Additivity of stand basal area predictions in canopy stratifications for natural oak forests

Abstract Stand basal area is an important variable for evaluating forest productivity. In this study, four types of growth models were used for modeling the stand basal area of natural oak forests in Hunan Province, China. The results showed that the Schumacher model (Eq. (3) ) performed the best. Because of different canopy layers in natural oak secondary forests, it is important to develop stand basal area models considering canopy stratification. Three widely used canopy stratification methods were used for natural oak forests: the clustering analysis of tree height, classification scheme of the International Union of Forest Research Organizations (IUFRO) standard, and canopy competition cut-off height (CCH) method. According to the standards set forth in the National Standards of the People’s Republic of China (GB/T 26424-2010), the forests were divided into two layers: upper and lower. Among the three canopy stratification approaches, the IUFRO method was selected as the best. In addition, it should be noted that the sum of the basal area predictions at different layers can generate inconsistent results, as compared to predictions from the total stand basal area model. To solve the additivity of stand basal area predictions, the additive stand basal area growth model was established by nonlinear seemingly unrelated regression (NSUR). Comparing with the whole stand basal area growth model without stratification, the R2 values of basal area growth models with stratification increased by 4.78% (from 0.9277 to 0.9721). In addition, compared with equations estimated separately, the NSUR method increased R2 by 0.82% (from 0.9721 to 0.9801). The results reveal that the NSUR method can be applied to solve the additivity of stand basal area predictions at each layer and can improve model precision, which provides helpful insights into developing stand growth models in natural forests.

[1]  Richard J. Harper,et al.  Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations , 2010 .

[2]  B. Parresol.  Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons , 1999, Forest Science.

[3]  Annabel Porté,et al.  Modelling mixed forest growth: a review of models for forest management , 2002 .

[4]  D. Hollinger,et al.  Quantifying climate–growth relationships at the stand level in a mature mixed‐species conifer forest , 2018, Global change biology.

[5]  E. Valinger,et al.  Multi-layered Scots pine forests in boreal Sweden result from mass regeneration and size stratification , 2019, Forest Ecology and Management.

[6]  Bernard R. Parresol,et al.  Additivity of nonlinear biomass equations , 2001 .

[7]  F. Rodríguez,et al.  A reduced growth model based on stand basal area. A case for hybrid poplar plantations in northeast Spain , 2010 .

[8]  J. Pezzopane,et al.  Métodos de estratificação vertical em Floresta Estacional Semidecidual , 2013 .

[9]  G. Tonoli,et al.  Effect of natural weathering and accelerated artificial in physical, mechanical and colorimetric OSB panels. , 2017 .

[10]  Y. Lei,et al.  A Basal Area Increment-Based Approach of Site Productivity Evaluation for Multi-Aged and Mixed Forests , 2017 .

[11]  R. Everett,et al.  Defining and Quantifying Canopy Strata , 2008 .

[12]  P. O. Adesoye.  Canopy Layers Stratified Volume Equations for Pinus caribaea Stands in South West Nigeria using Linear Mixed Models , 2014 .

[13]  A. Welsh,et al.  Fundamental shifts of central hardwood forests under climate change , 2016 .

[14]  U. Diéguez-Aranda,et al.  Modelling stand basal area growth for radiata pine plantations in Northwestern Spain using the GADA , 2007, Annals of Forest Science.

[15]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[16]  P. Tomlinson,et al.  Tropical Trees and Forests: An Architectural Analysis , 1978 .

[17]  Quang V. Cao,et al.  Compatibility of stand basal area predictions based on forecast combination. , 2010 .

[18]  J. P. Skovsgaard,et al.  Forest site productivity : A review of spatial and temporal variability in natural site conditions , 2013 .

[19]  L. Volkova,et al.  Additive biomass equations based on complete weighing of sample trees for open eucalypt forest species in south-eastern Australia , 2015 .

[20]  T. B. Lynch,et al.  Estimating parameters for tree basal area growth with a system of equations and seemingly unrelated regressions , 2001 .

[21]  I. Newman.  Locating Strata in Tropical Rain Forests , 1954 .

[22]  Mathias Schardt,et al.  An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data - A case study in complex temperate forest stands , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[23]  P. Lohmander,et al.  Estimation of a basal area growth model for individual trees in uneven-aged Caspian mixed species forests , 2017, Journal of Forestry Research.

[24]  Michael L. Clutter,et al.  Additive Tree Biomass Equations for Midrotation Loblolly Pine Plantations , 2015 .

[25]  Aged Forests.  PERFECTING A STAND-DENSITY INDEX FOR EVEN- , 2010 .

[26]  Pamela Hall,et al.  Comparisons of structure among mixed dipterocarp forests of north-western Borneo , 1992 .

[27]  P. Baker,et al.  A quantitative technique for the identification of canopy stratification in tropical and temperate forests , 2000 .

[28]  K. Jõgiste.  Productivity of mixed stands of Norway spruce and birch affected by population dynamics: a model analysis , 1998 .

[29]  A. Kozak.  Methods for Ensuring Additivity of Biomass Components by Regression Analysis , 1970 .

[30]  I. Krams,et al.  Estimates of Tree Canopy Closure and Basal Area as Proxies for Tree Crown Volume at a Stand Scale , 2020 .

[31]  C. Lusk,et al.  Vertical structure and basal area development in second‐growth Nothofagus stands in Chile , 2003 .

[32]  Joseph Buongiorno,et al.  A multi-species, density-dependent matrix growth model to predict tree diversity and income in northern hardwood stands , 1996 .

[33]  L. Pienaar,et al.  Basal area prediction and projection equations for pine plantations , 1986 .

[34]  K. O’Hara,et al.  Stocking Control Procedures for Multiaged Ponderosa Pine Stands in the Inland Northwest , 2003 .

[35]  P. Richards,et al.  The Vegetation of Moraballi Creek, British Guiana: An Ecological Study of a Limited Area of Tropical Rain Forest. Part II , 1933 .

[36]  S. Huang,et al.  Incorporating correlated error structures into mixed forest growth models: prediction and inference implications , 2010 .

[37]  Helio Garcia Leite,et al.  Emprego de análise multivariada para estratificação vertical de florestas ineqüiâneas , 2003 .

[38]  A. Duan,et al.  Predicting total and component biomass of Chinese fir using a forecast combination method , 2017 .

[39]  Gregory P. Asner,et al.  Cover of tall trees best predicts California spotted owl habitat , 2017 .

[40]  J. Midgley,et al.  Competition among canopy trees in indigenous forests: An analysis of the ‘additive basal area’ phenomenon , 2002 .

[41]  Hans R. Zuuring,et al.  A method for quantifying vertical forest structure , 1998 .

[42]  AVALIAÇÃO DO DESEMPENHO DE METODOLOGIAS PARA ESTRATIFICAÇÃO VERTICAL EM FLORESTAS NATIVAS , 2018, Ciência Florestal.