Biomass Model Research Articles

Multi-objective optimization model for regional renewable biomass supported electricity generation in rural regions

Greenhouse gas (GHG) emissions from fossil fuel-generated electricity can be avoided by utilizing biomass (wood waste) available in rural communities. This study developed a two-layer supply chain network model comprising feedstock supply sites and candidate power plant locations. The multi-objective mathematical model considers various decisions such as power plant locations, allocation of suppliers to power plants, biomass harvesting, storage, and transportation options in the supply chain network. The goal of this formulation is to minimize the overall system cost and GHG emissions in each process of the entire network. A case study is presented in which a direct-fired power plant converts wood waste to electricity for the Grenada County, Mississippi. A Pareto solution was obtained for the case study using a Lexicographic augmented ∊-constraint algorithm. The solution with no limits on GHG emissions facilitates a higher power plant capacity, 25 MW with lower system costs and satisfies 32.1% of the total electricity demand of the case study area. The solution with the highest GHG emission restrictions (5.19 Kiloton (kt)) reduces the power plant capacity to 10 MW, which satisfies only 10.2% of the total electricity demand with higher overall system costs due to the increase in the purchase of electricity from external sources as penalty cost. Finally, a sensitivity analysis was performed by varying the total feedstock supply quantity, total electricity demand, and conversion rate of the power plant. The results show that 10.2–41.5% (base case- 40% increase in conversion rate) of the total electricity can be satisfied by the bioenergy facility taking into account the solution which facilitates bioenergy facility. In conclusion, by proper selection of power plant location and plant capacity based on the supply chain network, wood waste-based electricity generation can reduce both GHG emissions and total operational costs. This model can be applied to any region to determine the ideal location of a bioenergy facility.

Aboveground Carbon Stock Estimation Model Using Sentinel-2A Imagery in Mbeliling Lanscape in Nusa Tenggara Timur, Indonesia

To determine emission levels, information on carbon stocks and changes in each carbon pool is required. Aboveground biomass, particularly on dry land, is one carbon pool that contributes significantly to carbon storage. The goal of this study was to develop a model for estimating aboveground carbon stocks in the Mbeliling landscape, in Nusa Tenggara Timur, using a vegetation index that was correlated with field carbon stocks. The best model was then used to create a map of the distribution of carbon stocks as the final result. Simple linear regression analysis and multiple linear regression analysis were used in the study. Google Earth Engine was used to process the images on a cloud system. When comparing the RGI index for measuring field carbon stocks to other indexes, the correlation test revealed a perfect correlation. The linear regression model for aboveground biomass = 14.046 + 272.496 RGI (R-sq = 0.86) was found to be the best model for aboveground biomass. In the multiple linear regression model, there were signs of multicollinearity. With an overall accuracy of 68% and a cappa accuracy of 54.23%, the best model was able to be used to create a carbon stock map in Mbeliling landscape.
 Keywords: Carbon stock estimation model, Above Ground Biomass, Sentinel 2A

Insight into the synergistic reaction mechanism of biomass pseudo components and low-density polyethylene for the production of light aromatics through co-catalytic fast pyrolysis over hierarchical HZSM-5

Co-feeding of waste plastics during biomass catalytic fast pyrolysis can improve remarkably the yield of aromatic hydrocarbons. In this work, the simulated biomass by using its model components of cellulose, xylan, and lignin at different proportions was Co-CFP with LDPE to investigate the synergistic reaction mechanism between biomass and low-density polyethylene (LDPE) over hierarchical HZSM-5. Results showed that biomass and its model components were oxygen-enriched feedstock, while LDPE was the hydrogen-enriched feedstock with H/Ceff over 2. The synergistic effect between biomass model components and LDPE was ordered as cellulose and LDPE > xylan and LDPE > lignin and LDPE. Because pyrolysis of cellulose produced higher yield of furans than that of xylan and lignin which acted as the key synergistic compounds in Diels-Alder reaction to form light aromatics. The transfer of hydrogen free radical from the LDPE-derived hydrocarbons to the biomass-derived oxygenates promoted the deoxygenation reaction of oxygenates in hierarchical HZSM-5. Diels-Alder reaction between furans and olefins was the key synergistic reaction which highly improved the yield of hydrocarbon aromatics. In general, the difference of the pyrolysis oxygenated intermediate among the biomass pseudo components resulted in different yields of light aromatics, where cellulose presented highest extent of synergistic reaction to LDPE.

