Urbanisation Pattern of Incipient Mega Region in India

Urbanisation refers to the growth of the towns and cities due to large proportion of the population living in urban areas and its suburbs at the expense of its rural areas. Unplanned urbanisation leads to the large scale land use changes affecting the sustenance of local natural resources. This necessitates an understanding of spatial patterns of urbanisation to implement appropriate mitigation measures. The focus of the current study is to analyse the spatial patterns of urbanisation and sprawl in Pune city with 10 km buffer using temporal remote sensing data through geo-informatics and spatial metrics. Land use analyses of the city with a buffer of 10km reveals that there has been a significant increase of built-up land from 2.96% (1977) to 20.4% (2013) with the reduction of vegetation from 22.49 to 17.96%. Shannon entropy reveal the tendency of sprawl in NW direction. Zone and Gradient-wise spatial metrics analysis is done to understand the spatial patterns of urbanisation at local levels. The analysis suggests that urbanisation has caused fragmentation with adjacencies in buffer zones. Spatial metrics substantiate rampant sprawl at the peri-urban regions and infilling at city centre. However, this value has reduced in 2013 indicating of reaching the threshold of urbanization. These analyses highlight of the significant changes in land cover with the decline in vegetation, water bodies, etc. This necessitates an integrated approaches in urban planning to ensure the sustenance of water, moderation of micro climate, etc. Conservative urban planning would take into account the sustenance of natural resources and people’s livelihood aspects. Visualization of urban growth at local levels helps the urban planners and decision-makers in understanding the role of policy decisions (industrialization, etc.) on land use dynamics, which helps in evolving region specific development strategies to mitigate the potential impacts on the urban environment. This research provides the details of land use and its development for guiding scientific-based decision support and policy making

Mapping of fuelwood trees using geoinformatics

Rural population of India constitutes about 70% of the total population and traditional fuels account for 75% of the rural energy needs. Depletion of woodlands coupled with the persistent dependency on fuel wood has posed a serious problem for household energy provision in many parts. This study highlights that the traditional fuels still meet 85-95% of fuel needs in rural areas of Kolar district; people prefer fuel wood for cooking and agriculture residues for water heating and other purposes. However, rapid changes in land cover and land use in recent times have affected these traditional fuels availability necessitating inventorying, mapping and monitoring of bioresources for sustainable management of bioresources. Remote sensing data (Multispectal and Panchromatic), Geographic Information System (GIS), field surveys and non-destructive sampling were used to assess spatially the availability and demand of energy. Field surveys indicate that rural household depends on species such as Prosopis juliflora, Acacia nilotica, Acacia auriculiformis to meet fuel wood requirement for domestic activities. Hence, to take stock of fuel wood availability, mapping was done at species level (with 88% accuracy) considering villages as sampling units using fused multispectral and panchromatic data.Bioresources Fuel wood Traditional energy Energy planning Geoinformatics Geographic Information System (GIS) Remote sensing Global Positioning System (GPS) Supervised classification Image fusion Species level mapping

