Spatial and Quantitative Comparison of Topographically Derived Different Classification Algorithms Using AWiFS Data over Himalayas,India

In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.     

Monitoring of Snow Cover Variability in Chenab Basin Using IRS AWiFS Sensor

Monitoring of seasonal snow cover is important for many applications such as melt runoff estimation, climate change studies and strategic requirements. Contribution of seasonal snow melt runoff of Chenab River is significant and important to meet hydrological requirement at foothills. Seasonal snow cover of Chandra, Bhaga, Miyar, Bhut, Warwan and Ravi, six major tributaries of Chenab River, becomes crucial to assess the water availability. In addition, altitudinal distribution of snow cover significantly influences the melt runoff which is highly sensitive to minor variations in atmospheric temperature. In this investigation, remote sensing based Normalized Difference Snow Index technique has been used to generate 10 daily snow cover product. Snow cover monitoring of all the sub-basins were carried out for 10 years from 2004–2005 to 2013–2014 during hydrological year (October to June) using Advanced Wide Field Sensor (AWiFS) of Indian remote sensing satellite (IRS). Accumulation and ablation patterns of snow cover have also been analyzed for the six sub-basins. Accumulation and ablation pattern of snow cover, from 2004 to 2014 which shows slightly increasing trend for all the sub-basins. Meteorological data of Kelong at Bhaga sub-basin was also analysed. Average monthly snow line altitude was estimated for all the sub-basins using hypsographic curve. Chandra and Bhaga sub-basins are at higher altitude and Ravi sub-basin is at lower altitude. It was also observed that areal extent of snow reaches to lower altitude during last 5 years, particularly in Ravi sub-basin.     

MI-ECku: A novel methodology for estimating unsaturated hydraulic conductivity of porous media

Acta Geotechnica - Numerous applications of hydraulic conductivity of porous media (e.g., soils, clay liners, rocks, concrete, ceramic filters, etc.) in their unsaturated state are well established...     

Seismic velocity imaging of the Kumaon–Garhwal Himalaya,India

Natural Hazards - Since the initial collision at 55 Ma, rocks of the Indian crust below the Himalayas have undergone modification chemically and compositionally due to the ongoing...     

Numerical modelling of rheological properties of landslide debris

Debris flow has caused severe human casualties and economic losses in landslide-prone areas around the globe. A comprehensive understanding of the morphology and deposition mechanisms of debris flows is crucial to delineate the extent of a debris flow hazard. However, due to inherent complex field topography and varying compositions of the flowing debris, coupled with a lack of fundamental understanding about the factors controlling the geomaterial flow, interparticle interactions and its final settlement resulted in a limited understanding of the flow behaviour of the landslide debris. In this study, a physical model was set up in the laboratory to simulate and calibrate the debris flow using PFC, a distinct element modelling-based software. After calibration, a case study of the Varunavat landslide was taken to validate the developed numerical model. Following validation with an acceptable level of confidence, several models were generated to evaluate the effect of slope height, slope angle, slope profile, and grain size distribution of the dislodged geomaterial in the rheological properties of debris flow. Both qualitative and quantitative analysis of the landslide debris flow was performed. Finally, the utility of retaining wall and their effect on debris flow is also studied with different retaining wall positions along the slope surface.

     

Understanding the cause and effect relationship of debris slides in Papum Pare district,Arunachal Himalaya,India

Natural Hazards - Slope failures are recurrent phenomena during the Indian Summer Monsoon (ISM) season in the mountainous regions of Arunachal Himalaya, NE India, with a consequent damaging impact...     

Seismic source characteristics in Kachchh and Saurashtra regions of Western India: <Emphasis Type="Italic">b</Emphasis>-value and fractal dimension mapping of aftershock sequences

Seismic source characteristics in the Kachchh rift basin and Saurashtra horst tectonic blocks in the stable continental region (SCR) of western peninsular India are studied using the earthquake catalog data for the period 2006–2011 recorded by a 52-station broadband seismic network known as Gujarat State Network (GSNet) running by Institute of Seismological Research (ISR), Gujarat. These data are mainly the aftershock sequences of three mainshocks, the 2001 Bhuj earthquake (M _w 7.7) in the Kachchh rift basin, and the 2007 and 2011 Talala earthquakes (M _w ≥ 5.0) in the Saurashtra horst. Two important seismological parameters, the frequency–magnitude relation (b-value) and the fractal correlation dimension (D _c) of the hypocenters, are estimated. The b-value and the D _c maps indicate a difference in seismic characteristics of these two tectonic regions. The average b-value in Kachchh region is 1.2 ± 0.05 and that in the Saurashtra region 0.7 ± 0.04. The average D _c in Kachchh is 2.64 ± 0.01 and in Saurashtra 2.46 ± 0.01. The hypocenters in Kachchh rift basin cluster at a depth range 20–35 km and that in Saurashtra at 5–10 km. The b-value and D _c cross sections image the seismogenic structures that shed new light on seismotectonics of these two tectonic regions. The mainshock sources at depth are identified as lower b-value or stressed zones at the fault end. Crustal heterogeneities are well reflected in the maps as well as in the cross sections. We also find a positive correlation between b- and D _c-values in both the tectonic regions.     

Mapping composite vulnerability to groundwater arsenic contamination: an analytical framework and a case study in India

Natural Hazards - Groundwater arsenic (As) contamination affects millions of people in South Asia. In this paper, we propose a composite vulnerability framework to identify, for mitigation, the...