Construction of Remote Sensing Model of Fresh Corn Biomass Based on Neural Network.

Corn has a high yield and is widely used. Therefore, developing corn production and accurately estimating corn biomass yield are of great significance to improving people's lives, developing rural economy and climate issues. In this paper, a 3-layer BP neural network model is constructed by using the LM algorithm as the training algorithm of the corn biomass BP network model. From the three aspects of elevation, slope, and aspect, combined with the BP neural network model of corn biomass, the spatial distribution of corn biomass in the study area is analyzed. The results showed that the average biomass per unit area of maize increased with the increase in altitude below 1000 m. There are relatively more human activities in low altitude areas, which are more active in forestry production. The best planting altitude of corn is 0 ∼ 1000 m. When the altitude is higher than 1000 m, the corn biomass gradually decreases. In terms of slope, if the slope is lower than 15°, the biomass of maize increases with the increase in slope. If the slope is lower than 15°, the biomass of maize decreases gradually with the increase in slope. The biomass of maize on sunny slope was higher than that on shady slope.

Benthic assemblages are more predictable than fish assemblages at an island scale

Decades of research have revealed relationships between the abundance of coral reef taxa and local conditions, especially at small scales. However, a rigorous test of covariation requires a robust dataset collected across wide environmental or experimental gradients. Here, we surveyed spatial variability in the densities of major coral reef functional groups at 122 sites along a 70 km expanse of the leeward, forereef habitat of Curaçao in the southern Caribbean. These data were used to test the degree to which spatial variability in community composition could be predicted based on assumed functional relationships and site-specific anthropogenic, physical, and ecological conditions. In general, models revealed less power to describe the spatial variability of fish biomass than cover of reef builders (R2 of best-fit models: 0.25 [fish] and 0.64 [reef builders]). The variability in total benthic cover of reef builders was best described by physical (wave exposure and reef relief) and ecological (turf algal height and coral recruit density) predictors. No metric of anthropogenic pressure was related to spatial variation in reef builder cover. In contrast, total fish biomass showed a consistent (albeit weak) association with anthropogenic predictors (fishing and diving pressure). As is typical of most environmental gradients, the spatial patterns of both fish biomass density and reef builder cover were spatially autocorrelated. Residuals from the best-fit model for fish biomass retained a signature of spatial autocorrelation while the best-fit model for reef builder cover removed spatial autocorrelation, thus reinforcing our finding that environmental predictors were better able to describe the spatial variability of reef builders than that of fish biomass. As we seek to understand spatial variability of coral reef communities at the scale of most management units (i.e., at kilometer- to island-scales), distinct and scale-dependent perspectives will be needed when considering different functional groups.

Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Rice biophysical parameter retrieval with optical satellite imagery: a comparative assessment of parametric and non-parametric models

This article presents a comparison of parametric and non-parametric models in rice biomass and leaf area index (LAI) retrieval using optical satellite imagery. Four parametric models including the linear, quadratic, logarithmic and exponential models, and four non-parametric models that include RF, SVM, kNN, and GBDT were applied, respectively, on the optical satellite dataset. GBDT produced the most accurate biomass estimates (RMSE of 191.8 g/m2) before heading and the quadratic model produced the most accurate biomass estimates (RMSE of 364.7 g/m2) after heading. RF registered the most accurate LAI estimates (RMSE of 0.79 m2/m2) before heading, whereas the quadratic model recorded the most accurate LAI estimates (RMSE of 1.04 m2/m2) after heading. Non-parametric models outperformed their parametric counterparts at before heading, whereas the reverse is the case after heading. These findings provide a guide to the optimal choice of empirical models for rice biomass and LAI retrieval with optical imagery.

BIOMASS ESTIMATION ALONG A CLIMATIC GRADIENT USING MULTI-FREQUENCY POLARIMETRIC RADAR VEGETATION INDEX

Abstract. Monitoring biomass changes in the Mediterranean ecosystems is important for better understanding their responses to climatic and anthropogenic changes. Multi-frequency SAR data (L-band PASLAR and C-band Sentinel-1) is investigated for developing a biomass estimation model along a climatic gradient in Israel. First, the relationships between biomass and transmissivity at various frequencies are discussed. Multi-frequency polarimetric radar vegetation index (MPRVI) is then proposed utilizing the ensemble average of degree of polarization and cross-polarized backscattering coefficients. After randomly partitioning the data for training set and testing set, the new MPRVI-based biomass model is evaluated. It shows a good agreement with reference biomass data with an r-square of 0.899 and a root-mean-squared error (RMSE) of 0.381 kg/m2 and with a relative RMSE (RRMSE) of 10.8%.

