Prevailing technologies have begun to emerge in the recent past to collect, store, manage, analyze, and communicate information regarding the Earth's surface phenomenon and to combine these with other types of environmental, social and economic information. Geospatial technologies, which include geographic information systems (GIS), the global positioning system (GPS), and remote sensing, are being used for disaster management, management and conservation of natural resources, infrastructure planning and development, land use planning are just a few examples of areas in which decision-making is contingent on availability of accurate and high quality spatial data. Developments in digital technologies, particularly the rapid advancements in Geographic Information Systems and Global Positioning Systems have now made it possible to correlate and use diverse map information, in conjunction, at the click of a mouse. There is a need for a PhD degree in GIS as the private/public sectors require intensive research in order to compete in the national/international market. Although the routine GIS applications are being covered by GIS analysts, research based products cannot be addressed properly until PhD level candidates are inducted in the private/public sectors. The PhD in Geospatial Information Sciences at NIIT University aims to develop individuals capable of evolving this field by developing new knowledge or competencies relevant to it.
The mission of the Doctor of Philosophy in Geographic Information Sciences program is to nurture innovative researchers capable of advancing the frontiers of knowledge in the geospatial information sciences through improved theories, new technologies, innovative methodologies, sophisticated quantitative analyses, and integrative applications. The programme graduates will be able to demonstrate their knowledge in the advanced methodologies of quantitative analyses used in spatial modelling, spatial computing and remote sensing.
Key Areas of Research:
Land Change Modeling, Estimation of Carbon Stocks and Emission Using Geospatial Technology
The Intergovernmental Panel on Climate Change (IPCC) estimates (AR4, 2007) that 20 per cent of global carbon emissions originate from land use changes such as deforestation and forest degradation. To combat this phenomenon, reducing emissions from deforestation and forest degradation or REDD plus as is widely known, is a set of steps designed to use market and financial incentives in order to reduce the emissions of greenhouse gases from deforestation and forest degradation. There have been many drivers of deforestation which can be planned land-cover changes resulting from zoned infrastructure development and agriculture or unplanned changes that usually occur in areas of poor governance, often manifesting themselves as spontaneous settlements along roads, near mines or adjacent to existing settlements. Areas at risk from both planned and unplanned deforestation and forest degradation are eligible for REDD+ payments after emission reductions have been measured and verified.
In Neemrana, a variety of activities are taking place, for example, development of industries and educational institutions and connected inflow of people and their domestic settlement, existing settlement leading to a change in the landuse and its pattern; simultaneously afforestation activities leading to sink. The amount by which emissions have been reduced can be measured and modeled and can be systematically and accurately recorded and analyzed using GIS, which will lead to evaluation of current practices and effective strategies for future purposes. Key areas of research are:
Modeling areas at risk of deforestation; and
Estimating carbon stocks, emissions and probable benefits from REDD+
Recent controversy about the rates of vanishing of Himalayan glaciers due to an erroneous statement in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, has shown major gaps in knowledge about the behavior of the Himalayan glaciers: Lack of information about long-term glacier changes and climate change in the Garhwal Himalayas; where field surveys are difficult due to complex terrain and harsh weather conditions, the availability of remotely sensed and climate datasets provides an opportunity for analyzing large data sparse glacier basins.
Research focus is to estimate the glacier changes in Garhwal Himalayas during the last five decades as a consequence of regional climatic changes using remote sensing and GIS methods., The objectives of the ongoing research are six fold:
To define knowledge base parameters which affect glacier formation and melting such as accumulation area, ablation area, slope, snow line altitude, total area, mass balance etc;
To develop knowledge based glacier mapping technique, which can utilize the visible, shortwave and thermal infrared data with topographic parameters;
To quantify spatio-temporal patterns of glacier changes in the Garhwal Himalaya in the last decades using developed knowledge based approach;
To derive mass change of glaciers using remote sensing geodetic method using multitemporal DEM’s;
To estimate the ice thickness distribution and volume of Gangotri glacier using Artificial Neural Networks;
To forecast the glacier status of Garhwal Himalayas with the help of developed knowledgebase.
Imaging spectrometers, or hyperspectral sensors, are remote sensing instruments that combine the spatial presentation of an imaging sensor with the analytical capabilities of a spectrometer. They may have up to several hundred narrow spectral bands with spectral resolution on the order of 10 nm or narrower. They have the capability to identify various earth surface features. We at NU are working on the development of methodologies for identification and classification of complex earth surfaces such as snow, ice, water and vegetation.
Web GIS Development
Web-based GIS is evolved from different Web maps and client-server architecture to distributed ones. As such, Internet redesigns all functions of information systems including: gathering, storing, retrieving, analysing, and visualizing data. Sharing spatial information on the web improves the decision-making processes. The research in this area aims at developing high performance Web GIS applications that can provide visualization, querying and analysing spatial information on the fly.