GIS mapping of rice straw residue for bioenergy purpose in a rural area of Assam, India

Agricultural residues are a promising source of biomass energy. However, agricultural residues are seasonally available and loosely distributed over large geographical areas and hence require spatio-temporal assessment. Satellite image is a handy input for such assessment and high resolution image could increase the preciseness of estimation. In the present study, rice cropland is mapped using high resolution WorldView-2 satellite image in a rural area of Assam, India. The rice cropland map in combination with agricultural statistics is then analyzed in GIS in order to assess rice straw availability for potential bioenergy generation. About 54% land of study area belongs to rice cropland, which can contribute 5360 tonnes surplus rice straw per annum (equivalent to 83,296 GJ). Potential electric power capacity from the surplus rice straw in the study area is 523.50 kW. However, at individual village level the potential varies from 4.45 kW to 28.69 kW. Considering the power crisis in India, the findings of this work are expected to assist policy makers and biomass energy developers in decision making process. Particularly, this paper generated information on village level rice straw residue availability and subsequently potential electric power capacity. Such information is limited in the India expect for few states.

[1]  Christoph Menke,et al.  Rice straw as a renewable energy source in India, Thailand, and the Philippines: Overall potential and limitations for energy contribution and greenhouse gas mitigation , 2009 .

[2]  D. Baruah,et al.  Rice straw residue biomass potential for decentralized electricity generation: A GIS based study in Lakhimpur district of Assam, India , 2011 .

[3]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[4]  F. Urban,et al.  Energy for rural India , 2009 .

[5]  D. Baruah,et al.  Crop residue biomass for decentralized electrical power generation in rural areas (part 1): Investigation of spatial availability , 2011 .

[6]  K. Hiscock,et al.  A GIS based assessment of bioenergy potential in England within existing energy systems , 2013 .

[7]  J. Clancy,et al.  Impacts of Electricity Access to Rural Enterprises in Bolivia, Tanzania and Vietnam , 2010 .

[8]  Katalin Bódis,et al.  The possible contribution of agricultural crop residues to renewable energy targets in Europe: A spatially explicit study , 2013 .

[9]  Subhes C. Bhattacharyya,et al.  Access to energy services by the poor in India: Current situation and need for alternative strategies , 2006 .

[10]  Raffaele Spinelli,et al.  A GIS decision support system for regional forest management to assess biomass availability for renewable energy production , 2012, Environ. Model. Softw..

[11]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[12]  Paul V. Bolstad,et al.  Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products , 1999 .

[13]  G. Esposito,et al.  Electrical energy production and operational strategies from a farm-scale anaerobic batch reactor loaded with rice straw and piggery wastewater , 2014 .

[14]  Vida N. Sharifi,et al.  Mathematical modelling of straw combustion in a 38 MWe power plant furnace and effect of operating conditions , 2007 .

[15]  Reza S. Abhari,et al.  Large scale technical and economical assessment of wind energy potential with a GIS tool: Case study Iowa $ , 2012 .

[16]  Shabbir H. Gheewala,et al.  Potential alternatives of heat and power technology application using rice straw in Thailand , 2010 .

[17]  P. Crutzen,et al.  Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles , 1990, Science.

[18]  Peter McKendry,et al.  Energy production from biomass (Part 2): Conversion technologies. , 2002, Bioresource technology.

[19]  Jorge Hilbert,et al.  Spatial analysis of the potential crops for the production of biofuels in Argentina. , 2008 .

[20]  S. K. Jain,et al.  Assessment of hydropower potential using spatial technology and SWAT modelling in the Mat River, southern Mizoram, India , 2015 .

[21]  Nikos Koutsias,et al.  Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site , 2008 .

[22]  Juan Miguel Sánchez-Lozano,et al.  Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain , 2013 .

[23]  Robert M. Weber,et al.  Riparian Vegetation Mapping and Image Processing Techniques, Hopi Indian Reservation, Arizona , 2001 .

[24]  K. Karimi,et al.  Improvement of acetone, butanol and ethanol production from rice straw by acid and alkaline pretreatments , 2013 .

[25]  Daniel K. Y. Tan,et al.  Potential Suitability and Viability of Selected Biodiesel Crops in Australian Marginal Agricultural Lands Under Current and Future Climates , 2011, BioEnergy Research.

[26]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[27]  T. Nussbaumer.  Combustion and Co-combustion of Biomass: Fundamentals, Technologies, and Primary Measures for Emission Reduction† , 2003 .

[28]  A. Demirbas,et al.  Biomass resource facilities and biomass conversion processing for fuels and chemicals , 2001 .

[29]  Yasuhiro Fujimitsu,et al.  Integration of Geothermal Exploration Data and Numerical Simulation Data Using GIS in a Hot Spring Area , 2013 .

[30]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[31]  Christoph Menke,et al.  Air pollutant emissions from rice straw open field burning in India, Thailand and the Philippines. , 2009, Environmental pollution.

[32]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[33]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[34]  H. Neue.  Methane emission from rice fields , 1993 .

[35]  Alexandre Carleer,et al.  Exploitation of Very High Resolution Satellite Data for Tree Species Identification , 2004 .

[36]  Horst Bischof,et al.  Multispectral classification of Landsat-images using neural networks , 1992, IEEE Trans. Geosci. Remote. Sens..