Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania

Abstract

BackgroundAirborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework.ResultsThe results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m2. The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7.ConclusionsThis study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

“…The plot locations were chosen to capture the variation in biomass by distributing them in different altitudinal zones [16]. To evaluate the representativeness of the 30 circular plots Mauya et al [16] compared the properties of the sample to a second sample of 153 systematically distributed plots covering the study area.…”

“…For this purpose the variance was estimated in a design-based framework. Mauya et al [16] concluded that reduced model noise from co-registration errors and boundary effects meant that larger plot size was preferable for ALS-supported biomass estimates.…”

“…Both co-registration errors and boundary effects are reduced with reducing the ratio of field plot periphery to plot area. Accordingly, several studies on modelling of forest biomass using remotely sensed data have documented that increased plot size increased the model precision [13][14][15][16].…”

“…To examine the effects of co-registration-and boundary-effects on the precision of ALS-supported biomass estimates, Mauya et al [16] compared the variance of field-based biomass estimates to the corresponding variance of the biomass estimates supported by ALS at different plot sizes. This ratio of variance estimates is referred to as relative efficiency, and has been used to compare different sample designs, estimators, and inferential frameworks, e.g., Payandeh [24], Ene et al [25].…”

“…This ratio of variance estimates is referred to as relative efficiency, and has been used to compare different sample designs, estimators, and inferential frameworks, e.g., Payandeh [24], Ene et al [25]. The objective of calculating the relative efficiencies in Mauya et al [16] was to assess the effect of plot size on the precision of ALS-derived biomass models. For this purpose the variance was estimated in a design-based framework.…”