Implications of allometric model selection for county-level biomass mapping

Forest aboveground biomass mapping has emerged as a critically important initiative for both constraining the global carbon cycle [18] and facilitating climate mitigation initiatives such as REDD+ [8]. Estimates of aboveground biomass are typically generated through a combination of field sampling and extrapolation using remote sensing data [9]. Lidar remote sensing, in particular, has emerged as a popular technology for mapping aboveground biomass in high biomass systems, as there is no apparent saturation of lidar metrics with high biomass provided that the lidar pulses penetrate to the ground [22]. However, the accuracies of all remote sensing-based biomass maps are inherently dependent on underlying accuracies of the field estimates of biomass that are used to calibrate remote sensing-based models.

Field estimates of biomass are generally estimated through the application of an allometric model relating some measurable attribute of field biomass, e.g. tree stem diameter or height, to aboveground biomass [5, 12]. These allometric models are typically developed through the destructive sampling of a relatively small number of trees, which are directly measured for their biomass. As destructive samples are costly to acquire, the sample sizes used to construct allometric models tend to be relatively small and spatially clustered [7]. As such, the accuracies of these allometric models outside their areas of development remain largely unknown due to a dearth of available sampled validation data [3]. Therefore, determining which allometric model to select for the estimation of field biomass is largely speculative.

In the United States, the two most common sets of allometric models are (a) a set of generalized models developed through a meta-analysis of the literature [12], and (b) the US Forest Service’s Component Ratio Method (CRM) [10, 20]. Jenkins et al. [12] combined thousands of allometric models that performed destructive sampling of trees, and simplified them to just 10 models for general applicability. The Jenkins et al. [12] models have been widely applied in North America, but, as acknowledged by the authors, there are some inherent weaknesses in these models. Notably, the mean number of trees destructively sampled per species in the available Jenkins papers was only 39 [7]. Additionally, the applicability of these models outside the environmental conditions or size class sampled is unknown. In an updated version of these models, Chojnacky et al. [4] included more models from the literature, and used taxonomic groupings and wood specific densities to regroup the original Jenkins divisions, producing 35 generalized models. Although these models are theoretically different, they are still based on the same original models (with some additions) and therefore should produce similar estimates. In contrast, the US Forest Service’s Forest Inventory Analysis program uses an entirely different approach to estimate a tree’s biomass: the CRM, [10, 20]. This method predicts tree merchantable volumes from models based on attributes such as stem diameter, height, and species. Tree volume is then used in conjunction with published wood specific gravity values to estimate the biomass in various components of the tree, namely bole, bark, and branches. Tree biomass is then calculated as the sum of these components.

Past analyses have demonstrated that the Jenkins models produce systematically higher estimates of biomass when compared to CRM, ranging from an 11% difference [13] to a 20% difference [4, 21]. Over the whole coterminous US, the Jenkins models yielded 16% greater biomass than CRM [6]. Although we can only speculate as to which set of allometric models produces a more accurate estimate of field biomass, it is important to characterize the sensitivity of biomass maps to allometric model selection. However, this is not commonly conducted in biomass mapping initiatives. In this study we explicitly test the sensitivity of county-level biomass estimates from lidar to allometric model choice, using the Jenkins et al. [12] models, Chojnacky et al. [4] models, and FIA’s CRM models.

As part of NASA’s Carbon Monitoring System (CMS), a pilot project has been funded to map forest aboveground biomass with wall-to-wall airborne lidar over Sonoma County, California. Part of this project has been focused on developing empirical models relating field estimates of forest biomass to lidar metrics, and applying the models to produce county-level biomass maps. The field and lidar data presented in this paper were collected as part of this CMS project.

