Using lidar data and a height-structured ecosystem model to estimate forest carbon stocks and fluxes over mountainous terrain

Abstract

Accurately predicting forest dynamics and associated carbon fluxes requires both knowledge of the current state of the ecosystem and an understanding of the underlying processes and environmental conditions that influence the ecosystem processes. Here, we apply a combination of light detection and ranging (lidar) remote sensing (LVIS), an individual-based height-structured ecosystem model (ED), and detailed topographic and climate data to address these requirements to predict carbon dynamics at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire. Lidar data provided substantial constraints on model estimates of carbon stocks and annual net ecosystem production (ANEP). Lidar-initialized model estimates of carbon stocks (10.77 kg C m−2) were within 5% of the field estimates over the domain and accounted for the 44% decrease in carbon stocks observed between minimum and maximum elevation at HBEF. Lidar-initialized model estimates of ANEP (0.023 kg C m−2 year−1) also compared favorably with recent field estimates. Model estimates of ANEP strongly depended on fine-scale (1 ha) lidar data on vegetation structure, environmental gradients, and the dynamics of disturbance events. Substituting fine-scale (1 ha) data on vegetation structure and climate with domain-wide inputs increased model estimates of ANEP by 84%. Substituting fine-scale climate data with domain-wide inputs but initializing with fine-scale data on vegetation structure increased estimates of ANEP by 40%. Model simulations initialized with spatial heterogeneity in environmental conditions but that lacked corresponding spatial heterogeneity in vegetation structure were the most problematic because this configuration had serious inconsistencies in areas where the domain mean canopy height exceeded the local potential for vegetation (e.g., at high elevations). Lastly, failing to account for increased natural disturbance rates with elevation increased model estimates of ANEP by 43%. This research demonstrates that the combination of lidar data and a height-structured ecosystem model can be a powerful tool for estimating forest carbon stocks and fluxes, even in complex mountainous environments. To be most useful for constraining model predictions, lidar data need to be at the scale of the underlying environmental heterogeneity that determines plant vital rates.