Frances Marie Panday

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.

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The scale of wildfires and their associated smoke plumes have increased in the last four decades and have exacerbated the health risks for people living within wildfire-prone areas. While satellite imagery enables us to monitor the location, abundance, and severity of smoke, datasets such as NOAA’s Hazard Mapping System (HMS) smoke product are unvalidated and may not reflect ground-level smoke conditions. We propose that airports monitoring the presence of smoke may offer a localized constraint on satellite smoke products. Our research aims to validate NOAA’s HMS product by comparing how well the HMS smoke data agree with NOAA’s Integrated Surface Database (ISD) of local, hourly airport data from 2010 to 2020 across the US. Our spatial and temporal analysis shows that the trend in the number of smoke days is similar between the two datasets. HMS smoke plumes are qualitatively categorized into 3 densities: light, medium, and heavy. Particularly, HMS smoke plumes with “medium or heavy” densities have the strongest correlation of r = 0.60 and a mean absolute error of 0.73. We observe the greatest difference in the airport and HMS-derived mean smoke days across the west and east coast, especially in California’s Central Valley and eastern Washington. However, there is a very weak correlation of r = 0.17 between surface-level mean smoke days and from HMS “heavy, medium, or light” density smoke days. Further, using all HMS smoke densities tend to overestimate the trends in and magnitude of smoke days. Based on these results, NOAA’s HMS product is somewhat consistent with trends observed at airports, but caution should be used in assuming equivalence with surface conditions in research investigating wildfire smoke and its consequences for public health.