Accurate estimation of forest aboveground biomass at high-resolution continues to remain a challenge and long-term goal for carbon monitoring and accounting systems. Here, we present an exhaustive evaluation and validation of a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms. We developed this framework over multiple phases of bottom-up monitoring efforts within NASA’s Carbon Monitoring Program. Lidar data were collected by different local and federal agencies and provided a wall-to-wall coverage of three states in the USA (Maryland, Pennsylvania and Delaware with a total area of 157 865 km²). We generated a set of standardized forestry metrics from lidar-derived imagery (i.e. canopy height model, CHM) to minimize inconsistency of data quality. We then estimated plot-scale biomass from field data that had the closet acquisition time to lidar data, and linked to lidar metrics using Random Forest models at four USDA Forest Service ecological regions. Additionally, we examined pixel-scale errors using independent field plot measurements across these ecoregions. Collectively, we estimate a total of ∼680 Tg C in aboveground biomass over the Tri-State region (13 DE, 103 MD, 564 PA) circa 2011. A comparison with existing products at pixel-, county-, and state-scale highlighted the contribution of trees over ‘non-forested’ areas, including urban trees and small patches of trees, an important biomass component largely omitted by previous studies due to insufficient spatial resolution. Our results indicated that integrating field data and low point density (∼1 pt m⁻²) airborne lidar can generate large-scale aboveground biomass products at an accuracy close to mainstream lidar forestry applications (R² = 0.46–0.54, RMSE = 51.4–54.7 Mg ha⁻¹; andR² = 0.33–0.61, RMSE = 65.3–100.9 Mg ha⁻¹; independent validation). Local, high-resolution lidar-derived biomass maps such as products from this study, provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale mapping efforts and future development of a national carbon monitoring system.

Disturbances, both natural and anthropogenic, are critical determinants of forest structure, function, and distribution. The vulnerability of forests to potential changes in disturbance rates remains largely unknown. Here, we developed a framework for quantifying and mapping the vulnerability of forests to changes in disturbance rates. By comparing recent estimates of observed forest disturbance rates over a sample of contiguous US forests to modeled rates of disturbance resulting in forest loss, a novel index of vulnerability, Disturbance Distance, was produced. Sample results indicate that 20% of current US forestland could be lost if disturbance rates were to double, with southwestern forests showing highest vulnerability. Under a future climate scenario, the majority of US forests showed capabilities of withstanding higher rates of disturbance then under the current climate scenario, which may buffer some impacts of intensified forest disturbance.

Bioenergy with carbon capture and storage (BECCS) has been proposed as a potential climate mitigation strategy raising concerns over trade‐offs with existing ecosystem services. We evaluate the feasibility of BECCS in the Upper Missouri River Basin (UMRB), a landscape with diverse land use, ownership, and bioenergy potential. We develop land‐use change scenarios and a switchgrass (Panicum virgatum L.) crop functional type to use in a land‐surface model to simulate second‐generation bioenergy production. By the end of this century, average annual switchgrass production over the UMRB ranges from 60 to 210 Tg dry mass/year and is dependent on the Representative Concentration Pathway for greenhouse gas emissions and on land‐use change assumptions. Under our simple phase‐in assumptions this results in a cumulative total production of 2,000–6,000 Tg C over the study period with the upper estimates only possible in the absence of climate change. Switchgrass yields decreased as average CO₂ concentrations and temperatures increased, suggesting the effect of elevated atmospheric CO₂ was small because of its C4 photosynthetic pathway. By the end of the 21st century, the potential energy stored annually in harvested switchgrass averaged between 1 and 4 EJ/year assuming perfect conversion efficiency, or an annual electrical generation capacity of 7,000–28,000 MW assuming current bioenergy efficiency rates. Trade‐offs between bioenergy and ecosystem services were identified, including cumulative direct losses of 1,000–2,600 Tg C stored in natural ecosystems from land‐use change by 2090. Total cumulative losses of ecosystem carbon stocks were higher than the potential ~300 Tg C in fossil fuel emissions from the single largest power plant in the region over the same time period, and equivalent to potential carbon removal from the atmosphere from using biofuels grown in the same region. Numerous trade‐offs from BECCS expansion in the UMRB must be balanced against the potential benefits of a carbon‐negative energy system.

There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.