Compatible Biomass Model of Moso Bamboo with Measurement Error

Moso bamboo is characterized by its fast growth and high yield and is important as a carbon sink species. Therefore, understanding the biomass distribution of its components is crucial. Based on the measured individual biomass data of 66 Phyllostachys heterocycla cv. Pubescens plants in the Yixing state-owned forest in Jiangsu Province, nonlinear simultaneous equations with measurement errors were constructed using nonlinear error-in-variable models (NEIVM) (one step, two step) and nonlinear seemingly unrelated regression (NSUR). Variables affecting biomass were evaluated, including diameter at breast height (DBH), bamboo height (H), height to crown base (HCB), node length at DBH (NL), base diameter (BD), and bamboo age (A). DBH, H, and HCB had significant effects on the biomass of each component. They were used to construct a one-predictor system using DBH, a two-predictor system using DBH and H, and a three-predictor system using DBH, H, and HCB. Regardless of the number of variables used, the fitting accuracy of the NEIVM one-step method exceeded that of the two-step method, and that of NEIVM exceeded that of NSUR estimation. As a system using three predictive variables is better than other systems, we recommend using the one-step NEIVM method for Moso bamboo biomass estimation.

Machine learning to understand patterns of burn severity from the SCU Lightning Complex Fires of August 2020

The SCU Lightning Complex Fire started on 16 August 2020 and burned more than 395,000 acres of woodlands and grasslands in six California counties. Satellite images of pre-fire green vegetation biomass from both 2020 springtime (moist) and summertime (drier) periods, along with slope and aspect were used as predictors of burn severity patterns on the SCU Complex landscape using machine learning algorithms. The main finding from this analysis was that the overall burn severity patterns of the SCU Complex fires could be predicted from pre-fire vegetation biomass, slope, and aspect model input variables with high accuracies of between 50% and 80% using Random Forest machine learning techniques. The August and April biomass cover variables had the highest feature importance values. It can be concluded that the amount of dry biomass present at a given location was essential to predict how severely and completely the 2020 fires burned the vegetation cover and surface soils across this landscape.

Conversion of Green Silica from Corn Leaf into Zeolites Na A-X

Combustion of corn leaf as a model of biomass from agricultural waste is a simple way to obtain the energy. It produces a low-value by-product of ash and is rich in silica that can become a precursor for zeolite production. In this study, acid-treated corn leaves combustion was performed to produce high purity silica (SiO2). The diffraction pattern suggested that the extracted silica was amorphous without the impurities phase. Additionally, the nitrogen isotherm indicated that the material was highly mesoporous silica with a total surface area of 200 m2/g. The hydrothermal method was then applied with a composition molar ratio of 1.25SiO2:1Al2O3:5Na2O:250H2O to synthesize zeolites from the silica. The temperature and time effect on the hydrothermal zeolite’s synthesis was investigated. The diffraction pattern shows that high crystalline zeolite Na A-X was produced at temperatures of 100°C and 8 h hydrothermal time. According to nitrogen physisorption analysis, the zeolite Na A-X consisted of micropores with a total surface area of 270 m2/g. The morphology of zeolite Na A-X was cube for the Na-A and octahedral for the Na-X. The hydrothermal temperature and time highly affected the zeolite formed. This research suggested that the ash waste could be valorized through conversion into a high economic value zeolites.

Improving the multiple linear regression method of biomass estimation using plant water-based spectrum correction

ABSTRACT Accurate observation of plant biomass is crucial for estimation of global carbon stocks and ecosystem productivity, and optical spectroscopy technology represents a potential solution. However, the plant water content limits biomass estimation accuracy, due to its contribution to the plant spectral characteristics. In this study, we used a plant water-based spectrum correction (PSC) method to decrease the influence of the plant water content, and biomass models were developed by combining the PSC and multiple linear regression (MLR) methods. The spectral data of 387 tree leaves were acquired using an ASD FieldSpec 3 spectrometer, and their water content values were determined by the oven-drying method. Based on the plant water values of the leaves, the optimal model for the PSC-MLR method was determined and was treated as the ultimate PSC-MLR model (coefficient of determination R 2 of validation = 0.6959, root-mean-square error of validation = 0.0220 kg m−2, mean relative error of validation = 20.60%, ratio of performance to deviation of validation = 1.3492), which produced a better performance than the standard MLR model. This work shows the great potential of combining PSC with MLR for improved plant biomass estimation accuracy.

