Forest inventories are increasingly based on airborne laser scanning (ALS). In Finland, the results of these inventories are calculated for small grid cells, 16 m by 16 m in size. Use of grid data in forest planning results in the additional requirement of aggregating management prescriptions into large enough continuous treatment units. This can be done before the planning calculations, using various segmentation techniques, or during the planning calculations, using spatial optimization. Forestry practice usually prefers reasonably permanent segments created before planning. These segments are expected to be homogeneous in terms of site properties, growing stock characteristics and treatments. Recent research has developed methods for partitioning grids of ALS inventory results into segments that are homogeneous in terms of site and growing stock characteristics. The current study extended previous methods so that also the similarity of treatments was considered in the segmentation process. The study also proposed methods to deal with biases that are likely to appear in the results when grid data are aggregated into large segments. The analyses were conducted for two datasets, one from southern and the other from northern Finland. Cellular automaton (CA) was used to aggregate the grid cells into segments using site characteristics with (1) growing stock attributes interpreted from ALS data, (2) predicted cutting prescriptions and (3) both stand attributes cutting prescriptions. The CA was optimized for each segmentation task. A method based on virtual stands was used to correct systematic errors in variable estimates calculated for segments. The segmentation was rather similar in all cases. The result is not surprising since treatment prescriptions depend on stand attributes. The use of virtual stands decreased biases in growth prediction and in the areas of different fertility classes. Automated stand delineation was not sensitive to the type of variables that were used in the process. Virtual stands are an easy method to decrease systematic errors in calculations.

中文翻译：

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从网格数据描绘林分

森林清单越来越多地基于机载激光扫描（ALS）。在芬兰，这些清单的结果是针对尺寸为16 m x 16 m的小型网格计算的。在森林规划中使用网格数据会导致额外的要求，即将管理处方汇总到足够大的连续处理单元中。这可以在计划计算之前使用各种分割技术来完成，或者在计划计算期间使用空间优化来完成。林业实践通常倾向于在计划之前创建合理的永久性细分。这些部位在场地性质，生长种群特征和处理方面均有望达到均质。最近的研究已经开发出将ALS库存结果的网格划分为在站点和不断增长的库存特征方面均一的段的方法。当前的研究扩展了以前的方法，因此在分割过程中也考虑了处理的相似性。该研究还提出了一些方法，以解决将网格数据聚合为大片段时可能在结果中出现的偏差。对两个数据集进行了分析，一个来自南部，另一个来自芬兰北部。使用元胞自动机（CA）使用站点特征将网格单元聚合为段，其中（1）从ALS数据解释增长的种群属性，（2）预测的切割处方，以及（3）两种站立切割的处方。针对每个细分任务优化了CA。使用基于虚拟机架的方法来校正针对分段计算的变量估计中的系统误差。在所有情况下，细分都相当相似。结果并不出人意料，因为治疗处方取决于立场属性。虚拟林分的使用减少了对生长预测和不同生育水平地区的偏见。展位的自动划分对过程中使用的变量类型不敏感。虚拟机架是减少计算中系统误差的一种简便方法。虚拟林分的使用减少了对生长预测和不同生育水平地区的偏见。展位的自动划分对过程中使用的变量类型不敏感。虚拟机架是减少计算中系统误差的一种简便方法。虚拟林分的使用减少了对生长预测和不同生育水平地区的偏见。展位的自动划分对过程中使用的变量类型不敏感。虚拟机架是减少计算中系统误差的一种简便方法。