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Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations
Authors:Huiquan Bi  Yushan Long  John Turner  Yuancai Lei  Peter Snowdon  Yun Li  Richard Harper  Ayalsew Zerihun  Fabiano Ximenes
Institution:1. Forest Science Centre, Science and Research Division, New South Wales Department of Primary Industries, PO Box 100, Beecroft, NSW 2119, Australia;2. School of Forest and Ecosystem Science, University of Melbourne, Australia;3. Forests NSW, PO Box 100, Beecroft, NSW 2119, Australia;4. Forsci Pty Ltd, Suite 4.05 Delhi Corporate, 32 Delhi Road, North Ryde, NSW 2113, Australia;5. Institute of Forest Resources Information Techniques, CAF, Beijing 100091, China;6. CSIRO Forest Biosciences, PO Box E4008, Kingston, ACT 2604, Australia;g School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China;h Forest Products Commission, Locked Bag 888, Perth Business Centre, Perth, WA 6849, Australia;i School of Agriculture and Environment, Curtin University of Technology, PMB 1, Margaret River, WA 6849, Australia
Abstract:A general and two country-specific systems of additive equations were developed to predict aboveground biomass of Pinus radiata plantations from stand variables that are routinely measured in inventory plots and predicted by conventional growth and yield models. The data for this work consisted of 319 plot-based biomass estimates that were derived from individual tree biomass equations developed in situ. These plot-based biomass estimates were compiled from studies reported in the forestry and ecological literature since 1960 and also from personal communications. They represent more than 60 sites worldwide with a majority in Australia and New Zealand. The systems of additive biomass equations developed from these data provide an alternative and addition to the current methods of estimating the aboveground biomass of P. radiata plantations. They also provide a direct linkage between forest inventory measures, outputs from conventional growth and yield models and biomass and carbon stock estimates at the same spatial scale. This direct linkage provides a new basis for scaling to a remote sensing image from which biomass and carbon stocks across the landscape can be mapped. Comparisons of prediction accuracies between this approach and other methods such as scaling up from individual tree biomass estimates and biomass expansion factors highlighted considerable methodological differences in the estimates of aboveground biomass and associated uncertainties over a range of stand age and conditions. These differences should be carefully evaluated before adopting a particular method to estimate aboveground biomass and carbon stocks of P. radiata plantations at a local, regional or national scale.
Keywords:Pinus radiata  Aboveground stand biomass  Additive equations  Skedastic  Functions
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