PDF (4.5 MB)
Collect
Submit Manuscript
Show Outline
Outline
Abstract
Keywords
References
Show full outline
Hide outline
Publishing Language: Chinese

Natural forest growth modelling for site quality evaluation

Inventory & Planning Institute, National Forestry and Grassland Administration, Beijing 100714, China
Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Show Author Information

Abstract

Objective

A approach for natural forest site quality evaluation based on site productive potential has been proposed by Fu, et al. This approach could be used to evaluate the productive of different site types quantitatively by combining of environmental and stand variables in theory. This study mainly focusing on the forest growth models in this approach by using stand basal area in the whole Jilin province, detailed modelling approaches of growth models, such as model selection, model parameterization, parameter estimation and model evaluation, have been proposed.

Method

The computational formula for the current annual increment of basal area in the two situations with the basal area models containing age directly or indirectly had been derived using mathematical theory by assuming identity trees growth in stand. A criterion for testing the relationship of basal area current annual increment and stand density site whether being the monotonic function was proposed. The effects of different categorical variables on basal area were descripted by dummy variable approach, namely model parameterization. The parameters in the basal area growth model with parameterization were estimated by modified least square method. Stand basal area models were developed based on the four continuous measured data from 3 634 permanent plots with size of 0.06 hm2 in Jilin province.

Result

The relationship of basal area annual increment and stand density site whether being the monotonic function was tested effectively using the approach proposed in this study, which would provide an important criterion for model selection. Considering parameterization in the developed model not only explained the differences among different levels in variables effectively but also improved the precision of the developed models. The parameters in the dummy models could be estimated effectively by modified least square approach.

Conclusion

The approach for developing natural forest growth model proposed in this study could be used as a technical support for Fu et al. natural site quality evaluation approach.

CLC number: S711 Document code: A Article ID: 1673-923X(2024)10-0017-10

References

[1]
WU F. Summarize of forest site classification and quality appraisement[J]. Forestry Science and Technology Information, 2010, 42(1):12-14.
[2]
GAO R N, XIE Y S, LEI X D, et al. Study on prediction of natural forest productivity based on random forest model[J]. Journal of Central South University of Forestry & Technology, 2019, 39(4):39-46.
[3]
TANG S Z, LIU S R. Conservation and sustainability of natural forests in China[J]. Review of China Agricultural Science and Technology, 2000(1):42-46.
[4]
GONG Z S. Site quality evaluation and growth models of Phoebe zhennan secondary stand in Hunan province[D]. Changsha: Central South University of Forestry& Technology, 2021.
[5]
HUANG S M, TITUS S J. An index of site productivity for uneven-aged or mixed-species stands[J]. Canadian Journal Forest Research, 2011, 23(3):558-562.
[6]
PALAHÍ M, PUKKALA T, KASIMIADIS D, et al. Modelling site quality and individual-tree growth in pure and mixed Pinus brutia stands in north-east Greece[J]. Annals of Forest Science, 2008, 65(5):501P1-501P14.
[7]
BERRILL J, O'HARA K L. Estimating site productivity in irregular stand structures by indexing the basal area or volume increment of the dominant species[J]. Canadian Journal Forest Research, 2014, 44(1):92-100.
[8]
FU L Y, SHARMA R P, ZHU G, et al. A basal area Increment-Based approach of site productivity evaluation for multi-aged and mixed forests[J]. Forests, 2017, 8(4):1-18.
[9]
TANG S Z, LI Y, FU L Y. Statistical foundation for biomathematical models[M]. 2nd ed. Beijing: Higher Education Press, 2015.
[10]
TANG S Z, LANG K J, LI H K. Computation for statistics and biomathematical models: ForStat course[M]. Beijing: Science Press, 2009.
[11]
YIN H Y, LI H K. Estimation methods of forest biomass with different levels[J]. Journal of Northwest Forestry University, 2016, 31(2):38-44.
[12]
ZHU G Y. Site quality classification and site productivity evaluation for forest lands in Jilin province[R]. Beijing: Chinese Academy of Forestry, 2017.
[13]
TANG S Z. Integrated stand growth model of massion pine in Daqingshan moutains, Guangxi province[J]. Journal of Forest Research, 1991, 4(Supp.1):8-13.
[14]
FU L Y, ZHANG H R, TANG S Z. Dominant height for Chinese fir plantation using nonlinear mixed effects model based on linearization algorithm[J]. Scientia Silvae Sinicae, 2012, 48(7):66-71.
[15]
FU L Y, SUN H, ZHANG H R, et al. Effects of diameter at breast height on crown characteristics of Chinese Fir under different canopy density conditions[J]. Acta Ecologica Sinica, 2013, 33(8):2434-2443.
[16]
FU L Y, LEI Y C, ZENG W S. Comparison of several compatible biomass models and estimation approaches[J]. Scientia Silvae Sinicae, 2014, 50(6):42-54.
[17]
FU L Y, TANG S Z, ZHANG H R, et al. Generalized above-ground biomass equations for two main species in northeast China[J]. Acta Ecologica Sinica, 2015, 35(1):150-157.
[18]
GUO Z X, CAO C, LIU P. Construction of biomass models of Cunninghamia lanceolata plantation based on the continuous forest inventory in Guangdong[J]. Journal of Central South University of Forestry & Technology, 2022, 42(8):78-89.
[19]
ZHANG Y J, BORDERS B E. Using a system mixed-effects modeling method to estimate tree compartment biomass for intensively managed loblolly pines-an allometric approach[J]. Forest Ecology and Management, 2004, 194(1-3):145-157.
[20]
FEHRMANN L, LEHTONEN A, KLEINN C, TOMPPO R. Comparison of linear and mixed-effect regression models and a k-nearest neighbor approach for estimation of single-tree biomass[J]. Canadian Journal of Forest Research, 2008, 38(1):1-9.
[21]
FU L Y, ZENG W S, ZHANG H R, et al. Generic linear mixed-effects individual-tree biomass models for Pinus massoniana Lamb. in southern China[J]. Southern Forests-A Journal of Forest Science, 2014, 76(1):47-56.
[22]
FU L Y, ZHANG H R, LU J, et al. Multilevel nonlinear mixed-effect crown ratio models for individual trees of Mongolian oak (Quercus mongolica) in northeast China[J]. PLoS ONE, 2015, 10(8):e0133294.
[23]
DONG L H, ZHANG L J, LI F R. A three-step proportional weighting system of nonlinear biomass equations[J]. Forest Science, 2015, 61(1):35-45.
[24]
ZENG W. Using nonlinear mixed model and dummy variable model approaches to develop origin-based individual tree biomass equations[J]. Trees, 2015, 29(1):275-283.
[25]
YUAN Y X. Theory and methods of optimization[M]. Beijing: Science Press, 1997.
[26]
TANG S Z, MENG C H, MENG F R, et al. A growth and self-thinning model for pure even-age stands: theory and applications[J]. Forest Ecology and Management, 1994, 70(1-3):67-73.
Journal of Central South University of Forestry & Technology
Pages 17-26,35
Cite this article:
ZHANG Y, FU L. Natural forest growth modelling for site quality evaluation. Journal of Central South University of Forestry & Technology, 2024, 44(10): 17-26,35. https://doi.org/10.14067/j.cnki.1673-923x.2024.10.002
Metrics & Citations  
Article History
Copyright
Return