is caused by human activities such as oil,
gas, or mineral extraction, as well as the construction of tunnels or underground
structures. D-InSAR enables the detection and monitoring of both natural and
anthropogenic subsidence processes.
Landslides contribute to significant surface displacement, often associated with slope
instability (Gattinoni et al., 2019). D-InSAR can detect and monitor landslides by
observing changes in surface phase caused by the movement of soil or rock masses.
This information is crucial for landslide hazard assessment and mitigation. In areas with
glaciers or permafrost, D-InSAR can capture surface displacement resulting from
glacial and periglacial processes. Melting, freezing, and thawing of ice can cause
ground uplift, subsidence, or lateral movement, which can be monitored using D-InSAR
techniques.
Various human-induced activities, such as construction projects like buildings,
infrastructure, dams, or reservoirs, can cause surface displacement. D-InSAR can
monitor and assess the impact of these activities on the surrounding environment,
providing valuable information for planning and management purposes.

18. 16
2.8 AUC Curve Generation
The ROC curve is widely used in the overall accuracy evaluation of binary classification in the
LSP modelling due to its satisfactory performance (Vakhshoori et al., 2019). ROC curve is
threshold independent curve. Its main advantage is that it is independent of the number and
spacing of thresholds used for calculation (Fawcett, 2006). Assuming n classes of the landslide
susceptibility indexes, n+1 thresholds can be defined, where the first threshold value (i = 1)
is lower than the minimum susceptibility index observed in the most stable category, and the
last threshold value (i = n+1) is higher than the maximum susceptibility index in the most
sensitive category. The Area Under Curve (AUC) is presented to measure the difference in
performance between methods used. AUC is a measure widely used for prediction models in
natural hazard assessment. The value of AUC varies from 0.5 to 1. A model with an AUC
value Higher than 0.5 is considered acceptable. A model with AUC in the range of 1.0 to 0.9
is considered extraordinary, 0.9 to 0.8 very good, 0.8-0.7 acceptable. Each threshold forms a
confusion matrix in which four types of pixels are defined: true positive (TP), false positive
(FP), true negative (TN), and false negative (FN) pixels. According to the number of pixels
in each threshold, two statistics can be calculated, namely TPR (true positive rate) and FPR
(false positive rate), as shown as:
TPR = TP/ TP + FN
FPR =FP /TN + FP
TPR and FPR are plotted on the Y-axis and X-axis of the ROC curve, respectively. Then the
AUC is calculated, the success rate of the model can be displayed through the participation of
AUC values in the training data set, meanwhile, the prediction rate of the test data set can be
displayed.

19. 17
3 Methodology
3.1 Study Area
The study area is located in Sindhupalchowk district, which is situated about 70 kilometers
northeast of the capital city of Kathmandu in Nepal. The climate in this area is subtropical,
temperate, and alpine, with a temperature range of 28.5 to 4.0 degrees Celsius and an average
annual rainfall of 3604.3 mm, with 80% of the rainfall occurring during the monsoon season.
The study area consists of hilly terrain that ranges from approximately 800 meters to 7000
meters. The geology is fragile, with the dominant types of soil being cambisols and regosols
formation. The tributaries of Koshi River flow through the study area, along with the Araniko
highway that connects Kathmandu with Kodari, Nepal's border with the People's Republic of
China.
However, after the earthquake in 2015, along with many aftershocks that were centered in the
district, the hills were shaken, leaving them with many landslides. As a result, the area has
fragile and ruptured landforms, which could lead to more surface displacement in the future.

20. 18
Figure 3: Study area map.
3.2 Software used
SNAP
The SNAP (Sentinel Application Platform) is a software platform developed by the European
Space Agency (ESA) for processing and analyzing data from the Sentinel series of Earth
observation satellites. The platform provides a range of tools and algorithms for SAR image
processing, including D-InSAR for surface displacement and landslide detection.
The SAR data were imported into the SNAP platform and the appropriate processing tools was
selected. An interferogram from the SAR images was generated, phase was unwrapped to
obtain a continuous and unambiguous phase field, and displacement map to detect any surface
deformation or landslides was created.

