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Spatial and temporal quantification of forest residue volumes and delivered costs

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https://ir.library.oregonstate.edu/concern/articles/12579t714

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Abstract
  • Growing demand for bioenergy, biofuels, and bioproducts has increased interests in the utilization of biomass residues from forest treatments as feedstock. In areas with limited history of industrial biomass utilization, uncertainties in the quantity, distribution, and cost of biomass production and logistics can hinder the development of new bio-based industries. This paper introduces a new methodology to quantify and spatially describe delivered feedstock volumes and costs across landscapes of arbitrary size in ways that characterize operational and annual management decision-making. Using National Agricultural Imagery Program (NAIP) imagery, the forest is segmented into operational-level treatment units. A remote sensing model based on NAIP imagery and Forest Inventory and Analysis plot data are used to attribute treatment units with stand-level estimates of basal area, tree density, aboveground biomass, and quadratic mean diameter. These methods are applied to a study site in southwestern Colorado to assess the quantity and distribution of treatment residue for use in bioenergy production. Results from the case study demonstrate how this generalized approach can be used in the analysis and decision-making process when establishing new bioenergy industries that use forest residue as feedstock.
  • Keywords: bioenergy, stand characteristics, treatment residue, stand delineation, forest biomass
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  • Wells, L. A., Chung, W., Anderson, N. M., & Hogland, J. S. (2016). Spatial and temporal quantification of forest residue volumes and delivered costs. Canadian Journal of Forest Research, 46(6), 832-843. doi:10.1139/cjfr-2015-0451
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  • 46
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  • 6
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  • This research was supported by the Agriculture and Food Research Initiative, Biomass Research and Development Initiative (Competitive Grant No. 2011-10006-30357) from the USDA National Institute of Food and Agriculture and a USFS Rocky Mountain Research Station Grant ( No. 10-JV-11221636-282) with significant cost match and in-kind support from the University of Montana and Oregon State University.
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