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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 1.44×10 7 t, followed by 1.32 ×10 7 t from CBEF, 1.31 ×10 7 t from NCBEF, 1.25 ×10 7 t from MB and 1.16 ×10 7 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.

& Key message A generalized algebraic difference approach (GADA) developed in this study improved the estimation of aboveground biomass dynamics of Cunninghamia lanceolata (Lamb.) Hook and Castanopsis sclerophylla (Lindl.) Schott forests. This could significantly improve the fieldwork efficiency for dynamic biomass estimation without repeated measurements. & Context The estimation of biomass growth dynamics and stocks is a fundamental requirement for evaluating both the capability and potential of forest carbon sequestration. However, the biomass dynamics of Cunninghamia lanceolata and Castanopsis sclerophylla using the generalized algebraic difference approach (GADA) model has not been made to date. & Aims This study aimed to quantify aboveground biomass (AGB, including stem, branch and leaf biomass) dynamics and AGB increment in C. lanceolata and C. sclerophylla forests by combining a GADA for diameter prediction with allometric biomass models. & Methods A total of 12 plots for a C. lanceolata plantation and 11 plots for a C. sclerophylla forest were selected randomly from a 100 m × 100 m systematic grid placed over the study area. GADA model was developed based on tree ring data for each stand. & Results GADA models performed well for diameter prediction and successfully predicted AGB dynamics for both stands. The mean AGB of the C. lanceolata stand ranged from 69.4 ± 7.7 Mg ha −1 in 2010 to 102.5 ± 11.4 Mg ha −1 in 2013, compared to 136.9 ± 7.0 Mg ha −1 in 2010 to 154.8 ± 8.0 Mg ha −1 in 2013 for C. sclerophylla. The stem was the main component of AGB stocks and production. Significantly higher production efficiency (stem production/leaf area index) and AGB increment was observed for C. lancolata compared to C. sclerophylla.

Background: Forest ecosystem functioning is strongly influenced by the absorption of photosynthetically active radiation (APAR), and therefore, accurate predictions of APAR are critical for many process-based forest growth models. The Lambert-Beer law can be applied to estimate APAR for simple homogeneous canopies composed of one layer, one species, and no canopy gaps. However, the vertical and horizontal structure of forest canopies is rarely homogeneous. Detailed tree-level models can account for this heterogeneity but these often have high input and computational demands and work on finer temporal and spatial resolutions than required by stand-level growth models. The aim of this study was to test a stand-level light absorption model that can estimate APAR by individual species in mixed-species and multi-layered stands with any degree of canopy openness including open-grown trees to closed canopies. Methods: The stand-level model was compared with a detailed tree-level model that has already been tested in mixed-species stands using empirical data. Both models were parameterised for five different forests, including a wide range of species compositions, species proportions, stand densities, crown architectures and canopy structures.

Background: Forest ecosystem functioning is strongly influenced by the absorption of photosynthetically active radiation (APAR), and therefore, accurate predictions of APAR are critical for many process-based forest growth models. The Lambert-Beer law can be applied to estimate APAR for simple homogeneous canopies composed of one layer, one species, and no canopy gaps. However, the vertical and horizontal structure of forest canopies is rarely homogeneous. Detailed tree-level models can account for this heterogeneity but these often have high input and computational demands and work on finer temporal and spatial resolutions than required by stand-level growth models. The aim of this study was to test a stand-level light absorption model that can estimate APAR by individual species in mixed-species and multi-layered stands with any degree of canopy openness including open-grown trees to closed canopies. Methods: The stand-level model was compared with a detailed tree-level model that has already been tested in mixed-species stands using empirical data. Both models were parameterised for five different forests, including a wide range of species compositions, species proportions, stand densities, crown architectures and canopy structures.