21. 19
ArcGIS
ArcGIS was used for mapping and AUC curve generation.
3.3 Workflow
The SLC Sentinel 1A imageries were acquired for the year 2016, 2020 and 2022. The images
were processed in SNAP. 2016 and 2020 images were master image for estimating surface
displacement between 2016 and 2020, and 2020 and 2022 respectively. The master and slave
images were coregistered to generate differential interferrogram. Differential interferrograms
were further processed to remove errors. And finally, phase unwrapping was done to get
surface displacement of study area.

22. 20
Figure 4: Methodological Workflow

23. 21
3.3.1 Data Acquisition
Access to Sentinel 1 data is obtained from the website https://search.asf.alaska.edu/. It should
be noted that the use of this data is particularly relevant in the context of Sentinel 1.
Table 2: Data Specification
Specification Type
Product type L1 Single Look Complex
Polarization VV + VH
Sensor Mode IW
Pass direction Descending
Path 121
Frame 499
Year 2016-10-15
2020-10-30
2022-02-22
Master and Slave Image
The master image is the reference image, which is acquired first and used as the baseline for
the InSAR processing. The slave image is acquired at a later time and is compared to the
master image to detect any changes in the Earth's surface.
The two images were acquired with similar imaging parameters, such as the frequency,
polarization, and incidence angle, to ensure that they are compatible for the interferometric
processing. The images were typically acquired from the same satellite sensor or from two
sensors with similar imaging parameters.

24. 22
The master image for our project was of year 2016 and slave image was of year 2020 for surface
displacement estimation between 2016 and 2020. Similarly, for surface displacement
estimation between 2020 and 2022, image of year 2020 was master image and image of year
2022 was slave image.
3.3.2 Co registration
The co registration process typically involved the following steps:(Bouchra et al., 2020)
TOPSAR Split
In this step, the TOPSAR (Terrain Observation with Progressive Scans) image was split into
sub-swaths (IW1, IW2 & IW3), which were smaller, manageable image subsets. This was
necessary because TOPSAR images were very large and can cause processing difficulties. IW3
sub-swath was used for our study.
Apply Orbit File
In this step, precise orbit information was applied to the TOPSAR images to correct for errors
caused by the movement of the SAR platform.
Back Geocoding
In this step, the TOPSAR images were transformed into ground range geometry and geocoded
to a specific coordinate system, which allows for comparison with other SAR images acquired
at different times.
Co registration
In this step, the slave image was aligned with the master image by matching the common
features between the two images. The matching was done using phase correlation technique.

25. 23
3.3.3 Differential Interferogram
A differential interferogram was generated by subtracting the phase of the master image from
the phase of the slave image, which had been previously coregistered and processed. The
resulting image showed the phase difference or "interference" between the two images, which
was proportional to the changes in the distance traveled by the radar signal between the two
acquisitions.
The following filters were used during the interferogram generation. They were:
TOPO Deburst
It was used to remove burst errors in the TOPSAR (Terrain Observation by Progressive Scans)
data. The final image of high quality and free of burst errors caused by malfunctioning radar
modules was ensured. VV polarization war used during TOPO Deburst.
TOPO Phase removal
TOPO phase removal included the process of removing the topographic phase component from
the interferogram, which was caused by the topography variations in the imaged scene. The
topographic phase component can mask or distort the signal caused by deformation or
displacement on the ground, so it was necessary to remove this component before analyzing
the deformation signal. It was typically performed by using a digital elevation model (DEM)
to estimate the phase shift caused by topographic variations in the scene and subtracting it from
the interferogram. Different parameters were used. They were:
DEM: SRTM 1 sec HGT
Orbit: 3
Tile Extension (%): 100

