Inventory-based estimation of forest biomass in Shitai County, China: A comparison of five methods

Authors

  • X. Tang Department of Forest Inventory and Remote Sensing, Burckhardt Institute, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
  • L. Fehrmann Department of Forest Inventory and Remote Sensing, Georg-August-Universität Göttingen, Büsgenweg 5, 37077 Göttingen, Germany
  • F. Guan Key laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, No.8, Futong Dongdajie, Wangjing, Chaoyang District, Beijing 100102, P. R. China
  • D. I. Forrester Department of Silviculture, Faculty of Environment and Natural Resources, Freiburg University, Tennenbacherstr. 4, 79108 Freiburg, Germany
  • R. Guisasola Department of Silviculture, Faculty of Environment and Natural Resources, Freiburg University, Tennenbacherstr. 4, 79108 Freiburg, Germany
  • C. Kleinn Department of Forest Inventory and Remote Sensing, Georg-August-Universität Göttingen, Büsgenweg 5, 37077 Göttingen, Germany

DOI:

https://doi.org/10.15287/afr.2016.574

Keywords:

biomass expansion factors (BEFs), forest type, forest inventory, carbon market

Abstract

Several comparative studies have reported that there can be great discrepancies between different methods used to estimate forest biomass. With the development of carbon markets, an accurate estimation at the regional scale (i.e. county level) is becoming increasingly important for local government. In this study, we applied five methodologies [continuous biomass expansion factor (CBEF) approach, mean biomass density (MB) approach, mean biomass expansion factor (MBEF) approach, national continuous biomass expansion factors (NCBEF) proposed by Fang et al (2002), standard IPCC approach] to estimate the total biomass for Shitai County, China. The CBEF is generally considered to provide the most realistic estimates in term of regional biomass because CBEF reflects the change of BEF to stand density, stand age and site conditions. The forests of the whole county were divided into four forest types, namely Chinese fir plantations (CF), hardwood broadleaved forests (HB), softwood–broadleaved forests (SB) and mason pine forests (MP) according to the local forest management inventory of 2004. Generally, the MBEF approach overestimated forest biomass while the IPCC approach underestimated forest biomass for all forest types when CBEF derived biomass was used as a control. The MB approach provided the most similar biomass estimates for all forest types and could be an alternative approach when a CBEF equation is lacking in the study area. The total biomass derived from MBEF was highest at 1.44×107 t, followed by 1.32 ×107 t from CBEF, 1.31 ×107 t from NCBEF, 1.25 ×107 t from MB and 1.16 ×107 t from IPCC. Our results facilitate method selection for regional forest biomass estimation and provide statistical evidence for local government planning to enter the potential carbon market.

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Published

2016-04-11

Issue

Vol. 59 No. 2 (2016)

Section

Research article

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