Manoj K. Arora

Potential of SAR intensity tracking technique to estimate displacement rate in a landslide-prone area in Haridwar region, India

Landslides constitute one of the major natural hazards that could cause significant loss of life and various human settlements. Mansa Devi hill near Haridwar city has encountered with such potential hazard for several years due to the instability of the slopes. Therefore, preparedness both on regional and site-specific basis at spatial level in the form of surface movements is extremely important to diminish the damage of human life and settlements. Though the surface movement measurement through field-based technique is always very accurate, this technique is time-consuming and unfeasible over a widely affected region. Therefore, areal and satellite remote sensing is gaining importance in landslide investigation due to its wide coverage. In recent years, synthetic aperture radar has already proven its potential for mapping ground deformation due to earthquake, landslide, volcano, etc. Therefore, in this study, an attempt has been made to identify the potential landslide-affected region in Mansa Devi area using one multi-temporal SAR technique and intensity tracking technique. Intensity tracking technique has identified significant mass movement in the landslide-affected region where the other conventional multi-temporal technique, SBAS, fails. An error analysis has been carried out in order to demonstrate the applicability of intensity tracking technique. This study demonstrated that intensity tracking can be considered as an alternative to conventional interferometry for the estimation of land surface displacement when latter is limited by loss of coherence due to rapid and incoherent surface movement and/or large acquisition time intervals between the two SAR images.

Surface displacement estimation along Himalayan frontal fault using differential SAR interferometry

The Himalayan region has been studied extensively during the past few decades in terms of present ongoing deformations. Various models have been proposed for the evolution of the Himalaya to explain the cause of earthquake occurrences and to understand the seismotectonics of the Himalayan collision zone. However, the information on displacements from field geodetic surveys is still too scarce in time and spatial domains so as to provide convincing evidences. Moreover, classical Probabilistic Seismic Hazard Approaches also fail due to paucity of data in higher magnitude range, thus emphasizing the need of spatial level displacement measurements. It is in this context that the present study has been carried out to estimate the surface displacement in a seismically active region of the Himalaya between Ganga and Yamuna Tear using Differential SAR interferometry. Three single-look complex images, obtained from ASAR sensor onboard ENVISAT satellite, have been used. A displacement rate of 8–10xa0mm per year in N15°E direction of Indian plate has been obtained in this three-pass SAR interferometry study. It has been noted that the estimated convergence rate using Differential SAR interferometry technique is relatively low in comparison with those obtained from previous classical studies. The reported low convergence rate may be due to occurrence of silent/quite earthquakes, aseismic slip, differential movement of Delhi Hardwar ridge, etc. Therefore, in view of the contemporary seismicity and conspicuous displacements, a study of long-term observations of this surface movement has been recommended in future through a time-series SAR interferometry analysis.

Inclusion of earthquake strong ground motion in a geographic information system-based landslide susceptibility zonation in Garhwal Himalayas

Garhwal Himalayas are seismically very active and simultaneously suffering from landslide hazards. Landslides are one of the most frequent natural hazards in Himalayas causing damages worth more than one billion US

A simple measure of confidence for fuzzy land-cover classification from remote-sensing data

and around 200 deaths every year. Thus, it is of paramount importance to identify the landslide causative factors to study them carefully and rank them as per their influence on the occurrence of landslides. The difference image of GIS-derived landslide susceptibility zonation maps prepared for pre- and post-Chamoli earthquake shows the effect of seismic shaking on the occurrence of landslides in the Garhwal Himalaya. An attempt has been made to incorporate seismic shaking parameters in terms of peak ground acceleration with other static landslide causative factors to produce landslide susceptibility zonation map in geographic information system environment. In this paper, probabilistic seismic hazard analysis has been carried out to calculate peak ground acceleration values at different time periods for estimating seismic shaking conditions in the study area. Further, these values are used as one of the causative factors of landslides in the study area and it is observed that it refines the preparation of landslide susceptibility zonation map in seismically active areas like Garhwal Himalayas.

