Integrative Biology Journals

JOURNAL OF FORESTRY RESEARCH ›› 2023, Vol. 34 ›› Issue (5): 1407-1422.DOI: 10.1007/s11676-022-01576-0

• Original Paper • Previous Articles     Next Articles

Developing nonlinear additive tree crown width models based on decomposed competition index and tree variables

Siyu Qiu1, Peiwen Gao2, Lei Pan1, Lai Zhou3, Ruiting Liang1, Yujun Sun1, Yifu Wang1,g   

  1. 1 National Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, 100083, Beijing, People’s Republic of China
    2 The School of Information Science and Technology, Beijing Forestry University, 100083, Beijing, People’s Republic of China
    3 Department of Forestry, Shanxi Agricultural University, 030801, Shanxi, People’s Republic of China
  • Received:2022-04-22 Accepted:2022-08-30 Online:2024-10-16
  • Contact: Yifu Wang

Abstract:

Crown development is closely related to the biomass and growth rate of the tree and its width (CW) is an important covariable in growth and yield models and in forest management. To date, various CW models have been proposed. However, limited studies have explicitly focused on additive and inherent correlation of crown components and total CW as well as the influence of competition on crown radius from the corresponding direction. In this study, two model systems were used, i.e., aggregation method system (AMS) and disaggregation method system (DMS), to develop crown width additive model systems. For calculating spatially explicit competition index (CI), four neighbor tree selection methods were evaluated. CI was decomposed into four cardinal directions and added into the model systems. Results show that the power model form was more proper for our data to fit CW growth. For each crown radius and total CW, height to the diameter at breast height (HDR) and basal area of trees larger than the subject tree (BAL) significantly contributed to the increase of prediction accuracy. The 3-m fixed radius was optimal among the four neighborhoods selection ways. After adding decomposed competition Hegyi index into model systems AMS and DMS, the prediction accuracy improved. Of the model systems evaluated, AMS based on decomposed CI provided the best performance as well as the inherent correlation and additivity properties. Our study highlighted the importance of decomposed CI in tree CW modelling for additive model systems. This study focused on methodology and could be applied to other species or stands.

Key words: Competition decomposition, Additivity, Crown width, Spatially explicit, Competitor selection