Skip to main content

Advertisement

SpringerLink
Log in

Dynamic prediction of uneven-aged natural forest for yield of Pinus taiwanensis using joint modelling

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

In order to determine the stand age in the uneven-aged natural forest, a dynamic prediction model of stand volume and biomass was established in this study. In the model, the site quality grade was used as the dumb variable and the interval was used as the independent variable. In addition, the parameters of the model were estimated using immune evolutionary algorithm. The model was verified with the field data and the result revealed that the model had high accuracy. On this basis, the dynamic prediction model for forest stock was applied to evaluate the asset evaluation of uneven-aged natural forest and estimate carbon storage/sink potential of forest biomass. The selective logging period of the forest in the four plots was determined at the selective logging intensity of 40%. However, at the selective logging intensity of 40%, the forest ecological environment was suffered from the adverse effect to a certain extent from the perspective of scientific management, diversity of species, etc. Based on the comprehensive consideration of all the factors, it is recommended to set the selective cutting intensity in the range of 30 to 35%. The results can provide technical support for the application of selective logging income method in asset evaluation. Therefore, the results of this study have theoretical significance and practical application value in dynamic prediction of forest resources, asset evaluation and management, decision-making, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Cao, Q. V. (2014). Linking individual-tree and whole-stand models for forest growth and yield prediction. Forest Ecosystems, 1(1), 18.

    Article  Google Scholar 

  • Chen, P. L., & Liu, J. (2002). Operational skills of forest resources asset assessment (pp. 52–60). Beijing: China Forestry Press 94, 156, 158–159.

    Google Scholar 

  • Chen, D., Fang, K. Y., Li, Y. J., Dong, Z. P., Zhang, Y., & Zhou, F. F. (2016). Response of Pinus taiwanensis growth to climate changes at its southern limit of Daiyun Mountain, mainland China Fujian Province. Science China, 59(2), 328–336.

    Article  CAS  Google Scholar 

  • Chen, Y. L., Wu, B. G., Cui, Y., & Wei, Y. J. (2019). Research and application of prediction model of subcompartment volume for Larix principis-rupprechtii mayr. based on back propagation neural network. Journal of Agricultural Science and Technology, 21(7), 82–93.

    CAS  Google Scholar 

  • Dar, J. A., & Sundarapandian, S. (2015). Variation of biomass and carbon pools with forest type in temperate forests of Kashmir Himalaya, India. Environmental Monitoring and Assessment, 187(2), 55.

    Article  Google Scholar 

  • Giese, L. A. B., Aust, W. M., Kolka, R. K., & Trettin, C. C. (2003). Biomass and carbon pools of disturbed riparian forests. Forest Ecology and Management, 180(1–3), 0–508.

  • Jenkins, J. C., Chojnacky, D. C., Heath, L. S., & Birdsey, R. A.(2004). Comprehensive database of diameter-base biomass regressions for North American tree species. General "Technical Report NE-319, USDA Forest Service, Northeastern Research Station, Newtown Square,PA.

  • Jianshe, Y. (1998). Global attractivity in a discrete delay logistic model. Journal of Applied Mathematics (English Edition), 14(1), 80–85.

    Google Scholar 

  • Kattan, M. W., & Gerds, T. A. (2018). The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models. Diagnostic & Prognostic Research, 2(1), 7.

    Article  Google Scholar 

  • Kauffman, J. B., Steele, M. D., Cummings, D. L., & Jaramillo, V. J.(2003). Biomass dynamics associated with deforestation, fire, and, conversion to cattle pasture in a mexican tropical dry forest. Forest Ecology and Management, 176(1–3), 0–12.

  • Li, G., & Wang, X. (2018). Prediction accuracy measures for a nonlinear model and for right-censored time-to-event data. Journal of the American Statistical Association, 54(1), 23–30.

    Google Scholar 

  • Li, Z. J., Taylor, J. E., & Hou, C. L. (2016). An unusual Lophodermium species on needles of Pinus taiwanensis from China. Mycological Progress, 15(12), 1229–1237.

    Article  Google Scholar 

  • Li, Y. F., Ram, S., Guang, Y. Z., Hai, K. L., Ling, X. H., & Hong, G. (2017). A basal area increment-based approach of site productivity evaluation for multi-aged and mixed forests. Forests, 8(4), 1–18.

    Google Scholar 

  • Li, M., Hong, Y., Song, Y., & Zhang, X. (2018). Effect of controllable parameter synchronization on the ensemble average bit error rate of space-to-ground downlink chaos laser communication system. Optics Express, 26(3), 2954.

    Article  CAS  Google Scholar 

  • Liang, X. L., Li, Z. Y., & Wang, J. Y. (2013). Immune evolutionary predator-prey algorithm for optimization of transverse diffusion coefficient of two-dimensional water quality model. Environmental Engineering, 5, 136–138.

    Google Scholar 

  • Liang, J. S., Tong, X. F., & Yuan, Z. B. (2014). The circular seal identification method based on average relative error. Applied Mechanics and Materials, 513-517, 4338–4341.

