Kamal Raj Aryal , Dipak Mahatara , Rajendra Kumar Basukala , Sabitra Khadka , Sakar Dhakal , Shubhashis Bhattarai , Hari Adhikari , Dinesh Jung Khatri , Ram P. Sharma
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As significant differentiation of the stem volume was observed by region in the analysis, a common tree stem volume model applicable to <em>S. robusta</em> forests in both regions was developed by applying the dummy variable modeling approach. Among some versatile growth functions (power, fractional and exponential functions) considered for fitting data with diameter at breast height, total tree height and crown width used as predictors, the power function provided the best fits (R<sup>2</sup><sub>adj</sub> = 0.9730; RMSE = 0.1427) with no systematic residual trends observed. The model simulation exhibited an increased volume with increasing tree height but decreasing crown width. The presented model was proved to be statistically flexible and biologically plausible and thus can be applied for a precise volume prediction of the species of interest. Model accuracy can be increased with the model recalibrated using additional predictor variables (e.g., site and climate variables) and more data collected in wider geographical ranges of the Siwalik and non-Siwalik hills of the Karnali province and beyond.</p></div>","PeriodicalId":36104,"journal":{"name":"Trees, Forests and People","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666719324001821/pdfft?md5=a1a7a68dce5edfabf776395f6f96d3fd&pid=1-s2.0-S2666719324001821-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Modeling tree stem volume for hill Shorea robusta Gaertn. forests in Karnali Province, Nepal\",\"authors\":\"Kamal Raj Aryal , Dipak Mahatara , Rajendra Kumar Basukala , Sabitra Khadka , Sakar Dhakal , Shubhashis Bhattarai , Hari Adhikari , Dinesh Jung Khatri , Ram P. 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引用次数: 0
摘要
盐树(Shorea robusta Gaertn.)是尼泊尔的主要树种,通过多种用途在社会经济发展中发挥着重要作用。建立树干体积模型为估算森林生物量、碳储量和木材经济价值提供了基本工具,对建立生长和产量模型以及分析森林生态系统非常有用。本研究利用尼泊尔西瓦利克山和非西瓦利克丘陵地区不同社区管理森林中 503 棵 S. robusta 树的测量数据,建立了树干体积模型。由于在分析中观察到不同地区的茎干体积存在明显差异,因此通过应用虚拟变量建模方法,建立了适用于这两个地区 S. robusta 森林的通用茎干体积模型。在胸径、总树高和冠幅作为预测因子的数据拟合过程中,考虑了一些多功能生长函数(幂函数、分数函数和指数函数),其中幂函数的拟合效果最好(R2adj = 0.9730;RMSE = 0.1427),而且没有观察到系统残差趋势。模型模拟结果表明,随着树高的增加,体积增大,但冠幅减小。事实证明,所提出的模型在统计学上是灵活的,在生物学上也是合理的,因此可用于对相关物种进行精确的体积预测。如果使用更多的预测变量(如地点和气候变量)对模型进行重新校准,并在卡纳利省的西瓦利克山和非西瓦利克山等更广阔的地理范围内收集更多的数据,那么模型的准确性还可以提高。
Modeling tree stem volume for hill Shorea robusta Gaertn. forests in Karnali Province, Nepal
Sal (Shorea robusta Gaertn.) is a major tree species of Nepal, which plays a vital role in the socio-economic development of livelihoods through multi-purpose uses. Developing a tree stem volume model provides a fundamental tool for estimating forest biomass, carbon stock, and economic value of timber and is useful for modeling growth and yield and analysis of forest ecosystems. This study developed tree stem volume models using measurements from 503 S. robusta trees of different community-managed forests in both the Siwalik Hill and non-Siwalik hilly regions of Nepal. As significant differentiation of the stem volume was observed by region in the analysis, a common tree stem volume model applicable to S. robusta forests in both regions was developed by applying the dummy variable modeling approach. Among some versatile growth functions (power, fractional and exponential functions) considered for fitting data with diameter at breast height, total tree height and crown width used as predictors, the power function provided the best fits (R2adj = 0.9730; RMSE = 0.1427) with no systematic residual trends observed. The model simulation exhibited an increased volume with increasing tree height but decreasing crown width. The presented model was proved to be statistically flexible and biologically plausible and thus can be applied for a precise volume prediction of the species of interest. Model accuracy can be increased with the model recalibrated using additional predictor variables (e.g., site and climate variables) and more data collected in wider geographical ranges of the Siwalik and non-Siwalik hills of the Karnali province and beyond.