Field biomass estimation

We used three different sets of allometric models to estimate the field biomass in each plot. The first two sets of models, the Jenkins et al. [12] and Chojnacky et al. [4] models, are based on a meta-analysis of published allometric models predicting biomass as a function of DBH and species. The third set follows the CRM, which for the species in Sonoma County requires tree height as a predictor variable [10, 20]. Although we only collected tree heights for a few of the tallest trees in each plot, we were able to estimate biomass with the CRM approach by predicting tree height as a function of DBH. We developed an empirical model predicting tree height as a linear function of DBH from all trees measured in the FIA dataset for Sonoma County for the 2001–2010 period (r² = 0.8, RMSE = 5.85 m, Fig. 1).

We also tested the effects of omitting “cull” by substituting measured tree heights for predicted heights on estimates of county biomass totals, but found no substantial difference. The height prediction model occasionally over-predicted tree heights for large DBHs, when DBHs were outside the range used to calibrate the height model, and we adjusted overestimates by setting the maximum tree height to the maximum height found in each 30 m Lidar pixel. These tree height estimates, as well as tree species and DBH, served as inputs to the CRM estimates of tree biomass.

To estimate plot level biomass, we estimated a biomass density at the plot centroid by summing the biomass estimates for each tree, divided by the ‘plot area’ of that tree. Therefore each tree contributes to the biomass density of the plot centroid as a function of its biomass, and distance from the plot centroid.

Field estimates of aboveground biomass are often referred to as ‘ground truth’ data in remote sensing studies, but without destructively sampling biomass in the field we do not know how accurate field estimates are. In this study, we demonstrate that allometric model selection yields on average a 19% difference in field plot estimates of aboveground biomass, and a 20% difference in the resulting county-level biomass map for Sonoma County.

Sonoma County is a particularly interesting area to conduct this study, as it hosts forests with some of the highest biomass densities in the United States. We see that the majority of discrepancies between allometric predictions occur in these high biomass areas, which is expected because more trees with smaller stems are destructively sampled for allometric model fitting, and indeed published allometries are often caveated by unknown uncertainties above a certain stem girth [12]. For example, 91 of the trees in our field plots were larger than the largest tree destructively sampled in a respective species class, as reported by Jenkins, thus potentially contributing to greater errors for these 91 trees. Although these trees only represent 8% of those sampled across the county, they represent 18% of the total estimated biomass in our field plots. However, given a lack of other available models, generalized allometric models such as applied in this paper are typically applied regardless of tree size.

The Jenkins and Chojnacky models were expected to perform similarly, as they are largely based on the same datasets of destructively sampled trees. The two studies combined the meta-analysis differently, partitioning allometric models from the literature into different combinations based on generalized species classes [12] or theoretical taxonomic groupings and wood specific gravity [4]. On average, these models produce similar biomass estimates and total county-level predictions, but discrepancies exist on a plot-to-plot basis depending on the species composition of a given plot. Most notably, the Chojnacky models produce greater estimates in high biomass plots, potentially because the models used by Chojnacky are more species-specific than the Jenkins models.

As with previous studies [4, 21], we found that the CRM field plot estimates were ~ 20% less than the Jenkins predictions. In a similar analysis in Maryland, CRM estimates in FIA plots were only ~ 11% less than the Jenkins estimates at the state level [11, 13]. The discrepancy between the differences seen in Maryland and Sonoma County could be due to the prevalence of conifer growth forms and larger trees found in Sonoma County, where we saw that estimates varied more in high biomass than low biomass areas.

There are several explanations for the differences between the Jenkins/Chojnacky and the CRM estimates seen both in this study and consistently observed in regional and national scale studies [5, 6, 14, 19]. First, the sample sizes used to construct the two sets of allometric models are different which could lead to systematic differences. Duncanson et al. [3, 7] demonstrated that allometric parameters are very sensitive to sample size, and that small sample sizes likely lead to an overestimate in biomass for a given DBH. The Jenkins and Chojnacky datasets are, on average, developed with smaller sample sizes than the FIA analysis [7]. These smaller sample sizes yield model deviations because of probable differences in the destructively harvested size distribution, with the inclusion likelihood of large individuals in a sample decreasing with sample size. Similarly, CRM volume models applied to certain species in Sonoma County were developed with destructively harvested trees outside of the region, which may yield errors if trees in Sonoma County are growing in different climate conditions or have different resource limitations than those included in the sample [19].

