Background: Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation—errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n = 285). Results: Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30–75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25–70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE] = 50%). This is likely due to low-sample size (10–20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE = 97%) and nationwide (2.2 billion Mg; % RMSE = 94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE = 165%). Conclusions: Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.

Allometric equations predict organism attributes from simple measurements and underlie many global‐scale estimates, from plant productivity to ecosystem carbon stocks. In forests, destructive harvesting of trees in common groups (e.g. plant functional type) or at the species level is necessary to develop allometry but, since sampling is extremely difficult, predictions from these equations have high uncertainty due to low sample size, spatial bias and unrepresentative sampling of tree size. Terrestrial laser scanning (TLS) is a promising remote sensing technology that enables efficient and nondestructive estimates of tree‐level structure for developing allometric equations. Here, we nondestructively estimated component biomass of three coniferous tree species (Pinus ponderosa, Pinus contorta and Pseudotsuga menziesii) in Colorado, USA using TLS. We evaluated the suitability for this nondestructive data to be supplanted for destructive data in the development of species‐specific allometric equations and compared the prediction accuracy against a commonly used national‐scale allometry. We found TLS biomass estimates were consistently more precise across species (RMSE = ~19%) than nation‐scale allometry (RMSE = ~39%). Nondestructive biomass estimates from TLS are a suitable addition to or replacement for traditional sampling methods, with indistinguishable biomass predictions across most of the tested diameter range. We further show how TLS can be used to develop allometric equations compatible with airborne LiDAR and other remote sensing variables (e.g. height and crown area), developing generalized biomass predictions from crown area and tree height (R² = 0.87). The ability for TLS to support the development of nondestructive allometry at a global scale will enable a more nuanced understanding of the drivers of individual tree architecture, while supporting the next generation of biomass remote sensing.

Here we present “CO-RIP”, a novel spatial dataset delineating riparian corridors and riparian vegetation along large streams and rivers in the United States (US) portion of the Colorado River Basin. The consistent delineation of riparian areas across large areas using remote sensing has been a historically complicated process partially due to differing definitions in the scientific and management communities regarding what a “riparian corridor” or “riparian vegetation” represents. We use valley-bottoms to define the riparian corridor and establish a riparian vegetation definition interpretable from aerial imagery for efficient, consistent, and broad-scale mapping. Riparian vegetation presence and absence data were collected using a systematic, flexible image interpretation process applicable wherever high resolution imagery is available. We implemented a two-step approach using existing valley bottom delineation methods and random forests classification models that integrate Landsat spectral information to delineate riparian corridors and vegetation across the 12 ecoregions of the Colorado River Basin. Riparian vegetation model accuracy was generally strong (median kappa of 0.80), however it varied across ecoregions (kappa range of 0.42–0.90). We offer suggestions for improvement in our current image interpretation and modelling frameworks, particularly encouraging additional research in mapping riparian vegetation in moist coniferous forest and deep canyon environments. The CO-RIP dataset created through this research is publicly available and can be utilized in a wide range of ecological applications.