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Articles containing the keyword 'topographic feature'

Category: Research article

article id 10242, category Research article
Shisheng Long, Siqi Zeng, Falin Liu, Guangxing Wang. (2020). Influence of slope, aspect and competition index on the height-diameter relationship of Cyclobalanopsis glauca trees for improving prediction of height in mixed forests. Silva Fennica vol. 54 no. 1 article id 10242. https://doi.org/10.14214/sf.10242
Keywords: secondary forest; effect; improved Hegyi_I; topographic feature; tree height estimation
Highlights: In this study, the effects of slope, aspect and competition index (CI) on the H-DBH relationship were explored and an improved CI was developed and included to improve predictions of Cyclobalanopsis glauca tree height; It was found that the effects were statistically significant and considering slope, aspect and CI for developing the H-DBH models significantly increased the H prediction accuracy.
Abstract | Full text in HTML | Full text in PDF | Author Info

Diameter at breast height (DBH) and height (H) of trees are two important variables used in forest management plans. However, collecting the measurements of H is time-consuming and costly. Instead, the H-DBH relationship is modeled and used to estimate H. But, ignoring the effects of slope, aspect and tree competition on the H-DBH relationship often impedes the improvement of H predictions. In this study, to improve predictions of Cyclobalanopsis glauca (Thunb.) Oerst. tree H in mixed forests, we compared eleven H-DBH models and examined the influence of slope and aspect on the H-DBH relationship using 426 trees. We then improved Hegyi competition index and explored its effect on the H predictions by including it in the selected models. Results showed 1) There were statistically significant effects of slope and aspect on the H-DBH relationship; 2) The log transformation and exponential model performed best for sunny- and shady-steep, respectively, and the Gompertz’s model was optimal for both sunny- and shady-gentle; 3) Compared with the whole dataset, the division of the data into the slope and aspect sub-datasets significantly reduced the RMSE of H predictions; 4) Compared with the selected models without competition index, adding the original Hegyi and improved Hegyi_I into the models improved the H predictions but only the models containing the improved Hegyi_I significantly increased the prediction accuracy at the significant level of 0.1. This study implied that modeling the H-DBH relationship under different slopes and aspects and including the improved Hegyi_I provided the great potential to improve the H predictions.

  • Long, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: shisheng3604@21cn.com
  • Zeng, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: zengsiqi@21cn.com
  • Liu, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: liufl680@126.com
  • Wang, Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA E-mail: gxwang@siu.edu (email)

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