[Show abstract][Hide abstract] ABSTRACT: Mapping of debris-covered glacier boundaries using remote sensing technique is restricted by the presence of supraglacial debris (debris over the glacier) since it has similar spectral properties than that of periglacial debris (debris outside glacial boundary). However, earlier studies have suggested that the temperature differences between the supraglacial and periglacial debris and/or geo-morphometric parameters can be used to separate these two classes. Several automated and semi-automated approaches have been developed for the mapping of debris-covered glacial boundaries utilizing thermal information and/or geo-morphometric parameters. Most of the techniques utilizing multisource datasets use semi-automated time consuming method of classification. In this article, a novel hybrid classification scheme utilizing both the maximum likelihood classification and knowledge based classification has been used which integrates inputs from ASTER optical, thermal and DEM remote sensing data for mapping debris-covered glacier boundary in a test area in the Chenab basin, Himalayas, India. The results of this new proposed classification scheme were compared with the classification results of maximum likelihood classification which has been used earlier by several researchers for a similar type of mapping. Further, cloud is also considered as one of the major hindrance in mapping of the glaciers due to its similar reflectance as that of snow. Additionally, the low radiometric resolution of most of the optical remote sensing data may sometimes cause serious problem in mapping glacial terrain classes due to saturation towards higher DN values due to higher reflectance of snow. A contrast enhancement using band transformation has been proposed in this remote sensing based study to resolve such problems.
Remote Sensing of Environment 01/2015; In Press. · 4.77 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Debris cover over glaciers affects the rate of ablation and is considered as an indicator of glacier health. It also affects the ability to map glacier bodies and thereby has a bearing on the accuracy of modelling used for climate prediction, runoff estimation, etc. Mapping of debris-covered glacier boundaries using remote sensing technique is restricted by the presence of supraglacial debris since it has similar spectral properties than that of periglacial debris. However, earlier studies have suggested that the temperature differences between the supraglacial and periglacial debris and/or geo-morphometric parameters can be used to map the extent of these two classes. Several automated and semi-automated approaches have been developed for the mapping of debris-covered glacial boundaries utilizing thermal information and/or geo-morphometric parameters. Most of the techniques utilizing multisource datasets uses semi-automated method of classification which are time consuming. A novel hybrid classification scheme utilizing both the maximum likelihood classification and knowledge based classification has been used here which integrates inputs from ASTER optical, thermal and DEM remote sensing data for mapping debris-covered glacier boundary. Further, cloud is also considered as one of the major hindrance in mapping of the glaciers due to its similar reflectance as of snow. Additionally, the low radiometric resolution of most of the optical remote sensing data may sometimes cause serious problem in mapping glacial terrain classes due to saturation towards higher DN values due to higher reflectance of snow. A contrast enhancement using band transformation has been proposed in this remote sensing based study to resolve such problems.
National Conference on Himalayan Glaciology (NCHG-2014), Shimla; 10/2014
[Show abstract][Hide abstract] ABSTRACT: The surface ice velocity has a major impact on the health and fate of the glacier. Measurement of ice velocity can help in modeling the glacier dynamics. This article presents sub-pixel image correlation technique (COSI-Corr) for calculating glacier ice velocity.
It is difficult to obtain sufficient ice velocity data with conventional glaciological techniques (field measurements) due to the frequent loss of stakes and difficulty in the handling of measuring instruments at the site. A number of researchers have also used SAR interferometry/speckle tracking to map glacier ice velocities. However, it has been reported that SAR based works have limitations in highly rugged terrains like the Himalayas and especially for fast-moving glaciers.
The literature suggests that optical image based correlation techniques appear to be more successful and robust matching method than SAR interferometry for the measurement of glacier ice velocity in Himalayan terrain, and therefore, the former is the focus in this study.