26. 24
Multilook
The process involved dividing the original complex SAR data into smaller windows, and
averaging the data within each window to create a new image with reduced noise. The spatial
resolution was decreased. The output of the tool was a new multilooked image with reduced
speckle noise. Different processing parameters were used. They were:
Number of Range Looks: 15
Number of Azimuth Looks: 3
Goldstein Phase Filter
Goldstein filter was used that works by comparing the phase differences at each point in the
interferogram to the average phase difference in a local window around that point. If the
difference was greater than 0.2, the point was considered to be noisy and was filtered out. The
filter was applied iteratively until the remaining phase differences were within given threshold.
The tool takes as input the interferogram and a coherence threshold, which determines the
minimum coherence required for a point to be included in the filtering process. The output
was a filtered interferogram with improved quality and reduced noise, which can be used for
further analysis and interpretation.
3.3.4 Phase Unwrapping
After applying the Goldstein filter in SNAP, the output was a "wrapped" phase image with
reduced noise and phase discontinuities. The next step was to perform phase unwrapping using
SNAPHU plugin to obtain an unwrapped phase image, which is necessary for accurate surface
displacement and landslide detection. The unwrapping process was done using the minimum
cost flow (MCF) algorithm.

27. 25
Once the phase unwrapping was completed, the unwrapped phase image can be used to
generate a displacement map or a coherence map, which can then be analyzed to detect surface
displacement and landslides.
3.3.5 Phase to displacement
After phase unwrapping in SNAP, the phase values were converted to displacement values by
multiplying with the wavelength and dividing by 4*pi. This conversion gave the actual
displacement in the line-of-sight direction between the two acquisitions of the SAR images.
The output of phase to displacement conversion was a geocoded displacement map that shows
the surface deformation in the study area. The displacement values were typically in units of
meters and represent the amount of movement in the line of sight direction of the radar.

28. 26
3.3.6 Validation
Figure 5: AUC curve of surface displacement between 2016 and 2020
In Figure 5, The AUC value of 0.731 indicates that this study has moderately discriminated
between landslide areas experiencing higher negative surface displacement and landslide areas
without such displacement. The 73.1% value (percentage equivalence of 0.731) implies that
the model's predictions of landslide areas experiencing higher negative surface displacement
align with the true negative instances around 73.1% of the time. It indicates a moderate level
of accuracy in classifying the absence of surface displacement. The true positive instances
were taken from Google Earth. The points experiencing landslides that occurred between 2016
and 2020 were taken as true positive instances.

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Figure 6: AUC curve of surface displacement between 2020 and 2022
In Figure 6, The AUC value of 0.745 indicates that this study has moderately discriminated
between landslide areas experiencing higher negative surface displacement and landslide areas
without such displacement. The 74.5% value (percentage equivalence of 0.745) implies that
the model's predictions of landslide areas experiencing higher negative surface displacement
align with the true negative instances around 74.5% of the time. It indicates a moderate level
of accuracy in classifying the absence of surface displacement. The true positive instances
were taken from Google Earth. The points experiencing landslides that occurred between 2020
and 2022 were taken as true positive instances.

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4 Results and Discussion
The output of the study is presented and discussed in this section. The surface displacement
map, phase difference map and coherence map between 2016 and 2020, and 2020 and 2022
are prepared and discussed in this section. The overlay map of the landslides is shown and
discussed in this section.
4.1 Delineation of landslide
Figure 7: Landslide between year 2016 and 2020
In Figure 7, one of the landslide that occurred between 2016 and 2020 is shown by a polygon.

31. 29
Figure 8: Overlay with landslide (2016 to 2020)
In figure 8, the displacement data was overlaid onto the Google Earth interface. Here red color
shows maximum subsidence with -0.16 m and on the other hand blue color shows maximum
uplifting with 0.10 m. From the figure, it is seen that the landslide is associated with the higher
negative displacement value. So the surface displacement data helps in delineating the
landslide in the study area.

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Figure 9: Landslide between year 2016 and 2020
In Figure 9, one of the landslide that occurred between 2016 and 2020 is shown by a polygon.

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Figure 10: Overlay with landslide (2016 to 2020)
In figure 10, the displacement data was overlaid onto the Google Earth interface. Here red
color shows maximum subsidence with -0.16 m and on the other hand blue color shows
maximum uplifting with 0.10 m. From the figure, it is seen that the landslide is associated with
the higher negative displacement value. So the surface displacement data helps in delineating
the landslide in the study area.
Similarly, overlaying for other 15 landslides that occurred between 2016 and 2020 were done.
Out of those 15 landslide, 11 of them were associated with higher negative displacement value.
Also, with this reference, AUC curve was generated which validate the presence of landslide
associated with higher negative surface displacement value.

