斯里维利普图尔野生动物保护区的生物质模式:通过机器学习方法探索因素和梯度
1Department of Ecology and Environmental Sciences, School of Life Sciences, Pondicherry University, Puducherry, India.
Environmental monitoring and assessment
|April 7, 2024
概括
森林生物质对于碳循环至关重要. 这项研究发现,立体结构,而不仅仅是高度,显著影响斯里维利普图尔野生动物保护区的树木碳库和生物量.
科学领域:
- 森林生态 森林生态
- 碳封存研究 碳封存研究
- 生物质估计生物质估计
背景情况:
- 森林生物质是全球碳循环的关键组成部分,影响气候调节.
- 准确估计树木碳库存 (TCS) 和地表生物质 (AGB) 对于理解森林生态系统动态至关重要.
研究的目的:
- 调查与斯里维利普图尔野生动物保护区的高度相关的树木碳库 (TCS) 和地面生物质 (AGB).
- 确定影响生物质分布的关键因素,包括种群结构和气候变量.
- 为了比较机器学习 (随机森林) 和概括的线性模型在预测生物质方面的性能.
主要方法:
- 采用一种非破坏性的碳估计方法.
- 利用克鲁斯卡尔-瓦利斯试验和线性回归来评估TCS和高度之间的关系.
- 应用随机森林 (RF) 和通用线性模型 (GLM) 来确定树干结构属性 (基底面积,乳房高度直径,密度) 和气候变量 (温度,降水,斜率) 对生物质的影响.
主要成果:
- 总树木生物质在220.9至720.6毫克/公之间,TCS在103.8至338.7毫克/公之间.
- 标准结构属性 (基底面积,DBH,密度) 在TCS上比气候因素更有影响.
- 在预测AGB的过程中,RF模型 (R2=0.92,RMSE=0.12) 的表现优于GLM (R2=0.88,RMSE=0.35).
结论:
- 树木的结构特征显著推动了森林生物质和碳库存,而高度起到了较小的作用.
- 与传统的统计模型相比,机器学习模型,特别是随机森林,在生物质估计方面提供了更高的准确性.
- 了解这些驱动因素对于有效的森林管理和碳循环研究至关重要.
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