Regular biomass estimations for natural and plantation forests are important to support sustainable forestry and to calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has been amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we investigated the influence of field plot and sample sizes on the accuracy of random forest models trained with information derived from Pléiades very high resolution (VHR) stereo images applied to plantation forests in an arid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and 500 m²). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass estimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but constant number of samples. In the second experiment, we fixed the total area to be sampled to account for the additional effort to collect large field plots. Our results for the first experiment show that model performance metrics Spearman’s r, RMSE_rel and RMSE_nor improve from 0.61, 0.70 and 0.36 at a sample size of 24–0.79, 0.51 and 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with a plot size of 100 m² (most samples) with Spearman’s r = 0.77, RMSE_rel = 0.59 and RMSE_nor = 0.15. Results from an analysis of variance type-II suggest that the overall most important factors to explain model performance metrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach accurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly contradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing data types (e.g. LiDAR).

中文翻译：

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利用超高分辨率立体卫星图像，样地和样本量对人工林地上生物量估算的影响

天然林和人工林的常规生物量估算对于支持可持续林业和计算碳相关统计数据非常重要。遥感数据估计森林生物量的应用已得到充分证明，但仍有提高当前方法效率的空间。在这里，我们调查了田间样地和样本大小对随机森林模型准确性的影响，该模型使用从在干旱环境中应用于人工林的极高分辨率（VHR）立体图像衍生的信息训练而来。我们收集了311个位置的现场数据，并绘制了三种不同的绘图区域大小（100、300和500 m ²）。在两个实验中，我们演示了样地和样本大小如何影响生物量估算模型的准确性。在第一个实验中，我们比较了具有不同样地大小但样本数量恒定的模型精度。在第二个实验中，我们固定了要采样的总面积，以解决收集大型现场图所付出的额外努力。我们对第一个实验的结果表明，模型性能指标Spearman的r，RMSE _rel和RMSE分别在样本量为24–0.79、192和192的情况下_{也不会分别}从0.61、0.70和0.36改善。在第二个实验中，使用Spearman's r得出的样地大小为100 m ²（大多数样本）时，获得了最高的准确度 = 0.77，RMSE _rel  = 0.59，RMSE_也不 = 0.15。II型方差分析的结果表明，解释我们生物量模型的模型性能指标的总体最重要因素是样本量。我们的结果表明，在人工林中使用VHR立体图像对任何样地面积进行准确的生物量估算均无明显优势。这是一个重要发现，部分与早期研究的建议相抵触，但需要对其他森林类型和遥感数据类型（例如LiDAR）进行验证。