RIEP: Regional integrated energy plan

The energy planning endeavours for a particular region involves the finding of a set of sources and conversion devices so as to meet the energy requirement/demand of all the tasks in an optimal manner. This optimality depends on the objective to minimise the total annual cost of energy and the dependence on non-local resources or maximise the overall system efficiency. Factors such as availability of resources in the region and task energy requirements impose constraints on the regional energy planning exercise. Thus, regional energy planning turns out to be a constrained optimisation problem. This paper describes an optimum energy allocation using integrated energy planning approaches for Uttara Kannada district and makes a satisfying energy allocation plan for the years 2005, 2010 and 2015. Integrated energy planning gives an optimal mix of new/conventional energy sources and is developed based on decision support systems (DSS) approach. The central theme of the energy planning at decentralised level would be to prepare regional energy plans to meet energy needs and development of alternate energy sources at least- cost to the economy and environment. Regional integrated energy planning (RIEP) mechanism takes into account various available resources and demands in a region. This implies that the assessment of the demand supply and its intervention in the energy system, which may appear desirable due to such exercises, must be at a similar geographic scale. Regional energy planning exercises need to be flexible (to cope with rapidly changing energy systems) and easy to use. The application of DSS is a new approach to this problem. Towards the goal of implementing analytical methods for integrated planning, computerised decision-system provides useful assistance in the analyses of available information, the projection of future conditions, and the evaluation of alternative scenarios. Some of the features of DSS found particularly useful in regional energy planning are: (i) flexible structure--allows appropriate feasible levels of disaggregation, (ii) integrated nature--promotes a better overall understanding of many processes and concepts involved in planning, allowing planners to concentrate on specific energy subsectors, and (iii) iterative nature and easy scenario testing features--provide guidance in optimising data collection activities. Regional integrated energy plan (RIEP) is a computer-assisted accounting and simulation tool being developed to assist policy makers and planners at district and state level in evaluating energy policies and develop ecologically sound, sustainable energy plans. Energy availability and demand situation are projected for various scenarios (base case scenario, high-energy intensity, and transformation, state-growth scenarios) in order to get a glimpse of future patterns and assess the likely impacts of energy policies. The application of DSS for Uttara Kananda district energy planning focuses on renewable resources that could be harnessed for energy, land use database, sectorwise energy demand database and optimal allocation of energy resources for various tasks, and then explore the energy use consequences of alternative scenarios, such as, base case scenarios, high-energy intensity and improved end use efficiency options. Linear programming formulation for optimum allocation based on the cost minimisation objective shows that there is substantial savings of about 19.19% in energy and 36.24% cost reduction in overall energy system. Cost per unit (kWh) of energy with optimal allocation of energy is Rs. 0.31/kWh (as against Rs. 0.39/kWh without optimisation). Optimisation carried out with the objective of maximisation of efficiency of 'ijk' combination for all combinations shows energy saving of 19.98% and cost of energy as Rs. 0.34/kWh. The scenario analyses reveal that relatively vigorous growth in energy demand in Uttara Kannada district can be accomplished without exceeding available resources.Integrated energy planning Decentralised energy plans Decision support system (DSS) Energy efficiency Optimisation Base case scenario Transformation scenario

Spatial mapping of renewable energy potential

An energy resource that is renewed by nature and whose supply is not affected by the rate of consumption is often termed as renewable energy. The need to search for renewable, alternate and non-polluting sources of energy assumes top priority for self-reliance in the regional energy supply. This demands an estimation of available energy resources spatially to evolve better management strategies for ensuring sustainability of resources. The spatial mapping of availability and demand of energy resources would help in the integrated regional energy planning through an appropriate energy supply-demand matching. This paper discusses the application of Geographical Information System (GIS) to map the renewable energy potential talukwise in Karnataka State, India. Taluk is an administrative division in the federal set-up in India to implement developmental programmes like dissemination of biogas, improved stoves, etc. Hence, this paper focuses talukwise mapping of renewable energy (solar, wind, bioenergy and small hydroenergy) potential for Karnataka using GIS. GIS helps in spatial and temporal analyses of the resources and demand and also aids as Decision Support System while implementing location-specific renewable energy technologies. Regions suitable for tapping solar energy are mapped based on global solar radiation data, which provides a picture of the potential. Coastal taluks in Uttara Kannada have higher global solar radiation during summer (6.31 kWh/m2), monsoon (4.16 kWh/m2) and winter (5.48 kWh/m2). Mapping of regions suitable for tapping wind energy has been done based on wind velocity data, and it shows that Chikkodi taluk, Belgaum district, has higher potential during summer (6.06 m/s), monsoon (8.27 m/s) and winter (5.19 m/s). Mysore district has the maximum number of small hydropower plants with a capacity of 36 MW. Talukwise computation of bioenergy availability from agricultural residue, forest, horticulture, plantation and livestock indicates that Channagiri taluk in Shimoga district yields maximum bioenergy. The bioenergy status analysis shows that Siddapur taluk in Uttara Kannada district has the highest bioenergy status of 2.004 (ratio of bioresource availability and demand).Renewable energy Spatial analysis Solar energy Wind energy Hydroenergy Bioenergy Energy demand Energy potential Bioenergy status GIS

Status and future transition of rapid urbanizing landscape in central Western Ghats - CA based approach