Finally, others have suggested that the differences between Jenkins and CRM results are due to the inclusion of tree height in the CRM approach, which may better estimate stem volume. However, we do not see strong evidence of this here, as neither maximum nor mean lidar height were highly correlated to differences between CRM and Jenkins field estimates in the tallest forests in our study area. Indeed, the plots with maximum heights between ~ 25 and 45 m had the largest discrepancies, while the tallest trees sampled approached 70 m in height. This may suggest that Jenkins DBH-based estimates may be over estimating biomass in areas of moderate height (~ 25 m) and potentially underestimating biomass in tall forests (> 50 m), while CRM estimates constrain high predictions for a given DBH in relatively short forests and increase estimates for a given DBH in very tall forests. Notably, all of the plots with the highest discrepancies had > 80% canopy cover. As canopy cover is highly correlated to biomass, it is unclear whether the variability in estimates is because of high canopy covers or high biomass densities. Certainly the limited destructive sample for large tree sizes would explain the high biomass density discrepancies, but it is conceivable that destructively harvested trees were also preferentially extracted from open, easily accessible areas with lower canopy covers. This may have caused deviations in allometries in comparison to trees growing in closed canopy systems with relatively taller, smaller crowned individuals.

Our assessment of the drivers of variability between allometric model selection remains speculative. We see that discrepancies are largest in high biomass plots with high canopies covers and moderate heights. Whether these discrepancies are due to inadequate sampling across gradients of biomass, canopy cover or height in either CRM, Jenkins/Chojnacky, or all datasets remains uncertain. Only testing the different allometric approaches against an independent destructively sampled tree dataset can determine the underlying drivers, and such a dataset is currently unavailable for use in this study. However, these results highlight the importance of improving allometric models for biomass mapping. Fortunately, progress is being made in this field, both through the collection of larger destructively harvested tree datasets that can be used to fit improved models (e.g. [5] or through the derivation of new, non-destructively derived allometries based on terrestrial laser scanning (TLS)(e.g. [2]).

All empirically derived aboveground biomass estimates are fundamentally based on the application of allometric models in the field, and thus have an error that is often unknown and unreported. The allometric models used in this study showed an approximately 20% difference in both mean plot-level and county-level totals of estimated aboveground biomass. This 20% difference is not a 20% error, as we do not have direct field measurements of biomass. Indeed, the error in field biomass estimation is likely to be greater than 20%, particularly in high biomass forests such as exhibited in some areas of Sonoma County. We found the largest discrepancies between allometric field estimates in high biomass plots with heights between 25 and 45 m, with > 80% canopy cover. Lidar heights were not highly correlated to discrepancies amongst popular allometric approaches, suggesting that an incorporation of height into models is unlikely to fully resolve observed discrepancies.

We anticipate many of existing problems related to forest biomass allometry will be addressed by the growing popularity of TLS, which enables field measurement to expand past stem diameters and heights to include full tree volumes. Taken in combination with traditional mensuration, this technology can either directly replace field estimates of biomass through direct estimation of individual tree volumes at the plot level, or improve existing allometric models through the inclusion of much greater numbers of non-destructively sampled individuals.

The findings in this not only underscore the importance of allometry in forest biomass mapping, but highlight that errors in existing allometric models are poorly understood. Further research into the effects of sample size, geographic representativeness, functional form, and the utility of TLS to address these questions is required to properly characterize errors in field estimates of biomass, and propagate these errors through to maps. This is particularly timely considering several upcoming active remote sensing datasets that will be used to map forest biomass at a global scale (e.g. NASA’s GEDI, NISAR, ESA’s BIOMASS). As in this study, the quality of these global maps will depend on the quality of field data used to calibrate the associated empirical biomass models, which will necessarily depend on the accuracy of the underlying allometric models. Thus, forest allometry is not only important at the local–regional scale studies in this paper, but for carbon accounting at a global scale.