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Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA’s Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms ( r ^2 = 0.38, bias = −2.3, RMSE = 45.2 Mg ha ^−1 ), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density ( r ^2 = 0.32, bias = 4.0, RMSE = 49.4 Mg ha ^−1 ) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA’s spaceborne lidar missions.

Afforestation and reforestation have the potential to provide effective climate mitigation through forest carbon sequestration. Strategic reforestation activities, which account for both carbon sequestration potential (CSP) and economic opportunity, can provide attractive options for policymakers who must manage competing social and environmental goals. In particular, forest carbon pricing can incentivize reforestation on private land, but this may require landholders to forego other profits. Here, we utilize an ambitious geospatial approach to quantify economic opportunities for reforestation in the state of Maryland (USA) based on high-resolution remoting sensing, ecosystem modeling, and economic analysis. Our results identify spatially-explicit areas of economic opportunity where the potential revenue from forest carbon outcompetes the expected profit of existing cropland at the hectare scale. Specifically, we find that under a baseline economic scenario of $20 per ton of carbon (5% rental rate) and decadal average crop profitability, a transition to forest on agricultural land would be more profitable than 23.2% of cropland in Maryland under a 20 year land-use commitment. Accounting for variations in carbon and crop pricing, 5.5%–55.4% of cropland would be immediately outcompeted by expected forest carbon revenue, with the potential for an additional 0.5%–10.6% of outcompeted cropland within 20 years. Under the baseline economic scenario, an annual allocation of $5.8 million towards a carbon rental program could protect 6.93 Tg C (3.4% of the state’s total remaining CSP) on reforested croplands. This moderate yearly cost is equal to 9.7% of Maryland’s average annual auction proceeds from participation in the Regional Greenhouse Gas Initiative (between 2014 and 2018), and 19.3% of the average annual subsidy payments for corn, soy, and wheat allocated over the same period. This methodological approach may be useful for state governments, not-for-profit organizations, or regional climate initiatives interested in identifying strategic areas for reforestation.