Forest resource data is important in targeting the forestry operations, and it is in the hearth of the precision forestry concept. The forest resource data can be produced with many techniques, and the number of existing forest data sources has increased during the years. In addition to the forest resource data, other data describing the circumstances of the forest site, such as trafficability and weather conditions, are available. In Finland, a forest data platform gathers the data sources under a single service for easier implementation of the precision forestry applications. This data is useful in operations planning, but it also describes the conditions that prevail when the forest machine arrives to the forest site. This study proposes data fusion between fieldbus time series of the forest machine and the forest data. The fused dataset enables explorative statistical analysis for examining the relationship between the machine performance and the forest attributes and provides data for building predictive models between the two. The presented methods are applied into a dataset generated from a field test data. The results show that some fieldbus time series features are predictable from forest attributes with $$R^{2}$$ R 2 value over 0.80, and clustering methods help in interpreting the machine behavior in different environments. In addition, an idea for generating a new forest data source to the forest data platform based on the fusion is discussed.

中文翻译：

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融合开放森林数据和机器现场总线数据用于森林机器性能分析

森林资源数据对于有针对性的林业运营很重要，它是精准林业概念的核心。森林资源数据可以通过多种技术生成，并且这些年来现有森林数据源的数量有所增加。除了森林资源数据外，还可以获得描述森林场地情况的其他数据，例如通行性和天气条件。在芬兰，森林数据平台在单一服务下收集数据源，以便更轻松地实施精准林业应用程序。该数据在运营规划中很有用，但它也描述了林业机械到达林地时的主要条件。本研究提出了森林机器的现场总线时间序列与森林数据之间的数据融合。融合数据集支持探索性统计分析，以检查机器性能与森林属性之间的关系，并为构建两者之间的预测模型提供数据。所提出的方法应用于从现场测试数据生成的数据集。结果表明，$$R^{2}$$R 2 值超过0.80的森林属性可以预测一些现场总线时间序列特征，并且聚类方法有助于解释不同环境中的机器行为。此外，还讨论了基于融合为森林数据平台生成新的森林数据源的思路。所提出的方法应用于从现场测试数据生成的数据集。结果表明，$$R^{2}$$R 2 值超过0.80的森林属性可以预测一些现场总线时间序列特征，并且聚类方法有助于解释不同环境中的机器行为。此外，还讨论了基于融合为森林数据平台生成新的森林数据源的思路。所提出的方法应用于从现场测试数据生成的数据集。结果表明，$$R^{2}$$R 2 值超过0.80的森林属性可以预测一些现场总线时间序列特征，并且聚类方法有助于解释不同环境中的机器行为。此外，还讨论了基于融合为森林数据平台生成新的森林数据源的想法。