The land use changes in forested landscape are highly complex and dynamic, affected by the natural, socio-economic, cultural, political and other factors. The remote sensing (RS) and geographical information system (GIS) techniques coupled with multi-criteria evaluation functions such as Markov-cellular automata (CA–Markov) model helps in analysing intensity, extent and future forecasting of human activities affecting the terrestrial biosphere. Karwar taluk of Central Western Ghats in Karnataka state, India has seen rapid transitions in its forest cover due to various anthropogenic activities, primarily driven by major industrial activities. A study based on Landsat and IRS derived data along with CA–Markov method has helped in characterizing the patterns and trends of land use changes over a period of 2004–2013, expected transitions was predicted for a set of scenarios through 2013-2022. The analysis reveals the loss of pristine forest cover from 75.51% to 67.36% (1973 to 2013) and increase in agriculture land as well as built-up area of 8.65% (2013), causing impact on local flora and fauna. The other factors driving these changes are the aggregated level of demand for land, local and regional effects of land use activities such as deforestation, improper practices in expansion of agriculture and infrastructure development, deteriorating natural resources availability. The spatio temporal models helped in visualizing on-going changes apart from prediction of likely changes. The CA-Markov based analysis provides us insights into the localized changes impacting these regions and can be useful in developing appropriate mitigation management approaches based on the modelled future impacts. This necessitates immediate measures for minimizing the future impacts

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Not AvailableThe functional properties of fish meat depend mainly on characteristics of myofibrillar proteins (Coll et al., 1997) which in turn depends on their composition, structure and their interaction with other food components (Colmenero and Borderias, 1993). Study of functional property of myofibrillar proteins is important in determining the quality of the product (Roura and Crupkin, 1995). The native characteristics of proteins in fish muscles can be better understood when investigated when proteins are extracted from fish in prime condition. Further, protein being hydrophilic in nature, tissue proteins usually exists in native characteristics in tune with the aqueous environment as shown by low surface hydrophobicity, low SH groups etc. in order to exhibit maximum physiological function. The variation in native characteristic is related to both intrinsic and extrinsic factors affecting the characteristics of protein in vivo and its interaction with other tissue components.Not Availabl

Hotspots of solar potential in India

Solar hotspots are the regions characterized by an exceptional solar power potential suitable for decentralized commercial exploitation of energy. Identification of solar hotspots in a vast geographical expanse with dense habitations helps to meet escalating power demand in a decentralized, efficient and sustainable manner. This communication focuses on the assessment of resource potential with variability in India derived from high resolution satellite derived insolation data. Data analysis reveals that nearly 58% of the geographical area potentially represent the solar hotspots in the country with more than 5 kWh/m2/day of annual average Global insolation. A techno-economic analysis of the solar power technologies and a prospective minimal utilization of the land available within these solar hotspots demonstrate their immense power generation as well as emission reduction potential. The study evaluates the progress made in solar power generation in the country especially with the inception of an ambitious National Solar Mission (NSM) also termed as 'Solar India'. The organizational aspects of solar power generation with focus on existing policy elements are also addressed so as to probe the actual potential of the identified solar hotspots in meeting the NSM targets and beyond.India Solar hotspots Solar resource potential National Solar Mission Solar power generation

Modelling urban dynamics in rapidly urbanising Indian cities

Metropolitan cities in India are emerging as major economic hubs with an unprecedented land use changes and decline of environmental resources. Globalisation and consequent relaxations of Indian markets to global players has given impetus to rapid urbanisation process. Urbanisation being irreversible and rapid coupled with fast growth of population during the last century, contributed to serious ecological and environmental consequences. This necessitates monitoring and advance visualisation of spatial patterns of landscape dynamics for evolving appropriate management strategies towards sustainable development approaches. This study visualises the growth of Indian mega cities Delhi, Mumbai, Pune, Chennai and Coimbatore, through Cellular Automata Markov model considering the influence of agent(s) of urban growth through soft computing techniques. CA Markov model is considered to be one of most effective algorithm to visualise the growth of urban spatial structures. Prediction of growth using agent based modelling considering the spatial patterns of urbanisation during the past four decades has provided insights to the urban dynamics. The industrial, infrastructural, socio-economic factors significantly influence the urban growth compared to the biophysical factors. Visualisation of urban growth suggest agents driven growth in the cities and its surroundings with large land use transformations in urban corridors and upcoming Industrial and ear marked developmental zones. Integrating local agents of urban growth help in identifying specific regions of intense growth, likely challenges and provide opportunities for evolving appropriate management strategies towards sustainable cities during the 21st century. Keywords: Urban pattern, Modelling, AHP, Cellular Automata, Agent based modellin