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Figure 11: Landslide between year 2020 and 2022
In Figure 11, one of the landslide that occurred between 2020 and 2022 is shown by a polygon.

35. 33
Figure 12: Overlay with landslide (2020 to 2022)
In figure 12, the displacement data was overlaid onto the Google Earth interface. Here red
color shows maximum subsidence with -0.10 m and on the other hand blue color shows
maximum uplifting with 0.18 m. From the figure, it is seen that the landslide is associated with
the higher negative displacement value. So the surface displacement data helps in delineating
the landslide in the study area.

36. 34
Figure 13: Landslide between year 2020 and 2022
In Figure 13, one of the landslide that occurred between 2020 and 2022 is shown by a polygon.

37. 35
Figure 14: Overlay with landslide (2020 to 2022)
In figure 14, the displacement data was overlaid onto the Google Earth interface. Here red
color shows maximum subsidence with -0.10 m and on the other hand blue color shows
maximum uplifting with 0.18 m. From the figure, it is seen that the landslide is associated with
the higher negative displacement value. So the surface displacement data helps in delineating
the landslide in the study area.
Similarly, overlaying for other 12 landslides that occurred between 2020 and 2022 were done.
Out of those 12 landslide, 9 of them were associated with higher negative displacement value.
Also, with this reference, AUC curve was generated which validate the presence of landslide
associated with higher negative surface displacement value.

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4.2 Surface displacement map
Figure 15: Surface displacement map showing displacement in surface between 2016 and 2020.
From Figure 15, it shows surface displacement with a value range from -0.16m to 0.10m, which
is shown using a color scale to represent different magnitudes of displacement.
On a surface displacement map, areas with green color represent the regions experiencing the
maximum subsidence of -0.16m. As we move towards the yellow shades, the displacement
decreases gradually. On the other side of the color scale, red shades represent areas with uplift
or positive displacement, and dark red areas indicate the maximum uplift of +0.10 m.
The color scale provides a visual representation of the magnitude and direction of surface
displacement, allowing us to observe the spatial distribution of subsidence and uplift in the
studied area.

39. 37
The surface displacement shown in Figure 15, is the combined effect of earthquake related
tectonic activity, landslide and natural subsidence. The negative value indicates that the
surface is displaced downward with respect to master image and the positive value indicates
that the surface is displaced upward with respect to master image. The major cause of surface
displacement in this time frame is due to the tectonic activities caused by aftershocks of 2015
earthquake as Sindhupalchowk is one of the adversely affected region. The surface seems to
negatively displaced from the west and positively displaced towards east. The negative surface
displacement is both due to tectonic activity and landslide. The positive surface displacement
is mainly due to tectonic activity caused by aftershock. Research conducted by (Kobayashi et
al., 2015) titled " Detailed crustal deformation and fault rupture of the 2015 Gorkha
earthquake, Nepal, revealed from ScanSAR-based interferograms of ALOS-2” further
supports the observation of uplift and positive surface displacement in various regions of
Nepal, including Sindhupalchowk district.
The surface displacement map provides insight on the unstable surface which can be useful for
urban planning and infrastructure development, disaster management and, community
resilience and vulnerability.

40. 38
Figure 16: Surface displacement map showing displacement of surface between 2020 and 2022.
From Figure 16, it shows surface displacement with a value range from -0.18m to 0.15m, we
can use a color scale to represent different magnitudes of displacement.
On a surface displacement map, areas with green color represent the regions experiencing the
maximum subsidence of -0.18m. As we move towards the yellow shades, the displacement
decreases gradually. On the other side of the color scale, red shades represent areas with uplift
or positive displacement, and dark red areas indicate the maximum uplift of +0.15m.
The color scale provides a visual representation of the magnitude and direction of surface
displacement, allowing us to observe the spatial distribution of subsidence and uplift in the
studied area.
The surface displacement shown in Figure 16, is the combined effect of earthquake related
tectonic activity, landslide and natural subsidence. The negative value indicates that the
surface is displaced downward with respect to master image and the positive value indicates

41. 39
that the surface is displaced upward with respect to master image. The surface seems to
positively displaced towards north and negatively in southern part. During this time frame, we
have used the varying season for study. The image of October of 2020 and the image of
February of 2022 is used. Since, February is the time for snowfall, majority of positive surface
displacement is seen in Northern part indicating the mass deposit due to snowfall. The southern
part has experienced the negative surface displacement which is due to landslide and tectonic
movement.
The surface displacement map provides insight on the unstable surface which can be useful for
urban planning and infrastructure development, disaster management and, community
resilience and vulnerability.

