Critical Thinking

A linear velocity signal which had a maximum

A
brief visualisation of the ground deformation velocity maps obtained through
the Standard deviation velocity (Vsd) confirmed that the study area
is affected by deformation in Himalayan region continuously. StaMPS background
is used to analyze phase account with 98521 reference Persistent Scatterers
(PS) candidates identified in study area.  The land surface movement velocities on the
PSs from 2004 to 2007 are derived to produce the deformation map as showed in
figure.

The
Vsd of the surface movement varies from 2.4 to 18.8 mm per year.
Standard deviation is the square root of variance which shows how much the
member of a group differ from the mean value. The earlier prominent scientific studies
also reveals the similar results as in this study.

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In this study, the SBAS technique is used. For the
SBAS analysis the interferograms were again

produced
using the DORIS software. Figure ___ show the input synthetic linear velocity
signal which had a maximum deformation rate of 16.4 mm per year. After
processing the data with STAMP, an open source software, the output results are
obtained after giving corresponding commands in the MATLAB workstation, 18520
number of stable-phase pixels (PS points) are identified, the results/outputs
as follows:

The preparation of landslide susceptibility zoning
(LSZ) map is a major step forward in hazard management. There are two methods
for LSZ mapping with the help of GIS techniques. First one is bivariate
statistics based and another one is Multivariate statistical method bases. In
this study, we apply Modified Bivariate frequency Ratio method for LSZ mapping
where the factor maps are prepared by GIS based qualitative and quantitative
techniques which are useful to analyze the relationship between landslides and
their influencing parameters. A
direct mapping approach has been used to establish the five different LSZ using
classical overlay operations after having established maps representing major
landslide influencing factors. The factors being used include elevation, slope
angle, curvature, land use-land cover and NDVI. Independent and dependent
factors are shown in figure

The Landslide susceptibility zone of study
area was classified using five different classes: (i) very

low susceptibility; (ii) low susceptibility; (iii) moderate
susceptibility; (iv) high susceptibility;  
(v) very high susceptibility. Using the frequency ratio,
the relationship was used as each factor’s rating in the overlay analysis. From
the calculation, it was found that the LSI had a minimum value of 3.17, and a
maximum value of 20.63, with an average value of 9.96 and a standard deviation
of 2.56. The high value of LSI indicates the higher susceptibility to
landslides and vice versa. The landslide susceptibility map calculated for FR
model is shown in Figure .

As
a result of continuous upliftment in Himalayan areas which causes disturbances
in the equilibrium of the isostatic system and hence the terrain tries to re-adjust
it through mass wasting like landslides. Hence, in this study area, the
upliftment is found to be uniform with the help of different scene of satellite
data, it is difficult to conclude that the upliftment is only the cause of
landslide. After comparing both LS and SLSM map, it can be concluded that the
most of the high uplifted region is found in highly landslide prone zone. After
analysing Figure 27 to 31, it’s quite transparent that the high and very
high risk zone for landslides are mostly located in moderate uplifted areas
only.

As
the area contains large amount of vegetation in lower and middle part of the
scene, PS pixels could not be seen at these locations (white space).  Large amount of vegetation holds the surface
and decrease the influence of landslide.

This paper presents a method based on Persistent Scatterer
InSAR techniques to detect the areas where the displacement gaps occur on land
surface near the Gangotri glacier. Comparison of the results using the single
look pixels displacement analysis by PS-InSAR technique with the results using
multi look pixels displacement analysis by SBAS technique and the hypothetical
Relation between LS and SLSM shows very strong structural significance in this
region. In future, further investigation such as field survey is required to
assess the validity of

the
proposed method and need more SAR data for better accuracy as these results are
totally based on Remote sensing Techniques without field survey.

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