Mass Balance Estimation of Dokriani Glacier in Central Indian Himalaya Using Remote Sensing Data

Representing the quality of thematic maps derived from remote-sensing image classification is important in assessing its fitness for use. Conventional approaches to represent the quality in terms of accuracy need information from the reference data at the same scale. Error-prone or dubious reference data may have an impact on the assessment of quality. Therefore, measures that complement the conventional accuracy measures are required to represent the quality. Uncertainty and confidence are such measures that do not require reference data. Few studies have been attempted to derive pixel-level confidence. However, these measures are not widely adopted by the remote-sensing community due to their limitations. In this article, a simple measure of confidence is derived to represent the quality of fuzzy classification. To derive the confidence value for a pixel, two values, viz. first highest class membership value as evidence and an associated degree of certainty, are required. When the difference between first and second highest membership values is used as degree of certainty in the proposed approach, the confidence measure derived is equal to the complement of existing measure of uncertainty, viz. confusion index in difference form.

Time series insar techniques to estimate deformation in a landslide-prone area in Haridwar region, India

AbstractDokriani Glacier is regarded as one of the important glaciers of Bhagirathi River basin, which fed river Ganges. The length of the glacier is about 4.6xa0km, and snout elevation is about 4028xa0mxa0m.s.l. The mass balance of this glacier was calculated using field-based measurements for few years during 1994 to 2000. However, due to remote and poor accessibility, the field-based measurements could not continue; thus, remote sensing-based methods become useful tool to estimate the long-term mass balance of the glacier. In this study, glacier mass balance has been determined using accumulation area ratio (AAR) method. Remote sensing data sets, e.g. Landsat TM, ETMu2009+u2009and OLI, have been used to estimate AAR for different years from 1994 to 2014. An attempt has also been made to develop a mathematical relationship between remote sensing-derived AAR and field-observed mass balance data of the glacier. Further, this relationship has been used to estimate mass balance of the glacier for different years using remote sensing-derived AAR. Estimated mass balance was validated from ground-observed mass balance for few years. The field-observed and remote sensing-derived mass balance data are compared and showed high correlation. It has been observed that AAR for the Dokriani Glacier varies from 0.64 to 0.71. Mass balance of the glacier was observed between −u200915.54xa0cm and −u200950.95xa0cm during the study period. The study highlights the application of remote sensing in mass balance study of the glaciers and impact of climate change in glaciers of Central Indian Himalaya.n

Wavelet Based Feature Extraction Techniques of Hyperspectral Data

Landslides are one of the most severe geological hazards, which threaten and influence the socioeconomic conditions of many countries across the globe. Many towns, road networks are at risk by these landslides and fatalities of human lives and infrastructure are very common. Landslides are very common in the Indian Himalayas which is geo-dynamically very active. Presence of large number of faults and lineaments make the region geologically very fragile and susceptible to landslides at any scale. Small progressive slips are often likely to occur prior to a sporadic slip. Therefore, progressive slips are investigated in order to enhance disaster prevention capabilities. The aim of this study is to investigate the potential of satellite based advanced InSAR techniques, such as Small Baseline Subset and Persistent Scatterers InSAR, in order to estimate the mass movement of landslide susceptible Haridwar region.

Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model

Hyperspectral data have many applications and are being promoted over multi-spectral data to derive useful information about the earth surface. But this hyperspectral data suffers from dimensionality problem. It is one of the challenging tasks to extract the useful information with no or less loss of information. One such technique to extract the useful information is by using wavelet transformations. In this paper, a series of experiments have been presented to investigate the effectiveness of some wavelet based feature extraction of hyperspectral data. Three types of wavelets have been used which are Haar, Daubechies and Coiflets wavelets and the quality of reduced hyperspectral data has been assessed by determining the accuracy of classification of reduced data using Support Vector Machines classifier. The hyperspectral data has been reduced upto four decomposition levels. Among the wavelets used for feature extraction Daubechies wavelet gives consistently better accuracy than that produced from Coiflets wavelet. Also, 2-level decomposition is capable of preserving more useful information from the hyperspectral data. Furthermore, 2-level decomposition takes less time to extract features from the hyperspectral data than 1-level decomposition.