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首页> 外文期刊>Carbon balance and management >Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania
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Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania

机译：田地面积大小对坦桑尼亚热带雨林中机载激光扫描辅助清单中地上生物量预测准确性的影响

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Background Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. Results The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m2. The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7. Conclusions This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.
机译：背景技术机载激光扫描（ALS）最近已成为一种有前途的工具，可以获取辅助信息，以改善基于样本的森林清单中地上生物量（AGB）的估算。在基于设计和模型辅助的推论框架下，估计依赖于一个模型，该模型将辅助ALS度量标准与在地块上估计的AGB相关联。由于所谓的边界效应会随着地块尺寸的减小而增加，因此田间地块的尺寸已被确定为模型不确定性的一种来源。最近在热带森林中进行的研究旨在量化边界对模型预测准确性的影响，但缺乏最终AGB估计后果的证据。在这项研究中，我们分析了场积大小对模型预测准确性的影响及其在模型辅助推理框架中的含义。结果结果表明，模型的预测精度随着样地大小的增加而提高。调整后的R2从0.35提高到0.74，而相对均方根误差从63.6降低到29.2％。确定边界效应的指标，并确认对模型残差具有重大影响。模型辅助平均AGB的方差估计值相对于基于纯现场AGB的相应方差估计值，随着地块大小（从200到3000 m2）的增加而减小。基于字段的估计相对于模型辅助方差的方差比在1.7到7.7之间。结论这项研究表明，与纯基于现场的清单相比，当使用ALS辅助的清单时，AGB估算精度的相对提高更大。

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