The principle involved in this technique is that two images acquired at different times are correlated to find out the shift in position of any moving object, which is then treated as displacement in this time interval. Though it has been used to measure ground deformation but it has been suggested that the proposed technique would also allow for the measurement of surface displacements due to ice-flow or geomorphic processes, or for any other change detection application. The algorithm works in four fundamental steps: In the first step each pixel from the satellite focal plane is projected onto a ground reference system. This operation utilizes knowledge from both the imaging systems and the ground topography. The second step involves optimizing the satellite viewing parameters with respect to some reference frame. The third step involves resampling of the acquired images with the previously calculated parameters. This yields ground-projected images, called orthorectified images. Then in the the fourth step, image correlation is run to calculate surface ice velocities. This algorithm has now been implemented in a software package, Co-registration of Optically Sensed Images and Correlation (COSI-Corr), developed with Interactive Data Language (IDL) and integrated under ENVI. The described approach allows for the correction of offsets due to attitude effects and sensor distortions, as well as elevation errors. This methodology is thus well suited to generate accurate, low-cost glacier-ice velocity data of remote regions like Himalayan glaciers where ground instrumentation is difficult to implement and terrain conditions are inhospitable.
National Conference on Himalayan Glaciology (NCHG-2014), Shimla; 10/2014
[Show abstract][Hide abstract] ABSTRACT: Water Cloud Model (WCM) relates the backscatter coefficient (σo) with soil moisture. The backscatter coefficient includes the backscatter coefficient due to vegetation (σoveg), and the backscatter coefficient due to soil (σosoil). The σoveg of WCM depends upon vegetation characteristics. The present study is aimed to investigate the effect of different vegetation descriptors in estimating soil moisture from WCM. The study is carried out in Solani river catchment of India. ENVISAT ASAR images of three dates were acquired for the study. The field data, volumetric soil moisture from the upper 0-10 cm soil layer, soil texture, soil surface roughness, Leaf Area Index (LAI), Leaf Water Area Index (LWAI), Normalized Plant Water Content (NPWC) and average Plant Height (PH) corresponding to satellite pass dates were collected. Genetic Algorithm optimization technique is used to estimate the WCM vegetation parameters. The use of LAI as vegetation descriptor results in minimum root mean square error (RMSE) of 1.77 dB between WCM computed backscatter and ENVISAT ASAR observed backscatter. Also, use of LAI in WCM as vegetation descriptor results in the least RMSE of 4.19%, between estimated and observed soil moisture for the first field campaign while it was 5.64% for the last field campaign which was undertaken after 35 days of first campaign. It is concluded that LAI can be treated as the best vegetation descriptor in studies retrieving soil moisture and backscatter from microwave remote sensing data. This article is protected by copyright. All rights reserved.
[Show abstract][Hide abstract] ABSTRACT: Although hyperspectral images contain a wealth of information due to its fine spectral resolution, the information is often redundant. It is therefore expedient to reduce the dimensionality of the data without losing significant information content. The aim of this paper is to show that proposed fractal based dimensionality reduction applied on high dimensional hyperspectral data can be proved to be a better alternative compared to some other popular conventional methods when similar classification accuracy is desired at a reduced computational complexity. Amongst a number of methods of computing fractal dimension, three have been applied here. The experiments have been performed on two hyperspectral data sets acquired from AVIRIS sensor.
