论文标题
使用收割机和机载激光扫描数据评估和减轻森林属性图中的系统错误
Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data
论文作者
论文摘要
剪切长度收割机收集有用的信息，以建模森林属性与空中激光扫描（ALS）数据之间的关系。但是，收割机在成熟的森林中运行，这可能引入选择偏见，从而导致基于数据的森林属性图导致系统错误。我们使用收割机和ALS数据为体积（V），高度（HL），茎频率（N），地面生物量和二次平均直径（QMD）的回归模型（收割机模型）拟合。在8.7 MHA研究区域使用国家森林库存图评估了收割机模型的性能。我们估计了大面积合成估计器的偏见，并将模型辅助（MA）估计量的效率与基于现场数据的直接估计器进行了比较。收割机模型在生产力上的表现要比非生产性森林表现更好，但是在两者中都出现了系统的错误。 MA估计器的使用导致HL最大的效率提高（相对效率，RE = 6.0），而对于QMD，最小的效率（RE = 1.5）。合成估计量的偏差为n（39％），对V（1％）最小。后者是由于高估了落叶和低估的云杉森林，而云杉森林偶然地平衡了。我们得出的结论是，可能需要进行参考观察的概率样本，以确保利用收割机数据的估计量的无偏见。
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.
