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Remote Sensing and GIS Techniques for the Assessment of Biofuel and Biomass Energy Resources

  • Lalit KumarEmail author
  • Anirudh Singh
Conference paper
Part of the Climate Change Management book series (CCM)

Abstract

The Pacific Island Countries (PICs) are faced with energy challenges arising from the lack of availability of fossil fuel sources in the region. Renewable energy has been identified as a primary means by which these challenges could be met. The successful utilization of renewable energy resources of the region will, however, depend on several factors. Among these are the availability of the relevant resources, and the political and legal framework, human capacity, and institutional mechanisms required to develop and implement renewable energy projects. Biomass and biofuels are two important resources available to many of the PICs. However, before these forms of renewable energy can be used, a necessary first step is the assessment of the availability of these resources, and the land area required to produce them. Remote sensing and GIS are two important techniques that can be employed for this purpose. In the technique of remote sensing, satellite imagery is used to quantitatively assess the biomass cover and available land area over large areas of a country. The information thus collected is conveniently stored in GIS systems which can be used for decision-making. This paper begins by showing why there is a need for a quantitative assessment of the biomass and biofuel resource potentials of the region before decisions about the use of such resources can be made. The techniques of remote sensing and GIS are then introduced, and examples of their potential application in the assessment of biomass and biofuel resources provided. The need for a biofuel resource assessment for Fiji is then considered in detail. Finally, recommendations are made for a biomass and biofuel assessment strategy for the Pacific region.

Keywords

Remote sensing GIS Pacific island countries (PICs) Resource assessment Biofuel resources Biomass resources 

References

  1. Achten WMJ, Verchot L, Franken YJ, Mathijs E, Singh VP, Aerts R, Muys B (2008) Jatropha bio-diesel production and use. Biomass Bioenergy 32(12):1063–1084CrossRefGoogle Scholar
  2. Azam MM, Waris A, Nahar NM (2005) Prospects and potential of fatty acid methyl esters of some non-traditional seed oils for use as biodiesel in India. Biomass Bioenergy 29(4):293–302CrossRefGoogle Scholar
  3. Brown S (1997) Estimating biomass and biomass change of tropical forests, a primer (UN-FAO forestry paper 134). UN-FAO, RomeGoogle Scholar
  4. Burton AJ, Pregitzer KS et al (1991) Leaf area and foliar biomass relationships in northern hardwood forests located along an 800 km acid deposition gradient. For Sci 37:1041–1059Google Scholar
  5. De Jong SM, Pebesma EJ et al (2003a) Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment. Int J Remote Sens 24(7):1505–1520CrossRefGoogle Scholar
  6. De Jong SM, Pebesma EJ et al (2003b) Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment. Int J Remote Sens 24(7):1505–1520CrossRefGoogle Scholar
  7. Drake JB, Dubayah RO et al (2002) Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sens Environ 81(2–3):378–392CrossRefGoogle Scholar
  8. Drake JB, Knox RG et al (2003) Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob Ecol Biogeogr 12(2):147–159CrossRefGoogle Scholar
  9. Fearnside P (1997) Greenhouse gases from deforestation in Brazilian Amazonia: net committed emissions. Clim Change 35:321–360CrossRefGoogle Scholar
  10. Francis G, Edinger R, Becker K (2005) A concept for simultaneous wasteland reclamation, fuel production, and socio-economic development in degraded areas in India: need, potential and perspectives of jatropha plantations. Nat Resour Forum 29(1):12–24CrossRefGoogle Scholar
  11. Houghton RA, Lawrence KT et al (2001) The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Glob Change Biol 7(7):731–746CrossRefGoogle Scholar
  12. Key Statistics (2011a) Fiji Bureau of statistics. p 36, June 2011Google Scholar
  13. Key Statistics (2011b) Fiji Bureau of statistics. p 34, June 2011Google Scholar
  14. Key Statistics (2011c) Fiji Bureau of statistics. p 33, June 2011Google Scholar
  15. Krishna I et al (2009) Potential for liquid biofuels in Fiji. SOPAC miscellaneous report 677. Available at: www.sopac.int/tiki-download_file.php?fileid=1885
  16. Landsberg JJ, Waring RH (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manag 95(3):209–228CrossRefGoogle Scholar
  17. Lefsky MA, Cohen WB et al (2001) Lidar remote sensing of aboveground biomass in three biomes. Int Arch Photogram, Remote Sens Spat Inf Sci 34:155–160Google Scholar
  18. Lefsky MA, Harding DJ et al (2005) Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32(22):1–17Google Scholar
  19. Lehtonen A, Mäkipää R et al (2004) Biomass expansion factors (BEFs) for scots pine, Norway spruce and birch according to stand age for boreal forests. For Ecol Manag 188(1–3):211–224CrossRefGoogle Scholar
  20. Li Z, Lin B, Zhao X, Sagisaka M, Shibazaki R (2010) System approach for evaluating the potential yield and plantation of Jatropha curcus L. on a global scale. Environ Sci Technol 44(6):2204–2209CrossRefGoogle Scholar
  21. Ministry of Natural Resources, and Environment (2011) Available at: www.mnre.gov.ws
  22. Nelson RF, Kimes DS et al (2000) Secondary forest age and tropical forest biomass estimation using thematic mapper imagery. Bioscience 50(5):419–431CrossRefGoogle Scholar
  23. Nelson R, Krabill W et al (1988) Estimating forest biomass and volume using airborne laser data. Remote Sens Environ 24:247–267CrossRefGoogle Scholar
  24. Nilsson M (1996) Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56(1):1–7CrossRefGoogle Scholar
  25. Olson JS, Watts JA et al (1983) Carbon in live vegetation of major world ecosystems, TR004U.S. Department of Energy, Washington, DCGoogle Scholar
  26. Popescu SC (2007) Estimating biomass of individual pine trees using airborne lidar. Biomass Bioenergy 31(9):646–655CrossRefGoogle Scholar
  27. Prueksakorn K, Gheewala SH (2008) Full chain energy analysis of biodiesel from Jatropha curcas L. in Thailand. Environ Sci Technol 42(9):3388–3393CrossRefGoogle Scholar
  28. Report on Fiji National Agricultural Census (2009) Department of agriculture. Economic Planning and Statistics Division, Suva Fiji. Available at: www.agroculture.org.fj
  29. Roy P, Ravan S (1996) Biomass estimation using satellite remote sensing data—a investigation on possible approaches for natural forest. J Biosci 21(4):535–561CrossRefGoogle Scholar
  30. Singh P (2011) Biofuel developments in Fiji. Presented at: national workshop on energy planning and policy. Southern Cross Hotel, Suva, Fiji, 19–21 Oct 2011Google Scholar
  31. Strahler AH, Woodcock CE et al (1986) On the nature of models in remote sensing. Remote Sens Environ 20(2):121–139CrossRefGoogle Scholar
  32. TERM (2011) Available at: www.tonga-energy.to
  33. Tiwari AK, Kumar A, Raheman H (2007) Biodiesel production from Jatropha (Jatropha curcas) with high free fatty acids: an optimized process. Biomass Bioenergy 31(8):569–575CrossRefGoogle Scholar
  34. Zheng D, Rademacher J et al (2004) Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens Environ 93(3):402–411CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Environmental and Rural ScienceUniversity of New EnglandArmidaleAustralia
  2. 2.University of the South PacificSuvaFiji

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