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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Biomass and Bioenerg...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Biomass and Bioenergy
Article . 2008 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Using spatial information technologies to select sites for biomass power plants: A case study in Guangdong Province, China

Authors: Xun Shi; Fang Wang; Haiming Jin; Xiaohao Zhang; Xia Li; Andrew J. Elmore; Nathaniel J. Gorence;

Using spatial information technologies to select sites for biomass power plants: A case study in Guangdong Province, China

Abstract

Biomass is distributed over extensive areas. Therefore, transportation cost is a critical factor in planning new biomass power plants. This paper presents a case study of using remote sensing and geographical information systems (GIS) to evaluate the feasibility of setting up new biomass power plants and optimizing the locations of plants in Guangdong, China. In this study, the biologically available biomass was estimated from MODIS/Terra remote sensing data. The amount of biomass that is usable for energy production was then derived using a model incorporating factors including vegetation type, ecological retaining, economical competition, and harvest cost. GIS was employed to define the supply area of each candidate site based on transportation distance along roads. The amount of usable biomass within the supply area was calculated and optimal sites were identified accordingly. This study presents a procedural framework for taking advantage of spatial information technologies to achieve more scientific planning in bioenergy power plant construction.

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