Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

Abstract

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.

Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates

“…Overall, we show several lines of evidence (tree DBH vs. leaf traits, tree DBH vs. spectroscopy, RS traits vs. field biomass, RS traits vs. field NPP/GPP) that traits can slightly improve estimates of tropical forest biomass and fluxes and possibly may be further improved in the future with data from new satellite missions like SBG. Other potential improvements in remote biomass estimates might come from integrating dynamic vegetation models that have trait data with GEDI observations (Ma et al, 2023).…”

Vertical Characteristics of Vegetation Distribution in Wuyishan National Park Based on Multi-Source High-Resolution Remotely Sensed Data

“…The limited number of samples for different tree species and the capability of high-resolution multispectral data to identify tree species are key limiting factors for achieving fine-scale tree species recognition. With the advancement of satellite and airborne hyperspectral remote sensing, as well as the availability of a large number of tree species training samples and high-resolution DEM obtained from nearsurface remote sensing such as an unmanned aerial vehicle (UAV), it becomes possible to achieve finer-scale tree species classification [49][50][51].…”

Assessment of Phytomass and Carbon Stock in the Ecosystems of the Central Forest Steppe of the East European Plain: Integrated Approach of Terrestrial Environmental Monitoring and Remote Sensing with Unmanned Aerial Vehicles

“…Knowledge in the field of forest carbon accounting is currently increasing. However, different methodological approaches [38][39][40][41][42][43] and computational models [44][45][46][47][48] have been found to produce inconsistent results, highlighting the need for further methodological improvements.…”