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Glaciers and snow cover area (SCA) plays an important role in river runoff in Himalayan region. There is a need to monitor SCA on spatio-temporal basis for better and efficient utilization of water resources. Moderate Resolution Imaging Spectroradiometer (MODIS) provides less cloudy data due to high temporal resolution as compared to other optical sensors for high elevation regions, and its 8-day snow cover product is globally used for snow cover estimation. The main objective of the present paper is to estimate annual and seasonal SCA in Chandra basin, Western Himalaya, and analysis of its variation with elevation, aspect, and slope during 2001 to 2017 using MODIS Terra (MOD10A2) and Aqua (MYD10A2) snow cover product as well as to correlate the same with temperature and precipitation using fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the global climate (ERA5) data. The total average SCA observed is 84.94% of basin area during the study period. The maximum annual average SCA was found as 91.23% in 2009 with minimum being 76.37% in 2016. Strong correlation is observed in annual and seasonal SCA with temperature which indicate that SCA variability is highly sensitive to temperature.

In glacier dynamics, surface velocity of glacier is an important parameter to understand the behaviour of glacier in absence of mass balance and long-term glacier area change information. In present study, surface velocity of Samudra Tapu Glacier, India is estimated using freely available Landsat-8 OLI (PAN) images during 2013–2017. To estimate surface velocity, open source COSI-Corr tool is used which is based on cross-correlation algorithm. Maximum annual surface velocity estimated is 55.68 ± 4.01 m/year during 2015–2016 while the minimum surface velocity being 44.99 ± 4.67 m/year in 2016–2017. The average annual velocity during 2013–2017 was 50.51 ± 4.49 m/year which is higher than other glaciers in Chandra basin. The variation in annual surface velocity is analysed which not only depends on mass loss but also on temperature, pressure and internal drainage. Further, as one moves opposite to glacier terminus, the surface velocity increases with the increase in glacier elevation and slope. The higher surface velocity can be attributed to the fact that Samudra Tapu is a top-heavy glacier based on HI index analysis having larger accumulation area along with high glacier ice-thickness.

Delineation of the glacier is an important task for understanding response of glaciers to climate. In Himalayan region, most of the glaciers are covered with debris. Supraglacial debris works as an obstacle for automatic mapping of glacier using remote sensing data. Different methods have been used to reduce this difficulty based on pixel-based and object-based approaches using optical data, thermal data and DEM. Pixel-based glacier mapping is a traditional method for delineation of the glacier but the object-based method has emerged as a new approach in cryosphere application leading to its successful application in different applications. All pixel-based methods require some degree of manual correction because these can’t be delineated automatically, especially in shadow area and debris covered part of the glacier. In the majority of studies, the object-based method has provided higher accuracy to delineate the debris-covered glacier. Spatially high spatial resolution satellite data is best suited for object-based image classification. In future, a combination of pixel-based method and object-based method can be attempted for delineation of the debris-covered glacier along with its critical analysis for suitability. The present paper critically reviews pixel-based and object-based methods as well as provides a framework for combined pixel and object-based method for delineation of debris-covered glacier.

Globally, 54.5% of the total population was living in urban settings in 2016 and a projection indicates that if the same trend goes, then this population will be 60% in 2030. Natural land has been converted to impervious space rapidly which is altering the climate change. The main focus of the present paper is the study of Land Surface Temperature (LST) dynamics and its relationship with Land Indices, viz., Normal Difference Vegetation Index (NDVI) which is found negative, Normal Difference Built-up Index (NDBI) which is found positive, Enhanced Built-up and Bareness Index (EBBI) which is found positive in Lucknow city on both time points of 1993 and 2019. This study also includes the effects of land indices on LST profiling in nine different parts and eight different directions to explore the spatial dynamics of city landscape. The NDVI is found higher in the southern side than any other parts of the city in 2019 because of high vegetation growth which resulted in reduction of LST by 4.42 °C to 5.76 °C as compared to parts of the city. The results of NDBI and EBBI exhibit high built-up growth in the landscape of the Lucknow city especially from city center to 13 kms (least growth in south-eastern side) from 1993 to 2019. The results indicate intensification of LST in the range of 0.26 °C to 2.24 °C between city centre and city periphery from 1993 to 2019. The findings of the present study will help urban planners and policy makers to adopt suitable measures for sustainable planning for Lucknow city landscape to reduce the adverse effects of LST.

Metabolic engineering is an attractive approach in order to improve the microbial production of drugs. Triterpenes is a chemically diverse class of compounds and many among them are of interest from a human health perspective. A systematic experimental or computational survey of all feasible gene modifications to determine the genotype yielding the optimal triterpene production phenotype is a laborious and time-consuming process. Based on the recent genome-wide sequencing of Saccharomyces cerevisiae CEN.PK 113-7D and its phenotypic differences with the S288C strain, we implemented a strategy for the construction of a β-amyrin production platform. The genes Erg8, Erg9 and HFA1 contained non-silent SNPs that were computationally analyzed to evaluate the changes that cause in the respective protein structures. Subsequently, Erg8, Erg9 and HFA1 were correlated with the increased levels of ergosterol and fatty acids in CEN.PK 113-7D and single, double, and triple gene over-expression strains were constructed. The six out of seven gene over-expression constructs had a considerable impact on both ergosterol and β-amyrin production. In the case of β-amyrin formation the triple over-expression construct exhibited a nearly 500% increase over the control strain making our metabolic engineering strategy the most successful design of triterpene microbial producers.

