Ecological Solutions and Evidence (Oct 2022)

Laser scanning reveals potential underestimation of biomass carbon in temperate forest

  • Kim Calders,
  • Hans Verbeeck,
  • Andrew Burt,
  • Niall Origo,
  • Joanne Nightingale,
  • Yadvinder Malhi,
  • Phil Wilkes,
  • Pasi Raumonen,
  • Robert G. H. Bunce,
  • Mathias Disney

DOI
https://doi.org/10.1002/2688-8319.12197
Journal volume & issue
Vol. 3, no. 4
pp. n/a – n/a

Abstract

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Abstract Quantifying climate mitigation benefits of biosphere protection or restoration requires accurate assessment of forest above‐ground biomass (AGB). This is usually estimated using tree size‐to‐mass allometric models calibrated with harvested biomass data. Using three‐dimensional laser measurements across the full range of tree size and shape in a typical UK temperate forest, we show that its AGB is 409.9 t ha−1, 1.77 times more than current allometric model estimates. This discrepancy arises partly from the bias towards small trees in allometric model calibration: 50% of AGB in this forest was in less than 7% of the largest trees (stem diameter > 53.1 cm), all larger than the trees used to calibrate the widely used allometric model. We present new empirical evidence that the fundamental assumption of tree size‐to‐mass scale‐invariance is not well‐justified for this kind of forest. This leads to substantial biases in current biomass estimates of broadleaf forests, not just in the UK, but elsewhere where the same or similar allometric models are applied, due to overdependence on non‐representative calibration data, and the departure of observed tree size‐to‐mass from simple size‐invariant relationships. We suggest that testing the underlying assumptions of allometric models more generally is an urgent priority as this has wider implications for climate mitigation through carbon sequestration. Forests currently act as a carbon sink in the UK. However, the anticipated increase in forest disturbances makes the trajectory and magnitude of this terrestrial carbon sink uncertain. We make recommendations for prioritizing measurements with better characterized uncertainty to address this issue.

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