Abstract
The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.
Highlights
Why Monitoring in Near Real-Time? monitoring in forest ecology has a long history (Schimel and Keller, 2015; Richardson et al, 2018; Etzold et al, 2019, 2020; Schulze et al, 2019), the lack of more automated approaches to deal with in situ ecophysiological measurements becomes more obvious due to the increasing data requirements from Earth system models and stakeholder requests
Many forest ecology networks are based on traditional growth measurements to assess forest functioning and rely either on manually recorded long-term data series or automatically measured data stored in decentral loggers and data bases
TreeNet’s setup is well suited for relative comparisons of the temporal and spatial dynamics of tree water relations and tree growth, which is reflected in the official title of the network: TreeNet– the biological drought and growth indicator network
Summary
Monitoring in forest ecology has a long history (Schimel and Keller, 2015; Richardson et al, 2018; Etzold et al, 2019, 2020; Schulze et al, 2019), the lack of more automated approaches to deal with in situ ecophysiological measurements becomes more obvious due to the increasing data requirements from Earth system models and stakeholder requests. Data are collected automatically in the field (Figure 2) in a 10-min resolution and continuously transferred to the end user through several steps in near real-time, i.e., stored in databases, cleaned and processed with the R software package treenetproc (R Core Team, 2019; Haeni et al, 2020; Knüsel et al, 2021), filtered, aggregated and visualized in an internet interface. The detailed functionalities of treenetproc are described in Knüsel et al (2021) and, in addition to the automatically executed functions, include features for user-initiated checking, cleaning and processing of time series, e.g., to raise the data quality from L2 to LM data (Figure 3) or to obtain seasonal characteristics as beginning and ending of the stem growth period. This type of data processing allows for the detection of unusual seasonal deviations from the norm in the current GRO and TWD data
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