Optics and Lasers in Engineering 04/2014; 55:267–274. · 1.70 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Earthquake is one of the most destructive natural hazards which pose a
real threat to India with nearly 59% of its geographical area vulnerable
to seismic disturbance of varying intensities including the capital city
of the country. India has experienced several major earthquakes mainly
in Himalayan region and is also considered as one of the most earthquake
prone regions in the world. Therefore, during past few decades, the
Himalayan region has been studied extensively in terms of present
ongoing displacements. It can be believed that a better estimate of the
current Himalayan convergence rate and possible rupture can improve
seismic hazard evaluations. Moreover, an improved convergence rate is
also necessary to estimate if any slip deficit is available to drive
future earthquakes in this region. In recent years, SAR interferometry
has been successfully used for generating large scale surface
displacement maps in radar look direction on a dense grid and with a
centimeter to millimeter accuracy. In this context, the usefulness of
SAR interferometry technique and its variations for estimation of
displacements has been studied and presented in this paper. To study the
displacement both conventional and multi-temporal Differential SAR
interferometry has been used. In order to get the 3-D surface
displacement, interferogram from both ascending and descending track can
be used. However, due to the unavailability of ascending track data, a
well-known mathematical model also has been used. Overall average
displacement rates in the present study are found to be relatively lower
as compared to the reported convergence rates. From geophysical point of
view, the results presented in this paper for a small area are quite
promising. Several explanations have also been presented in this paper
to support the results. The reported low convergence rate may be due to
the 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
Optics and Lasers in Engineering 02/2014; · 1.70 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The aim of this letter is to estimate incoming and net shortwave radiation fluxes of large snow
covered area of western Himalaya and to evaluate the results with in situ observations. Radiation
fluxes are estimated at spatial level using remote sensing data from Moderate Resolution
Imaging Spectroradiometer (MODIS) sensor and Shuttle Radar Topographic Mission (SRTM)
digital elevation model (DEM) supplemented with sparse field data obtained from automatic
weather stations (AWSs). Snow cover albedo has been estimated from MODIS data using
narrowband to broadband conversion method for clear sky days. Geo-spatial maps of air temperature (AT) and relative humidity (Rh) have been generated for the study area using AWS recorded AT/Rh and
DEM. Parameterization techniques have been used for estimating incoming and net shortwave radiation fluxes, which have been validated from in situ AWS observations. The root mean square error (RMSE) in estimation of incoming shortwave radiation flux and net shortwave radiation flux has been found to be 75 W m-2 and 84.9 W m-2 respectively. Further, the higher radiation fluxes have been observed on south aspect
slopes than those observed on north aspect slopes.
[Show abstract][Hide abstract] ABSTRACT: Landslides are the most damaging and threatening aftereffect of seismic events in Garhwal Himalayas. It is evident from past seismic events in Uttarakhand, India that no other phenomena can produce landslides of so great in size and number as a single seismic event can produce.
Landslide inventories are produced for the study area before and after the occurrence of Chamoli Earthquake using Panchromatic (PAN) sharpened Linear Imaging Self Scanning-III (LISS-III) images. A sudden increase in number of landslides after the earthquake is observed. Further, two Landslide Susceptibility Zonation (LSZ) maps have been derived using pre- and post-Chamoli Earthquake landslide inventories. The difference of two LSZ indicates that landslides are very complex phenomenon and are affected by static factors in seismic conditions also.
An attempt has been made to estimate the seismic displacements using Differential Synthetic Aperture Radar Interferometry (DIn SAR). European Remote Sensing Satellite-1/2 (ERS-1/ 2) SAR images have been used for preparing differential interferogram. Geometric and temporal decorrelation in SAR images is very high in the study area, which limits the use of DInSAR for displacement estimation. Theoretical displacement has been estimated using fault displacement modeling parameters for Chamoli earthquake. Post-Chamoli earthquake landslide inventory is overlaid over displacement map for understanding the impact of seismic displacement pattern with other static factors on the occurrence of landslides. It is observed that distribution and size of landslides is affected by displacement pattern controlled by other static factors also.
[Show abstract][Hide abstract] ABSTRACT: Improved change vector analysis (ICVA) has recently been promoted as an effective algorithm for multi-class change detection. Unlike the conventional change vector analysis (CVA) that works on two-dimensional data, the ICVA works on multidimensional data. However, ICVA has limitations when the change vector is fraught with similar direction cosine values. In this article, a new algorithm, named median change vector analysis (MCVA) has been proposed for multi-class change detection. The algorithm is based on an enhanced 2n-dimensional feature space comprising direction cosine values of both the change vector and the median vector, which allows for more accurate detection of change classes than those obtained from ICVA. As a case study, the proposed algorithm has been implemented on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images of a typical Indian city and surrounding areas for land-cover change detection.
[Show abstract][Hide abstract] ABSTRACT: 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 8–10 mm 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.
