首页> 外文期刊>Silva Fennica >Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach.
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Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach.

机译:从机载激光扫描数据和航拍照片估算北方森林的特定物种直径分布和锯材原木回收率:一种基于分布的方法。

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摘要

The low-density airborne laser scanning (ALS) data-based estimation methods have been shown to produce accurate estimates of mean forest characteristics and diameter distributions, according to several studies. The used estimation methods have been based on the laser canopy height distribution approach, where various laser pulse height distribution-derived predictors are related to the stand characteristics of interest. This approach requires very delicate selection methods for selecting the suitable predictor variables. In this study, we introduce a new nearest neighbour search method that requires no complicated selection algorithms for choosing the predictor variables and can be utilized in multipurpose situations. The proposed search method is based on Minkowski distances between the distributions extracted from low density ALS data and aerial photographs. Apart from the introduction of a new search method, the aims of this study were to: (1) produce accurate species-specific diameter distributions; and (2) estimate factual saw log recovery, using the estimated height-diameter distributions and a stem data bank. The results indicate that the proposed method is suitable for producing species-specific diameter distributions and volumes at the stand level. However, it is proposed, that the utilization of more extensive and locally emphasized reference data and auxiliary variables could yield more accurate saw log recoveries.
机译:根据几项研究,基于低密度机载激光扫描(ALS)数据的估算方法已显示出对平均森林特征和直径分布的准确估算。所使用的估计方法基于激光冠层高度分布方法,其中各种激光脉冲高度分布派生的预测变量与感兴趣的林分特性相关。这种方法需要非常精细的选择方法来选择合适的预测变量。在这项研究中,我们介绍了一种新的最近邻搜索方法,该方法不需要复杂的选择算法即可选择预测变量,并且可以在多种情况下使用。所提出的搜索方法基于从低密度ALS数据中提取的分布与航空照片之间的Minkowski距离。除了引入一种新的搜索方法外,本研究的目的还在于:(1)产生精确的物种特定直径分布; (2)使用估计的直径直径分布和茎数据库,估计实际的锯木采伐量。结果表明,所提出的方法适用于在林分水平上产生特定物种的直径分布和体积。但是,建议使用更广泛和局部强调的参考数据和辅助变量可以产生更准确的锯木回收率。

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