Forest height, as a measure of the quantity and quality of forest resources, plays a significant role in the study of the ecological functions performed by forests. Although the polarimetric synthetic aperture radar interferometry (PolInSAR) technique has evolved as a potent method for forest height inversion, uncertainties still exist in the process of estimating forest height, and the uncertainties in predicted forest height directly lead into the uncertainty of terrestrial carbon stock calculation results. In this study, we took the Random Volume over Ground (RVoG) model as likelihood function and constructed a hierarchical Bayesian framework to calculate and reduce the uncertainty of forest height inversion using L / P band PolInSAR airborne data via RVoG model. Uncertainties resulted from five canopy types and three forest densities were analyzed, respectively. The results showed that among the five different canopy types, L band has the highest prediction accuracy in pure coniferous canopy with . = 0.90. The uncertainty is extremely low for pure forest, with the ratio of uncertainty values of 0.09 for L band and 0.15 for the P band in pure coniferous canopy, and uncertainty values of 0.16 for L band and 0.11 for P band in pure broadleaf canopy, respectively. Furthermore, when the forest density is between 300 and 600 stems/ha, the ratio of uncertainties for the L band is 0.27, whereas the P band is 0.24. As forest density increases, the uncertainty in forest height estimates decreases for both bands. The changes in canopy types and forest density affect forest height estimation uncertainties obviously, the effects are different at each frequency. The forest height inversion accuracy of the L band in pure coniferous canopy surpasses that in other canopy types, with the lowest uncertainty. P band performed well in broadleaf canopy forest height inversion. The inversion uncertainties at both frequencies decrease with increase of forest densities.

中文翻译：

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使用 L / P 波段 PolInSAR 数据集和 RVoG 模型对 kryclan 森林站点进行森林高度反演的不确定性分析


森林高度作为森林资源数量和质量的衡量标准，对于研究森林的生态功能具有重要作用。尽管极化合成孔径雷达干涉测量（PolInSAR）技术已发展成为一种有效的森林高度反演方法，但在估算森林高度的过程中仍然存在不确定性，预测森林高度的不确定性直接导致陆地碳储量计算的不确定性结果。在本研究中，我们以地面随机体积（RVoG）模型为似然函数，构建了分层贝叶斯框架，通过RVoG模型使用L/P波段PolInSAR机载数据计算并减少森林高度反演的不确定性。分别分析了五种树冠类型和三种森林密度引起的不确定性。结果表明，在5种不同冠层类型中，L波段在纯针叶树冠层中的预测精度最高。 = 0.90。纯林的不确定性极低，纯针叶林冠层L波段和P波段不确定性值之比分别为0.09和0.15，纯阔叶林冠层L波段和P波段不确定性值分别为0.16和0.11 。此外，当森林密度在 300 至 600 株/公顷之间时，L 波段的不确定性比率为 0.27，而 P 波段的不确定性比率为 0.24。随着森林密度的增加，两个频段的森林高度估计的不确定性都会降低。冠层类型和森林密度的变化对森林高度估计的不确定性影响明显，且各频率的影响不同。 纯针叶林冠层L波段的森林高度反演精度超过其他冠层类型，不确定性最低。 P波段在阔叶林冠层高度反演中表现良好。两个频率的反演不确定性随着森林密度的增加而降低。