Sharing and management of geospatial data among different communities and users is a challenge which is suitably addressed by Spatial Data Infrastructure (SDI). SDI helps people in the discovery, editing, processing and visualization of spatial data. The user can download the data from SDI and process it using the local resources. However, large volume and heterogeneity of data make this processing difficult at the client end. This problem can be resolved by orchestrating the Web Processing Service (WPS) with SDI. WPS is a service interface through which geoprocessing can be done over the internet. In this paper, a WPS enabled SDI framework with OGC compliant services is conceptualized and developed. It is based on the three tier client server architecture. OGC services are provided through GeoServer. WPS extension of GeoServer is used to perform geospatial data processing and analysis. The developed framework is utilized to create a public health SDI prototype using Open Source Software (OSS). The integration of WPS with SDI demonstrates how the various data analysis operations of WPS can be performed over the web on distributed data sources provided by SDI.

Geographic Information System (GIS) is a tool used for capture, storage, manipulation, query and presentation of spatial data that have applicability in diverse fields. Web GIS has put GIS on Web, that made it available to common public which was earlier used by few elite users. In the present paper, development of Web GIS frameworks has been explained that provide the requisite knowledge for creating Web based GIS applications. Open Source Software (OSS) have been used to develop two Web GIS frameworks. In first Web GIS framework, WAMP server, ALOV, Quantum GIS and MySQL have been used while in second Web GIS framework, Apache Tomcat server, GeoServer, Quantum GIS, PostgreSQL and PostGIS have been used. These two Web GIS frameworks have been critically compared to bring out the suitability of each for a particular application as well as their performance. This will assist users in selecting the most suitable one for a particular Web GIS application.

The emergence of new tools and technologies to gather the information generate the problem of processing spatial big data. The solution of this problem requires new research, techniques, innovation and development. Spatial big data is categorized by the five V’s: volume, velocity, veracity, variety and value. Hadoop is a most widely used framework which address these problems. But it requires high performance computing resources to store and process such huge data. The emergence of cloud computing has provided, on demand, elastic, scalable and payment based computing resources to users to develop their own computing environment. The main objective of this paper is to develop a cloud enabled hadoop framework which combines cloud technology and high computing resources with the conventional hadoop framework to support the spatial big data solutions. The paper also compares the conventional hadoop framework and proposed cloud enabled hadoop framework. It is observed that the propose cloud enabled hadoop framework is much efficient to spatial big data processing than the current available solutions.

With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the whole cycle of geospatial data production and distributions, i.e., from data capture to storage and to sharing. SDI initiative at national level, termed as National Spatial Data Infrastructure (NSDI), has been taken by different countries including India. Geospatial community is facing various challenges like handling of large volumes of geospatial data, requirement of high computing resources to process geospatial data, scalability and interoperability. Therefore, need of advanced technologies in the form of SDI and cloud computing is realized to resolve the above challenges. Cloud computing has several characteristics like scalability, elasticity and self-provisioning that offers high-performance computing resources to perform geoprocessing efficiently. The main aim of the present paper is to study SDI and its components along with analysis and comparison of NSDI of various countries as well as to conceptualize and discuss service oriented architecture of cloud enabled SDI. Several challenges of the spatial data handling and processing that occurred due to the high intensity of data and lack of processing capability can be solved by adopting proposed cloud enabled SDI architecture. This research will help geospatial community and SDI developers in various perspectives including data sharing and management, interoperability, security and reliability, fault tolerance, mass market solution, upfront cost and global collaboration.

Ensembles of two Global Climate Models (GCMs), CGCM3 and HadCM3, are used to project future maximum temperature ( T Max ), minimum temperature ( T Min ) and precipitation in a part of Sutlej River Basin, northwestern Himalayan region, India. Large scale atmospheric variables of CGCM3 and HadCM3 under different emission scenarios and the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis datasets are downscaled using Statistical Downscaling Model (SDSM). Variability and changes in T Max , T Min and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model are presented for future periods: 2020s, 2050s and 2080s. The study reveals rise in annual average T Max , T Min and precipitation under scenarios A1B and A2 for CGCM3 model as well as under A2 and B2 scenarios for HadCM3 model in 2020s, 2050s and 2080s. Increase in mean monthly T Min is also observed for all months of the year under all scenarios of both the models. This is followed by decrease in T Max during June, July August and September. However, the model projects rise in precipitation in months of July, August and September under A1B and A2 scenarios of CGCM3 model and A2 and B2 of HadCM3 model for future periods.