42. 40
4.3 Phase difference map
Figure 17: Map showing phase difference between year 2016 and 2020
From Figure 17, it shows phase difference between the year 2016 and 2020 where the value
ranges from +3.13 (+ℼ) to -3.13 (-ℼ).
The blue color represents the maximum value. As we move towards the yellow shades the
value decreases gradually. The minimum value is represented by reddish brown color.

43. 41
Figure 18: Map showing phase difference between year 2020 and 2022
From Figure 18, it shows phase difference between the year 2016 and 2020 where the value
ranges from +3.13 (+ℼ) to -3.13 (-ℼ).
The blue color represents the maximum value. As we move towards the yellow shades the
value decreases gradually. The minimum value is represented by reddish brown color.
The maps in Figure 17 and 18 are phase difference map representing phase shift or phase
difference between two sets of data. Phase difference maps are generated by comparing the
phase of radar signals acquired at different times. The radar waves bounce off the Earth's
surface and return to the sensor, creating interference patterns. By measuring the phase
difference between these patterns, it is possible to determine the displacement of the surface.
The phase unwrapping of these phase displacement map is done to obtain surface displacement.

44. 42
4.4 Coherence map
Figure 19: Map showing coherence between year 2016 and 2020
From Figure 19, it shows coherence between the year 2016 and 2020 where the value ranges
from 0.92 to 0.07. The minimum value is represented by black color. As we move towards
the white shades, the value increases gradually. The maximum value is represented by white
color.

45. 43
Figure 20: Map showing coherence between year 2020 and 2022
From Figure 20, it shows coherence between the year 2020 and 2022 where the value ranges
from 0.87 to 0.07. The minimum value is represented by black color. As we move towards
the white shades, the value increases gradually. The maximum value is represented by white
color.
The maps in Figure 19 and 20 are coherence map representing coherence or correlation between
radar signals acquired at different times over the same area. Areas with high coherence (values
close to 1) indicate that the radar signals are highly correlated, meaning there is good
agreement between the phase measurements. This suggests that the interferometric
measurements are reliable, and the acquired data can be used to accurately estimate surface
displacements. Areas with low coherence (values close to 0 or 0%) indicate that the radar
signals have experienced significant decorrelation or inconsistency between the two
acquisitions.

46. 44

47. 45
5 Limitations
Due to the unavailability of Sentinel 1A dataset of 2018 and 2019 covering our study area, we
were unable to perform surface displacement estimation of equal interval. Unavailability of
high processing computer delayed the study as the processing of large datasets consume high
time. The disintegration of the surface displacement into displacements caused by landslide,
tectonic activity was not possible due to the lack of CORS in the study area.

48. 46
6 Conclusion and Recommendations
The project fulfilled the objective of our project to delineate the landslide area of
Sindhupalchowk district between 2016 and 2020 and 2020 and 2022. The study identifies that
the surface of the study area has been displaced due to the combined effect of landslide,
earthquake related tectonic activity and natural subsidence. The interferogram of the study
period was computed using SNAP and the unwrapping of interferogram was done using
Snaphu.
The study found that the surface displacement between 2016 and 2020 is in the range of -0.16m
to 0.10m and between 2020 and 2022 is in the range of -0.18m to 0.15m. The surface of
Sindhupalchowk is in continuous displacement making it the area prone to displacement related
hazards such as earthquake, landslide etc. The obtained surface displacement map can be
useful for urban planning and infrastructure development, disaster management and,
community resilience and vulnerability.
Following recommendations are made for future works:
Use of high processing computer is recommended which allows to perform more
accurate PSI method for surface displacement estimation.
Detailed study of the project area should be done before selection to ensure enough
number of CORS for validation purpose.
SAR data pairs with less time period and equal interval is recommended to get more
accurate result.