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.

Species-specific and generalized biomass models for estimating carbon stocks of young reforestations

Biomass models are key to the correct quantification of aboveground biomass and carbon stocks. Most of the current available sets of biomass models were developed for adult trees although very few have been employed to accurately estimate biomass during the first years of reforestations, despite the increasing number of reforestations over recent years.In this study, species-specific and generalized models for aboveground biomass of single trees have been developed for the main 14 species used in reforestations in Spain. A comparison with the existing biomass models at individual tree level for larger trees of these species was also conducted to confirm the hypothesis that specific models are required for the first years of the reforestation.A new set of biomass models was fitted simultaneously per species, based on three different independent variables – root-collar-diameter, total height and crown projection area- and their combinations. The fitted models provide more accurate and unbiased predictions of aboveground biomass in the first years after reforestation when compared with previously existing models for adult trees. The generalized models can be applied to reforestations when the main species is unknown or for mixed plantations where species are not individualized.The set of models provided can be used in a wide variety of surveys and monitoring conditions depending on the available data, from classical field inventories to more novel approaches such as airborne LiDAR data or aerial photographs.

Effects of mixing proportions on carbon storage and allocation in mixed plantation of Larix olgensis and Fraxinus mandshurica

In this study, four types of mixed Larix olgensis and Fraxinus mandshurica plantations were selected according to the rows-mixing proportions (type Ⅰ: 5:3, type Ⅱ: 6:4, type Ⅲ: 5:5, type Ⅳ: 1:1). The see-mingly unrelated biomass models of L. olgensis and F. mandshurica were developed for obtaining biomass values, and the difference and composition of carbon storage in each forest layer and ecosystem were analyzed. The results showed that carbon storage of arbor layer in different stand types was 39.86-50.12 t·hm-2, the carbon storage of arbor layer inⅠ, Ⅱ and Ⅳ was significantly higher than that in type Ⅲ. The carbon storage of understory was 0.10-0.30 t·hm-2, with that in type Ⅱ being significantly higher than other types. Carbon storage of litter layer was 4.43-6.96 t·hm-2, with type Ⅱ and Ⅲ being significantly higher than those of the other types. In the soil layer, carbon storage was 34.97-54.66 t·hm-2. The carbon storage of soil layer in type Ⅱ was significantly greater than those in the other types. At the whole ecosystem level, carbon storage of type Ⅰ-Ⅳ was 90.43, 108.27, 85.83 and 89.92 t·hm-2, respectively. Type Ⅱ had significantly greater carbon storage than the other types. The arbor layer and soil layer were the major carbon pools in the ecosystem, which accounted for 43.3%-55.7% and 38.7%-50.5% of the total, respectively. Our results suggested that mixing by six rows of L. olgensis and four rows of F. mandshurica was better for future planting.

Microwave pyrolysis of biomass model compounds for bio-oil: Formation mechanisms of the nitrogenous chemicals and DFT calculations

Microwave pyrolysis of nitrogen-rich biomass for bio-oil is a green and sustainable pathway to realize energy conversion and produce high-value nitrogenous chemicals. This work selected three typical amino acids (serine (Ser), glutamic acid (Glu), and phenylalanine (Phe)) as nitrogenous model compounds for biomass, and the distribution characteristics and formation mechanisms of the nitrogenous chemicals in bio-oil were studied in depth. Results showed that the amino acid structure and pyrolysis temperature were the crucial factors affecting the distribution and selectivity of nitrogenous chemicals. The nitrogenous chemicals were mainly produced at 200–500 °C from the model amino acids, and the nitrogen in Ser was more prone to accumulating in bio-oil compared with Glu and Phe. At 300 °C, the model amino acids generated numerous amines and cyclic amides through decarboxylation, decarbonylation, and dehydration. Higher temperatures (400 and 500 °C) promoted amines/amides to form nitriles and N-heterocycles, with the releasing of H2. Nitriles (including propionitrile and phenylacetonitrile) were typically produced by the straight-chain dehydrogenation of amines. Ser, Glu, and Phe produced N-heterocycles (including pyrazine, pyrrole, 2-pyrrolidone, and indole) based on the cyclization and dehydration between carbonyl and amino compounds, intramolecular dehydration, and cyclization dehydrogenation of phenethylamine, respectively. Finally, the possible formation pathways of the main nitrogenous chemicals were proposed, and the kinetics and thermodynamic analyses were performed through density functional theory (DFT) calculations. The findings could provide theoretical support for the nitrogenous chemicals production from microwave pyrolysis of amino acids and biomass.

