Rachel L. Lamb

Carbon monitoring is needed to track the exchange of carbon between the land and atmosphere. However, comprehensive monitoring of terrestrial ecosystems is challenging, partially because of the fine-scale spatial heterogeneity of land cover and vegetation structure. While forest ecosystems are relatively well studied, knowledge of trees outside forests (TOFs) is limited, even though TOF contribute substantially to many ecosystem services. In this study, we leveraged advances in remote sensing and modeling to quantify TOF and their contribution to the 2011–2023 Maryland state carbon budget. Specifically, the Ecosystem Demography model was initialized with 1 m airborne optical and LiDAR data. These datasets were combined with 30 m National Land Cover Database maps to identify and monitor TOF statewide. In the base year, TOF contributed to 22.86% (2795.59 km ² ) of the total tree cover, 14.70% (15.98 Tg C) of the total aboveground live tree carbon stocks, and 22.56% (0.39 Tg C yr ⁻¹ ) of the corresponding carbon fluxes in the state, with variation by land cover class. TOF on developed and planted/cultivated lands contributed approximately 90% of the total TOF tree cover and carbon stocks, as well as over 96% of the total TOF carbon fluxes. Most TOF occurred as polygons smaller than 1 km ² and were within 600 m distance from forests. From 2011–2023, persistent TOF carbon stocks were estimated to increase by 5.15 Tg C, representing 23.71% of the state’s total increase in tree carbon stocks over the interval, with substantial interannual variability in carbon fluxes. These results suggest TOF are an important contribution to statewide tree cover and produce carbon stocks and fluxes that should be highlighted in carbon budgets for effective monitoring and assessment. Moving forward, efforts are needed to expand the availability and application of high-resolution remote sensing data and models needed for TOF quantification in carbon budgets.

Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously, we developed a new forest carbon modeling system that combined high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning, we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984–2016 at 30 m resolution and provided an extension to 2023. Statewide, forested land had an average annual net above ground carbon sink of 1.37 TgC yr⁻¹, comparable to prior estimates. However, unlike the prior estimates, there was considerable variation around this mean. The statewide net above ground flux ranged interannually from −0.65 to 2.77 Tg C yr⁻¹. At the county scale, the average annual net above ground flux ranged spatially from 0.01 to 0.13 Tg C yr⁻¹ and spatiotemporally from −0.43 to 0.24 Tg C yr⁻¹. Attribution analyses indicate the primary importance of persistent and regrowing forests, vegetation structure, local disturbance, and rising CO₂ to the mean flux, and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning, reporting and assessment. With this approach, it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning.

A document by Rachel Lamb. Click on the document to view its contents.

A document by Rachel Lamb. Click on the document to view its contents.

The United States STEM workforce has yet to reflect the demographics of the larger population. This discrepancy begins at the base of the STEM pipeline with a significant lack of minority STEM K-12 teachers to serve as mentors and role models to minority students. Research has shown that minority students’ exposure to same-race teachers increased academic output and education attainment up to 32%. Unfortunately, minority teachers face a revolving-door effect: the cycle of increased recruitment countered by a high turnover amongst minority teachers compared to their white counterparts. Minority teachers who leave the profession consistently cite negative teaching environments, discrimination, and lack of support as the main drivers of their decision to quit teaching. The Maryland state legislature recently passed the Blueprint for Maryland’s Future Act, which attempts to address teacher recruitment and retention more comprehensively. Here, we go beyond the Blueprint’s baseline tools to recommend targeted strategies to recruit and retain minority STEM K-12 teachers in Maryland. Through the creation of a robust peer mentorship pipeline between new and experienced teachers, prioritization of school staff diversity and inclusion training, and the promotion of teacher autonomy, we will increase minority student education attainment and encourage the growth of a diverse STEM workforce in Maryland.

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.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group, we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative region of the Eastern U.S. For each state we reviewed the (a) policy framework for climate mitigation, (b) GHG reduction goals, (c) inclusion of forest activities in the state’s climate action plan, (d) existing science used to quantify forest carbon estimates, and (e) stated needs for forest carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.

The inclusion of forest carbon in climate change mitigation planning requires the development of models able to project potential future carbon stocks—a step beyond traditional monitoring, reporting and verification frameworks. Here, we updated and expanded a high-resolution forest carbon modelling approach previously developed for the state of Maryland to 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain, which includes Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. In this study, we employ an updated version of the Ecosystem Demography (ED) model, an improved lidar initialization strategy, and an expanded calibration/validation approach. High resolution (90 m) wall-to-wall maps of present aboveground carbon, aboveground carbon sequestration potential, aboveground carbon sequestration potential gap (CSPG), and time to reach sequestration potential were produced over the RGGI domain where airborne lidar data were available, including 100% of eight states, 62% of Maine, 12% of New Jersey, and 0.65% of New York. For the eight states with complete data, an area of 228 552 km², the contemporary forest aboveground carbon stock is estimated to be 1134 Tg C, and the forest aboveground CSPG is estimated to be larger at >1770 Tg C. Importantly, these estimates of the potential for added aboveground carbon sequestration in forests are spatially resolved, are further partitioned between continued growth of existing trees and new afforested/reforested areas, and include time estimates for realization. They are also assessed for sensitivity to potential changes in vegetation productivity and disturbance rate in response to climate change. The results from this study are intended as input into regional, state, and local planning efforts that consider future climate mitigation in forests along with other land-use considerations.

Longstanding friction between socioeconomic development and biodiversity conservation has become increasingly untenable in an unprecedented era of anthropogenic global environmental change. Despite numerous and high-profile attempts at better integrating these often-competing priorities, unresolved tensions remain as a result of dueling worldviews and approaches: much of development tends to be anthropocentric (centered on humans) while much of conservation remains eco/biocentric (centered on the bio/ecosphere). We offer theocentrism as a biblically normative and conceptually effective way to transcend and reconcile these conflicting worldviews. After providing a brief biblical overview of theocentrism, we highlight seven theoretical implications of this worldview. We then offer four practical applications for faith-based organizations along with examples of Christian groups that are seeking to more holistically integrate conservation and development in their work around the world.

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.