Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass

Abstract

We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m2 and a 10.4% lower sample size for larger plots of 1700 m2. We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.

The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies

“…This first model of the horizontal distribution of crown biomass may support the improvement of precision of estimating biomass from field sampling (as recently illustrated in Kleinn et al [7]). Further, we expect that remote-sensing-based models to predict biomass may benefit from the geometrically more specific determination of the plot biomass when applying the HBD to all sample trees within a fixed area plot.…”

“…The individual tree biomass distribution is a basic element required to generate horizontal forest biomass distribution for application in forest inventory for biomass from fixed-area plots [7], and alike applications. It will also be of interest for remote sensing-based modelling of forest biomass when matching field plot biomass to remotely sensed data, as well as for many other ecological and forest management studies [7].…”

“…The individual tree biomass distribution is a basic element required to generate horizontal forest biomass distribution for application in forest inventory for biomass from fixed-area plots [7], and alike applications. It will also be of interest for remote sensing-based modelling of forest biomass when matching field plot biomass to remotely sensed data, as well as for many other ecological and forest management studies [7]. In the absence of available models as of yet, we chose two empirical pathways to construct our own models: (1) a destructive study of 23 trees and (2) combining the destructive sampling study with TLS which may help to increase sample size in a costefficient manner.…”

“…For example in forest fire behavior and risk modelling [4,5], predictions of the horizontal biomass distribution (HBD) is required for models that predict the spread of forest fires [6]. Further, in a context of enhancing the precision of forest biomass estimation, the HBD may gain relevance as recent simulation studies indicate [7]. Lastly, HBD models are also relevant when field plot data on biomass density are used in the area based approach [8] as the dependent variable in models with remotely sensed predictors because the variance explained by the model also depends on the match between the reference areas for field and remotely-sensed data sources.…”

A century of national forest inventories – informing past, present and future decisions

“…At the scale of their study area, the use of HMB revealed that 75% of the uncertainty in biomass estimates was caused by uncertainties in tree-level biomass model parameter estimates. Kleinn et al (2020) describe how a new perspective on continuous landscape variables, such as full-tree biomass, can reduce uncertainty in estimates using common field sampling. With their continuous approach, the spatial, 2-dimensional biomass distribution of trees is modelled, instead of aggregating all biomass to the point of the stem position as with the traditional approach.…”