Reducing Rural Fire Risk through the Development of a Sustainable Supply Chain Model for Residual Agroforestry Biomass Supported in a Web Platform: A Case Study in Portugal Central Region with the Project BioAgroFloRes

In the European Mediterranean region, rural fires are a widely known problem that cause serious socio-economic losses and undesirable environmental consequences, including the loss of lives, infrastructures, cultural heritage, and ecosystem services such as carbon sequestration and the provisioning of raw materials. In the last decades, the collapse of the traditional rural socioeconomic systems that once characterized the Mediterranean region, along with land-use changes, have created conflicts and additional driving factors for rural fires. Within Europe, Portugal is the most affected country by rural fires. This work intends to demonstrate the importance of recovering and valorizing residual agroforestry biomass to reduce rural fire risk in Portugal, and thus contributing to a fire resilient landscape. From the results of the known causes of fires in Portugal, it becomes very clear that it is crucial to educate people to end risky behaviors, such as the burning of agroforestry leftovers that causes 27% of fires in Portugal each year. The valorization of the existing energy potential in the lignocellulosic biomass of agroforestry residues favors the reduction of the probability of rural fires, this being the focus of the project BioAgroFloRes—Sustainable Supply Chain Model for Residual Agroforestry Biomass supported in a Web Platform—introduced and explained here.

Dynamic Changes in Leaf Biomass and the Modeling of Individual Moso Bamboo (Phyllostachys edulis (Carrière) J. Houz) under Intensive Management

Accurate estimations of leaf biomass are required to quantify the amount of material and energy exchanged between vegetation and the atmosphere, to enhance the primary productivity of forest stands, and to assess the contributions of vegetation towards the mitigation of global climate change. The leaf biomass of Moso bamboo (Phyllostachys edulis (Carrière) J. Houz) changes dramatically during the year owing to changes in the leaves and the growth of new shoots. Furthermore, the relationship between the leaf biomass of Moso bamboo under cutting the top of the culm and the diameter at breast height (D) and culm height is decoupling, which increases the difficulty of estimating leaf biomass. Consequently, an effective method to accurately estimate the leaf biomass of Moso bamboo under cutting the top of the culm is required. In this study, leaf biomass and other factors (age, D, culm height, crown length, and crown width) were measured for 54 bamboo samples collected from December 2019 to December 2020. Models for predicting the leaf biomass of the Moso bamboo were established using multiple linear regression with two strategies, and their accuracies were evaluated using leave-one-out cross-validation. The results showed that crown length, crown width, and age were highly correlated with leaf biomass, and these were important factors when making estimations. Variation in monthly averaged leaf biomass is significant, with a decreasing trend from January to May and an increasing trend from June to December in off-years. The leaf biomass model that utilized data from the three leaf change periods had a better fit and accuracy, with R2 values of 0.583–0.848 and prediction errors between 8.59% and 24.19%. The model that utilized data for all months had a worse fit and accuracy, with an R2 value of 0.228 and prediction error of 46.79%. The results of this study provide reference data and technical support to help clarify the dynamic changes in Moso bamboo leaf biomass, and therefore, aid in the development of accurate simulations.

Additive Root Biomass Equations for Betula platyphylla Suk. Plantations in Northeast China

Most of the forest biomass models that have been developed so far focus on the study of the aboveground biomass of forest trees and the prediction of belowground biomass remains obviously insufficient. Moreover, most of the existing studies on the estimation of the belowground biomass of trees have considered roots as a whole, ignoring the differences in composition and function of roots within different diameter classes. In this study, we measured the root biomass of birch plantation forests in northeastern China using extensive destructive sampling, in which we divided the root system into three parts: coarse, medium, and fine roots. We selected the best model base form from three common allometric biomass equations and determined the most appropriate error structure for the two sets of models using likelihood comparisons. The additive and disaggregated models were fitted using maximum likelihood with open-source software. We also added the site factor as a dummy variable into the two models. Finally, the competency of the two models was tested using ten-fold cross-validation. The results showed that both models could provide relatively accurate estimates of birch root biomass but that the disaggregated model performed slightly better than the additive model.

Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems.