论文标题
使用挪威国家森林库存和机载激光扫描数据对直径分布的预测和模型辅助估计
Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data
论文作者
论文摘要
乳房高度（DBH）分布的直径为运营和战略性森林管理决策提供了宝贵的信息。我们使用挪威国家森林库存和机载激光扫描数据预测了DBH分布，并比较了线性混合效应（PPM），广义线性混合（GLM）和K最近的邻居（NN）模型的预测性能。虽然GLM导致的预测错误比PPM较小，但两者均明显优于NN。因此，我们研究了使用适用于系统抽样的模型辅助（MA）估计值在8.7 MHA研究区域中DBH类提高DBH类茎频率估计精度的能力。 MA估计值仅使用现场数据而产生的效率大于或大致相等的效率。与MA估计相关的相对效率（RES）分别为2和6 cm DBH类宽度在0.95-1.47和0.96-1.67之间，当假定已知的主要树种是已知的时。使用预测的树种图，而不是观察到的信息，将RES降低了多达10％。
Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95-1.47 and 0.96-1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.
