Bright et al. (2025) Using point dendrometers to improve forest transpiration estimation accuracy at stand scales

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2025
• Date: 2025-12-16
• Authors: Ryan M. Bright, Danielle Creek, Holger Lange, Helge Meissner, Morgane Merlin, Junbin Zhao
• DOI: 10.1016/j.agrformet.2025.110986

Research Groups

• Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway
• Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Ås, Norway

Short Summary

This study investigates whether increasing the number of sampled trees for stand-level transpiration estimation, by augmenting sap flow measurements with data from point dendrometers, can reduce uncertainty. It found that expanding the tree sample size using point dendrometer-derived sap flow estimates reduced the uncertainty of stand-level transpiration by 31–37%, demonstrating a cost-effective method to improve accuracy.

Objective

• To test the hypothesis that enlarging the tree sample size, combining conventional sap flow monitoring with point dendrometer measurements, increases stand representativeness and reduces the uncertainty of stand-level transpiration estimates.

Study Configuration

• Spatial Scale: A mature boreal forest stand dominated by Norway spruce (Picea abies) in Hurdal municipality, southeast Norway (ICOS Level 2 eddy-covariance site NO–Hur). The representative sampling area covered 19,140 m² (just under two hectares). Measurements were conducted on individual trees and scaled to the stand level.
• Temporal Scale: Two growing seasons (2022 and 2023). Raw data collected at 10-second or 10-minute intervals, aggregated to hourly for sap flow modeling and daily for stand-level transpiration estimation.

Methodology and Data

• Models used:
  • Empirical model for hourly individual tree sap flow, developed using ensemble learning (least-squares gradient boosting) based on point dendrometer measurements, biometric data, and meteorological variables.
  • Reference stand-level transpiration (Tref) models: P-model (Stocker et al., 2020; Wang et al., 2017) and three configurations of the Penman-Monteith-Leuning (PML) model (Leuning et al., 2008; Launiainen et al., 2016; Hoshika et al., 2018).
  • Linear mixed-effects model for formal uncertainty quantification.
• Data sources:
  • Point dendrometers (TOMST s.r.o.): Measured stem radius fluctuations with micrometer precision at 10-minute intervals on 5 (2022) and 15 (2023) dominant trees.
  • Sap flow sensors (SFM1, ICT International Pty Ltd.): Measured sap velocity using the heat ratio method at two depths (12.5 mm and 27.5 mm) at 10-minute intervals on 5 (2022) and 13 (2023) spruce and pine stems.
  • Forest inventory: Tree height (H), diameter at breast height (DBH), and sapwood depth (SWD) measured at two continuous plots and 20 sparse plots (total 19,140 m²) in 2021 and 2023.
  • Meteorological station (ICOS Level 2 site NO–Hur): Temperature, humidity, radiation (incoming solar, incoming downwelling longwave, net), vapor pressure deficit (VPD), wind speed, relative humidity, air temperature, precipitation, CO2 concentration, and photosynthetic photon flux density (PPFD) at 20-second intervals. Effective leaf area index (LAI) from monthly measurements.

Main Results

• A critical threshold of four sampled trees was identified for achieving significant uncertainty reductions in stand-level transpiration estimates, with diminishing returns observed beyond this number.
• Including modeled tree-level sap flow from point dendrometer-equipped trees, alongside direct sap flow measurements, reduced the uncertainty of stand-level transpiration estimates (rRMSD and rMAD) by an average of 31–37%.
• This uncertainty reduction was robust, holding true despite the introduction of modeling error from the sap flow estimation and across different reference transpiration models.
• The improvement gained by incorporating point dendrometer data was comparable to adding another sap flow measurement tree, offering substantial cost savings given that point dendrometers are approximately 20 times less expensive than sap flow monitors.
• Point dendrometers also provide practical benefits such as low power consumption, simple data logging, robust design, and the ability to fill gaps in observational records, ensuring continuity in transpiration estimates.

Contributions

• This study provides the first explicit investigation into the combined effect of sampling and modeling error on the uncertainty of stand-level transpiration estimation, particularly leveraging point dendrometers.
• It demonstrates a cost-effective and practical approach to improve the accuracy and representativeness of stand-level transpiration estimates by integrating point dendrometer data with traditional sap flow measurements.
• The findings have significant implications for long-term eddy covariance-based monitoring networks, advocating for the broad deployment of point dendrometers as a cost-efficient tool to capture real-time forest physiological dynamics and generate robust field data for large-scale modeling and upscaling.
• The research highlights the potential of point dendrometers to enhance understanding of ecosystem water and carbon dynamics and to provide redundancy in monitoring systems.

Funding

• Research Council of Norway grant number 343437
• Research Council of Norway grant number 342631/L10
• Research Council of Norway grant number 350341 (ICOS3)

Citation

@article{Bright2025Using,
  author = {Bright, Ryan M. and Creek, Danielle and Lange, Holger and Meissner, Helge and Merlin, Morgane and Zhao, Junbin},
  title = {Using point dendrometers to improve forest transpiration estimation accuracy at stand scales},
  journal = {Agricultural and Forest Meteorology},
  year = {2025},
  doi = {10.1016/j.agrformet.2025.110986},
  url = {https://doi.org/10.1016/j.agrformet.2025.110986}
}