Imaging spectroscopy- and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics

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Abstract:

The composition and structure of vegetation are key attributes of ecosystems, affecting their current and future carbon, water, and energy fluxes. Information on these attributes has traditionally come from ground-based inventories of the plant canopy within small sample plots. Here we show how imaging spectrometry and waveform lidar can be used to provide spatially comprehensive estimates of forest canopy composition and structure that can improve the accuracy of the carbon flux predictions of a size-structured terrestrial biosphere model, reducing its root-mean-square errors from 85%-104% to 37%-57%. The improvements are qualitatively and quantitatively similar to those obtained from simulations initialized with ground measurements and approximately double the estimated rate of ecosystem carbon uptake as compared to a potential vegetation simulation. These results suggest that terrestrial biosphere model simulations can utilize modern remote-sensing data on vegetation composition and structure to improve their predictions of the current and near-term future functioning of the terrestrial biosphere.Key PointsPredictions of forest change hampered by errors in current model formulations Remote Sensing can derive fine-scale information on the current ecosystem state Regional carbon fluxes can be constrained using remote sensing derived info

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Af8rjTimes Cited:2Cited References Count:39