49. 47
7 References
Brenner, A. R., & Roessing, L. (2008). Radar imaging of urban areas by means of very high-
resolution SAR and interferometric SAR. IEEE Transactions on Geoscience and Remote
Sensing, 46(10), 2971-2982.
Chowdhury, R., Bhattacharya, G., & Flentje, P. (2009). Geotechnical slope analysis: Crc
Press.
Delacourt, C., Allemand, P., Berthier, E., Raucoules, D., Casson, B., Grandjean, P., . . .
Varel, E. (2007). Remote-sensing techniques for analysing landslide kinematics: a review.
Bulletin de la Société Géologique de France, 178(2), 89-100.
Farina, P., Colombo, D., Fumagalli, A., Marks, F., & Moretti, S. (2006). Permanent Scatterers
for landslide investigations: outcomes from the ESA-SLAM project. Engineering Geology,
88(3-4), 200-217.
Frattini, P., Crosta, G. B., Rossini, M., & Allievi, J. (2018). Activity and kinematic behaviour
of deep-seated landslides from PS-InSAR displacement rate measurements. Landslides, 15,
1053-1070.
Highland, L., & Bobrowsky, P. T. (2008). The landslide handbook: a guide to understanding
landslides: US Geological Survey Reston, VA, USA.
Jakob, M. (2022). Landslides in a changing climate Landslide Hazards, Risks, and Disasters
(pp. 505-579): Elsevier.

50. 48
Jha, M. N., Levy, J., & Gao, Y. (2008). Advances in remote sensing for oil spill disaster
management: state-of-the-art sensors technology for oil spill surveillance. Sensors, 8(1), 236-
255.
Liao, M., Jiang, H., Wang, Y., Wang, T., & Zhang, L. (2013). Improved topographic mapping
through high-resolution SAR interferometry with atmospheric effect removal. ISPRS journal
of photogrammetry and remote sensing, 80, 72-79.
Liao, M., Tang, J., Wang, T., Balz, T., & Zhang, L. (2012). Landslide monitoring with high-
resolution SAR data in the Three Gorges region. Science China. Earth Sciences, 55(4), 590.
Mondini, A. C., Guzzetti, F., Chang, K.-T., Monserrat, O., Martha, T. R., & Manconi, A.
(2021). Landslide failures detection and mapping using Synthetic Aperture Radar: Past,
present and future. Earth-Science Reviews, 216, 103574.
Papathoma-Köhle, M., Kappes, M., Keiler, M., & Glade, T. (2011). Physical vulnerability
assessment for alpine hazards: state of the art and future needs. Natural hazards, 58, 645-680.
Rott, H. (2009). Advances in interferometric synthetic aperture radar (InSAR) in earth system
science. Progress in Physical Geography, 33(6), 769-791.
Svalova, V., Zaalishvili, V., Ganapathy, G., Nikolaev, A., & Melkov, D. (2019). Landslide
risk in mountain areas. Geology of the South of Russia, 2.
Tessari, G., Floris, M., & Pasquali, P. (2017). Phase and amplitude analyses of SAR data for
landslide detection and monitoring in non-urban areas located in the North-Eastern Italian pre-
Alps. Environmental earth sciences, 76, 1-11.

