Abstract
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} 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.
Highlights
In precision forestry, a wide range of data sources is utilized to generate accurate information about the state of the forest at a given location
Regressions for the data in individual forwarder runs showed R2 values higher than 0.80 with many of the fieldbus signals, and similar results were found with some fieldbus signals from the moving harvester
The study found the grid cells useful in the clustering of machine signals, as they provided a link between machine data clusters and forest data
Summary
In precision forestry, a wide range of data sources is utilized to generate accurate information about the state of the forest at a given location. This information is mainly for forest resource management and decision support systems in the wood procurement process for targeting forestry operations (Fardusi et al 2017; Gülci et al 2015). The current state estimate of the forest for precision forestry operations can be updated with data from other geographical information systems (GIS), such as soil topography and weather databases (Mason et al 2016; Salmivaara et al 2017)
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