Geography

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Author: search.filters.author.Ma, Lei

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    Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling
    (Wiley, 2023-04-04) Ma, Lei; Hurtt, George; Tang, Hao; Lamb, Rachel; Lister, Andrew; Chini, Louise; Dubayah, Ralph; Armston, John; Campbell, Elliott; Duncanson, Laura; Healey, Sean; O'Neil-Dunne, Jarlath; Ott, Lesley; Poulter, Benjamin; Shen, Quan
    Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.
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    Changing cropland in changing climates: quantifying two decades of global cropland changes
    (Institute of Physics, 2023-05-12) Kennedy, Jennifer; Hurtt, George C.; Liang, Xin-Zhong; Chini, Louise; Ma, Lei
    Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.
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    Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics
    (2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.