Remote Sensing based assessment of glacial lake growth on Milam glacier,Goriganga basin,Kumaon Himalaya

The climate change of the twentieth century had an evident effect on glacier environments of the Himalaya. Temporal images of Indian Remote Sensing satellites provide an opportunity to monitor the recession of glacier and development of glacial lakes in the Himalayan cryosphere with a cost to time benefit ratio. The recession of Milam glacier and subsequent growth of a proglacial lake near the snout was analysed using Resourcesat-1 and Resourcesat-2 data. The recession of 480 m during 2004 to 2011 and growth of 47 epiglacial ponds over Milam glacier shows the glacier is in a state of imbalance and losing the ice by downwasting.     

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

Quantitative modeling of groundwater in Satluj River basin of Rupnagar district of Punjab using remote sensing and geographic information system

Water is a fluctuating resource making it difficult to measure in time and in space. To demonstrate the efficiency of the geographic information system (GIS) for groundwater studies, information on the parameters controlling groundwater such as lithology, geomorphology and lineament analysis were analyzed. LISS-III and Landsat satellite image of the area was used to infer information on the geologic lineaments and geomorphology. To delineate linear features enhancement and direction, filtering was performed on single bands of Landsat images. Thematic maps for geology, slope, geomorphology and lineament were prepared and integrated in GIS by assigning the weights and ranking to various parameters controlling the occurrence of groundwater to generate the groundwater potential map for the study area. The results indicate that the floodplain of river and its adjoining areas have very good groundwater potential, whereas the steeply sloping area in the northern part having high relief and slope possesses poor groundwater potential.     

Seasonal extreme rainfall variability over India and its association with surface air temperature

Theoretical and Applied Climatology - Spatial variability in catchment processes is crucial for hydrologic and water resources planning and management. The spatial density of ground-based rain...     

Mudbank regime off the Kerala coast during monsoon and non-monsoon seasons

Field experiments conducted in the nearshore ocean to understand the dynamics of mudbank off Kerala, south-west coast of India, are highlighted. Real time monitoring of the nearshore ocean off Purakkad, Kerala was accomplished using pressure transducers for nearshore surface wave measurements, and current sensors for nearshore velocity measurements. Comprehensive information on the spatial structure of mudbank was obtained from aerial surveys. Extensive data collected on surface waves and currents in the nearshore ocean, indicate that the infra-gravity (IG) waves (leaky modes and trapped edge wave modes), and far infra-gravity (FIG) waves coupled with strong shoreline reflections and undertow play an important role in the dynamics associated with the mudbanks off Kerala during the monsoon season. During the non-monsoon season evidence for progressive edge waves in the infragravity frequency band, an energetic gravity wave band and a strong undertow with weak reflections was observed.     

Directional spread parameter at intermediate water depth

The characteristics of directional spread parameters at intermediate water depth are investigated based on a cosine power ‘2s' directional spreading model. This is based on wave measurements carried out using a Datawell directional waverider buoy in 23 m water depth. An empirical equation for the frequency dependent directional spreading parameter is presented. Directional spreading function estimated based on the Maximum Entropy Method is compared with those obtained using a cosine power ‘2s' parameter model. A set of empirical equations relating the directional spreading parameter corresponding to the peak of wave spectrum to other wave parameters like significant wave height and period are obtained. It shows that the wave directional spreading at peak wave frequency can be related to the non-linearity parameter, which allows estimation of directional spreading without reference to wind information.     

Interpolation of wave heights

Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output.