51. 49
Thapa, P. B. (2018). Geo-disaster and risk sensitive land use planning in Nepal. Bulletin of
Nepal Geological Society, 35.
Tomás, R., Romero, R., Mulas, J., Marturià, J. J., Mallorquí, J. J., López-Sánchez, J. M., . .
. Fernández, J. (2014). Radar interferometry techniques for the study of ground subsidence
phenomena: a review of practical issues through cases in Spain. Environmental earth sciences,
71, 163-181.
Yerro, A., Corominas, J., Monells, D., & Mallorquí, J. J. (2014). Analysis of the evolution
of ground movements in a low densely urban area by means of D-InSAR technique.
Engineering Geology, 170, 52-65.
Blanco-Sànchez, P., Mallorquí, J. J., Duque, S., & Monells, D. (2008). The Coherent Pixels
Technique (CPT): An Advanced DInSAR Technique for Nonlinear Deformation Monitoring.
In A. G. Camacho, J. I. Díaz, & J. Fernández (Eds.), Earth Sciences and Mathematics (pp. 1167–
1193). Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8907-9_10
Bouchra, A., Mustapha, H., & Mohammed, R. (2020). D-InSAR analysis of Sentinel-1 data for
landslide detection in northern Morocco, case study: Chefchaouen. Journal of Geoscience and
Environment Protection, 8(7), 84–103.
Caló, F., Notti, D., Galve, J. P., Abdikan, S., Görüm, T., Pepe, A., & Balik Şanli, F. (2017).
DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in
Konya Plain, Turkey. Remote Sensing, 9(1), Article 1. https://doi.org/10.3390/rs9010083
Chen, Y., & Xu, H. (2014). Comparisons of Speckle Noise Filtering Methods on Interferometric
Synthetic Aperture Radar Images. Journal of Computers, 9(4), 908–915.
https://doi.org/10.4304/jcp.9.4.908-915
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–
874. https://doi.org/10.1016/j.patrec.2005.10.010

52. 50
Gattinoni, P., Consonni, M., Francani, V., Leonelli, G., & Lorenzo, C. (2019). Tunnelling in
landslide areas connected to deep seated gravitational deformations: An example in Central
Alps (northern Italy). Tunnelling and Underground Space Technology, 93, 103100.
https://doi.org/10.1016/j.tust.2019.103100
Hanssen, R. F. (2001). Radar Interferometry: Data Interpretation and Error Analysis. Springer
Science & Business Media.
Kalaranjini, V. S., & Ramakrishnan, S. S. (2020). Landslide investigation using SAR
Interferometry on selected regions of Idukki district, Kerala, India. 49(05).
Kelany, K. A. H., Baniasadi, A., Dimopoulos, N., & Gara, M. (2020). Improving InSAR Image
Quality and Co-Registration through CNN-Based Super-Resolution. 2020 IEEE International
Symposium on Circuits and Systems (ISCAS), 1–5.
https://doi.org/10.1109/ISCAS45731.2020.9180733
Kumar, A., & Kumar, D. (2020). Urban Features Identification from Dual-Pol SAR Images with
Filter Properties. Journal of Landscape Ecology, 13(3), 39–62. https://doi.org/10.2478/jlecol-
2020-0016
Li, F. K., & Goldstein, R. M. (1990). Studies of multibaseline spaceborne interferometric
synthetic aperture radars. IEEE Transactions on Geoscience and Remote Sensing, 28(1), 88–97.
https://doi.org/10.1109/36.45749
Maître, H. (2013). Processing of Synthetic Aperture Radar (SAR) Images. John Wiley & Sons.
Mastro, P., Serio, C., Masiello, G., & Pepe, A. (2020). The Multiple Aperture SAR
Interferometry (MAI) Technique for the Detection of Large Ground Displacement Dynamics:
An Overview. Remote Sensing, 12(7), Article 7. https://doi.org/10.3390/rs12071189
Mirbagheri, M., Barbeau, H., Ladouceur, M., & Kearney, R.(2001). Intrinsic and reflex stiffness
in normal and spastic, spinal cord injured subjects. Experimental Brain Research, 141(4), 446–
459. https://doi.org/10.1007/s00221-001-0901-z