[Show abstract][Hide abstract] ABSTRACT: Route planning in hilly areas is a compound job as it involves consideration
of a number of factors. The conventional route planning practice is time
consuming and does not consider factors related to geo-hazards such as landslide
hazard zones, geological faults etc., thereby resulting in increased cost of road
design, maintenance etc. The aim of this chapter is to develop a Geographic
Information System (GIS) based software for planning a road route that passes
through landslide safe areas. A number of thematic cost factors have been integrated
in GIS. Dijkstra’s least-cost finding algorithm together with improved
neighbourhood analysis to compute the neighbourhood movement cost has been
used to find landslide safe route. Working examples have been presented to
demonstrate the utility of the software for route planning in highly landslide prone
area in the Himalayas.
Terrigenous Mass Movements, Edited by B Pradhan, M Buchroithner, 04/2012: pages 349-368; Springer-Verlag Berlin Heidelberg., ISBN: 978-3-642-25495-6
[Show abstract][Hide abstract] ABSTRACT: The aim of this study was to map soil moisture from ERS-2 SAR images by minimizing the effect of vegetation on the backscatter coefficient. Detailed analysis was carried out to identify the prominent crop descriptor (i.e. crop height, h; leaf area index, LAI; and plant water content, PWC), and to minimize its effect on soil moisture estimation. A semi-empirical water cloud model was used to eliminate the vegetation effects on the backscatter coefficient. Our results showed that the water cloud model based on LAI as the canopy descriptor was able to estimate the crop-covered backscatter coefficient more accurately than the models based on either of the other two crop descriptors. Once the crop-covered backscatter coefficient was determined, a nonlinear least square method (LSM) was implemented to estimate the volumetric soil moisture. A significantly high correlation (R ≈ 0.94) between the estimated soil moisture and the corresponding observed soil moisture for barren land, as well as crop-covered surfaces, was obtained. Subsequently, individual soil moisture maps were generated from the three ERS-2 SAR images to depict the spatial distribution of soil moisture during the three seasons.Editor Z.W. Kundzewicz; Associate editor J. SimunekCitation Said, S., Kothyari, U.C. and Arora, M.K., 2012. Vegetation effects on soil moisture estimation from ERS-2 SAR images. Hydrological Sciences Journal, 57 (3), 1–18.
[Show abstract][Hide abstract] ABSTRACT: 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$ 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.
[Show abstract][Hide abstract] ABSTRACT: Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed–Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed–Xiaoli anomaly detector and Uniform Target Detector (RXD–UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.
International Journal of Applied Earth Observation and Geoinformation 10/2011; 13:730-740. · 2.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper introduces a new subpixel classification algorithm that incorporates prior information from known class proportions in the linear mixture model. The prior information is expressed in terms of the occurrence probabilities of each land-cover class in a pixel. The use of different error cost functions that measure the similarity between the model-derived mixed spectra and the observed spectra is also investigated. Under these assumptions, the maximum a posteriori (MAP) methodology is employed for optimization. Finally, optimization problems under the MAP criteria for different error cost functions are formulated and solved. Our numerical results illustrate that the performance of the subpixel classification algorithm can be significantly improved by incorporating prior information from the known class proportions. Furthermore, there are marginal differences in accuracy when different error cost functions are used.
IEEE Transactions on Geoscience and Remote Sensing 04/2011; · 2.93 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Land surface displacement is a phenomenon of ground movement, which may occur due to various reasons including unplanned mining. The quantification of land surface displacement through conventional field surveys is based on sparingly distributed point data, which may be insufficient for many applications. A detailed spatial and temporal monitoring of land surface displacements through remote sensing-based synthetic aperture radar (SAR) interferometry may be valuable. Over the last two decades, differential SAR interferometry (DInSAR) has been effectively used globally for the estimation of spatial land surface displacements caused due to natural and man-made hazards. However, it has not gained momentum in India, where occurrences of natural and man-made hazards are a common phenomenon. In this article, preliminary results from DInSAR to measure land surface displacement in Jharia coal fields have been presented. DInSAR results effectively identified the land surface displacement due to several mining activities in the region during a one-month period.
Geocarto International 01/2011; · 0.90 Impact Factor