利用采伐机和机载激光扫描数据评估和减少森林属性图中的系统误差
Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data
摘要：
定长切割收割机收集有用的信息，用于建模森林属性和机载激光扫描（ALS）数据之间的关系。然而，收割机在成熟的森林中作业，这可能会引入选择偏差，从而导致基于收割机数据的森林属性地图中的系统性错误。我们拟合回归模型（收割机模型）体积（V），高度（HL），茎频率（N），地上生物量，断面积，和平方平均直径（QMD）使用收割机和ALS数据。性能的收割机模型进行了评估，使用国家森林资源清查地块在8.7 Mha的研究领域。我们估计了大面积合成估计的偏差，并比较了模型辅助（MA）估计与基于现场数据的直接估计的效率。收获机模型在生产性森林中的表现优于非生产性森林，但两者都出现了系统误差。使用MA估计量导致HL的效率增益最大（相对效率，RE=6.0），QMD的效率增益最小（RE=1.5）。合成估计量的偏倚最大为N（39%），最小为V（1%）。后者是由于高估了落叶和低估了云杉林，偶然平衡。我们的结论是，可能需要一个参考观测的概率样本，以确保利用收割机数据估计的无偏性。
英文摘要：
Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE=6.0) and smallest for QMD (RE=1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data.
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