53. 51
Niraj, K. C., Gupta, S. K., & Shukla, D. P. (2022). Kotrupi landslide deformation study in non-
urban area using DInSAR and MTInSAR techniques on Sentinel-1 SAR data. Advances in
Space Research, 70(12), 3878–3891. https://doi.org/10.1016/j.asr.2021.11.042
Pepe, A., & Calò, F. (2017). A Review of Interferometric Synthetic Aperture RADAR (InSAR)
Multi-Track Approaches for the Retrieval of Earth’s Surface Displacements. Applied Sciences,
7(12), Article 12. https://doi.org/10.3390/app7121264
Pepe, A., Euillades, L. D., Manunta, M., & Lanari, R. (2011). New Advances of the Extended
Minimum Cost Flow Phase Unwrapping Algorithm for SBAS-DInSAR Analysis at Full Spatial
Resolution. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 4062–4079.
https://doi.org/10.1109/TGRS.2011.2135371
Petley, D. (2012). Global patterns of loss of life from landslides. Geology, 40, 927–930.
https://doi.org/10.1130/G33217.1
Prats, P., Reigber, A., & Mallorqui, J. J. (2005). Topography-dependent motion compensation
for repeat-pass interferometric SAR systems. IEEE Geoscience and Remote Sensing Letters,
2(2), 206–210. https://doi.org/10.1109/LGRS.2005.846005
Pu, L., Zhang, X., Zhou, Z., Li, L., Zhou, L., Shi, J., & Wei, S. (2021). A Robust InSAR Phase
Unwrapping Method via Phase Gradient Estimation Network. Remote Sensing, 13(22), Article
22. https://doi.org/10.3390/rs13224564
Semwal, P. (2013). Study of slow moving landslide using SAR Interferometry technique.
Andhra University, Indian Institute of Remote Sensing, Dehradun.
Tarchi, D., Casagli, N., Fanti, R., Leva, D. D., Luzi, G., Pasuto, A., Pieraccini, M., & Silvano,
S. (2003). Landslide monitoring by using ground-based SAR interferometry: An example of
application to the Tessina landslide in Italy. Engineering Geology, 68(1), 15–30.
https://doi.org/10.1016/S0013-7952(02)00196-5
Tomás, R., & Li, Z. (2017). Earth Observations for Geohazards: Present and Future Challenges.
Remote Sensing, 9(3), Article 3. https://doi.org/10.3390/rs9030194

54. 52
Tzouvaras, M., Kouhartsiouk, D., Agapiou, A., Danezis, C., & Hadjimitsis, D. G. (2019). The
Use of Sentinel-1 Synthetic Aperture Radar (SAR) Images and Open-Source Software for
Cultural Heritage: An Example from Paphos Area in Cyprus for Mapping Landscape Changes
after a 5.6 Magnitude Earthquake. Remote Sensing, 11(15), Article 15.
https://doi.org/10.3390/rs11151766
Vakhshoori, V., Pourghasemi, H. R., Zare, M., & Blaschke, T. (2019). Landslide Susceptibility
Mapping Using GIS-Based Data Mining Algorithms. Water, 11(11), Article 11.
https://doi.org/10.3390/w11112292
Waltham, T. (2009). Foundations of Engineering Geology, Third Edition. CRC Press.
Wang, S., Bai, Z., Lv, Y., & Zhou, W. (2022). Monitoring Extractive Activity-Induced Surface
Subsidence in Highland and Alpine Opencast Coal Mining Areas with Multi-Source Data.
Remote Sensing, 14(14), Article 14. https://doi.org/10.3390/rs14143442
Wei, M., & Sandwell, D. T. (2010). Decorrelation of L-Band and C-Band Interferometry Over
Vegetated Areas in California. IEEE Transactions on Geoscience and Remote Sensing, 48(7),
2942–2952. https://doi.org/10.1109/TGRS.2010.2043442
Ye, Y., Yang, C., Zhu, B., Zhou, L., He, Y., & Jia, H. (2021). Improving Co-Registration for
Sentinel-1 SAR and Sentinel-2 Optical Images. Remote Sensing, 13(5), Article 5.
https://doi.org/10.3390/rs13050928
Zebker, H. A., Rosen, P. A., Goldstein, R. M., Gabriel, A., & Werner, C. L. (1994). On the
derivation of coseismic displacement fields using differential radar interferometry: The Landers
earthquake. Journal of Geophysical Research: Solid Earth, 99(B10), 19617–19634.
https://doi.org/10.1029/94JB01179
Zebker, H. A., Rosen, P. A., & Hensley, S. (1997). Atmospheric effects in interferometric
synthetic aperture radar surface deformation and topographic maps. Journal of Geophysical
Research: Solid Earth, 102(B4), 7547–7563. https://doi.org/10.1029/96JB03804

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Annex
Figure 21: Figure showing wrapped phase between 2020-2022
Figure 22: Figure showing wrapped phase between 2020-2022

56. 54
Figure 23: Figure showing topographic contribution between 2016-2020
Figure 24: Figure showing topographic contribution between 2020-2022