Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns, and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning (ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded. Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALS-enhanced stand-level inventory. Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/− 5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used. ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.

中文翻译：

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机载激光扫描采样强度对两相林清单性能的影响

森林清单一直是有关森林生态系统状态的主要信息来源。在抽样方案规划过程中，众多重要因素产生了各种应用的调查方法。最重要的方面包括调查目标和规模，目标人群的固有变异和模式以及可用资源。最后一个因素通常会抑制目标，因此必须做出妥协。机载激光扫描（ALS）已作为森林清单的一种经济有效的选择经过了严格测试。尽管已有基础，但研究已提供了不同的结果。环境条件是极大影响库存绩效的因素之一。因此，对与站点相关的采样优化的需求已得到充分确立。此外，由于林分是受管理森林资产的基本业务单位，很少有相关研究提出标准结果。因此，在这里，我们测试了采样强度对ALS增强型标准清单性能的影响。通过比较ALS模型估算值和来自5个林区的3300个样地和300多个对照林地的实地调查得出的参考值，绘制了可能的误差分布。由于扫描密度，未观察到结果的改善。所获得误差的方差稳定在200–300个样本图的间隔内，将偏差保持在+/- 5％之内，而精度保持在80％以上。从100平方米过渡到200平方米时，样地面积影响得分。当使用更大的地块时，只能观察到一点点收益。ALS增强的库存有效地满足了对广大地区单一展台结构的全面详细信息的需求。了解采样强度和ALS估计的准确性之间的关系可以确定某些采样强度阈值。当将所需的样本大小和准确性与可用资源进行匹配时，这将很有用。站点优化可能是必要的，因为由于采样方案，估算器类型或森林站点而可能会发生某些错误，因此值得进一步考虑这些因素。当将所需的样本大小和准确性与可用资源进行匹配时，这将很有用。站点优化可能是必要的，因为由于采样方案，估算器类型或森林站点而可能会发生某些错误，因此值得进一步考虑这些因素。当将所需的样本大小和准确性与可用资源进行匹配时，这将很有用。站点优化可能是必要的，因为由于采样方案，估算器类型或森林站点而可能会发生某些错误，因此值得进一步考虑这些因素。