    Article  Google Scholar 

  • Liang, X. J., Liang, W. H., Huang, K. S., Li, K. X., & Wu, J. Y. (2019). Application study on biomass regression model of Cinnamamun cassia plantation. Chinese Agricultural Science Bulletin, 35(35), 40–44.

    Google Scholar 

  • Lim, T. S., Loh, W. Y., & Shih, Y. S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3), 203–228.

    Article  Google Scholar 

  • Mahsa, J. T., Omid, B. H., & Hugo, A. L. (2019). Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview. Environmental Monitoring and Assessment, 191, 439.

    Article  Google Scholar 

  • Oliveira, S. V., & Cortes, R. M. V. (2006). Combining logistic models with multivariate methods for the rapid biological assessment of rivers using macroinvertebrates. Environmental Monitoring and Assessment, 112(1–3), 93–113.

    Article  CAS  Google Scholar 

  • Shi, G. M., Chen, S. L., Jiang, X. Z., & Li, X. L. (2013). Projection pursuit forest ecological function evaluation model based on immune evolutionary algorithm. Journal of Fujian Forestry College, 2, 142–145.

  • Tang, S. Z., Li, X. F., & Meng, Z. H. (1993). Advances in the study of stand growth models. Forestry Science Research, 6, 672–679.

    Google Scholar 

  • Thomas, W., & O’Gorman. (2001). A comparison of the f-test, Friedman’s test, and several aligned rank tests for the analysis of randomized complete blocks. Journal of Agricultural, Biological, and Environmental Statistics, 6(3), 367–378.

    Article  Google Scholar 

  • Tian, Y. Y., Qin, F., Yan, H., Guo, W. H., & Wang, Q. W. (2011). Carbon content of common woody plants in China. Anhui Agricultural Science, 26, 16166–16169.

  • Wang, H. Y., Wang, W. J., Qiu, L., Su, D. X., An, J., Zheng, G. Y., & Zu, Y. G. (2012). Differences in biomass, litter and soil organic carbon storage of Larixgmelinii forest with stand growth. Journal of Ecology, 3, 833–843.

  • Wang, S. J., Deng, H. F., Xiang, W., Huang, G. S., & Wang, X. J. (2018). Establishment of prediction model for Pinus tabulaeformis stand volume based on mixed model. Journal of Northwest A&F University(Nat. Sci. Ed), 46(2), 29–38.

    Google Scholar 

  • Witelski, T. P. (1998). Perturbation analysis for wetting fronts in Richards’ equation. Transport in Porous Media, 27(2), 121–134.

    Article  Google Scholar 

  • Wulder, M. A., White, J. C., Stinson, G., Hilker, T. , Kurz, W. A. , & Coops, N. C. (2010). Implications of differing input data sources and approaches upon forest carbon stock estimation. Environmental Monitoring and Assessment, 166(1–4), 166–543.

  • Xie, G. D., Li, S. M., Xiao, Y., & Qi, Y. (2011). Formation and evaluation of carbon sink value. Journal of Natural Resources, 1, 1–10.

  • Yan, W., Duan, G. S., Wang, Y. H., Sun, Z., Zhou, T. L., & Fu, L. Y. (2019). Construction of stand basal area and volume growth model for Quercus and Populus in Henan Province of Central China. Journal of Beijing Forestry University, 41(6), 55–61.

    Google Scholar 

  • Zeng, W. S. (2019). Construction of the stand area and accumulation growth model of Quercus and poplar in Henan Province. Forest Resources Management, 6, 38–41.

    Google Scholar 

  • Zhang, H. R., Zhao, Y. X., Wang, X. L., & Wang, Z. M.(1999). Study on the establishment of compatible biomass model by linear simultaneous equations [J]. Forestry resource management, 28(6): 63–67.

Download references

Funding

This work received financial support from the National Science Foundation for Young Scholars of China (Grant No. 51406141), which belongs to the Project of Department of Education, Fujian Province (JA13321) and provides the Distinguished Young Scientific Research Talents Plan in Universities of Fujian Province (MES No. 54, 2015).

Author information

Author notes

  1. Hongmeng Ye contributed equally to this work and should be considered co-first author.

Authors and Affiliations

  1. Department of College of Ecology and Resource Engineering, Wuyi University, Wuyishan, 354300, Fujian, China

    Weiping Hua, Hongmeng Ye, Jui-Yeh Rau & Tian Qiu

  2. Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, College of Ecological and Resource Engineering, Wuyi University, Wuyishan, 354300, Fujian, China

    Weiping Hua, Hongmeng Ye, Jui-Yeh Rau & Tian Qiu

  3. Fujian Higher Education Institutions Key Laboratory of Green Chemistry and Technology, Wuyishan, China

    Weiping Hua, Hongmeng Ye, Jui-Yeh Rau & Tian Qiu

Authors
  1. Weiping Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongmeng Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jui-Yeh Rau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tian Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jui-Yeh Rau.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hua, W., Ye, H., Rau, JY. et al. Dynamic prediction of uneven-aged natural forest for yield of Pinus taiwanensis using joint modelling. Environ Monit Assess 192, 239 (2020). https://doi.org/10.1007/s10661-020-8204-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-020-8204-7

Keywords

Search

Navigation