Regional carbon predictions in a temperate forest using satellite lidar

Antonarakis, Alexander and Guizar Coutino, Alejandro (2017) Regional carbon predictions in a temperate forest using satellite lidar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (11). pp. 4954-4960. ISSN 1939-1404

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Large uncertainties in terrestrial carbon stocks and sequestration predictions result from insufficient regional data characterizing forest structure. This study uses satellite waveform lidar from ICESat to estimate regional forest structure in central New England, where each lidar waveform estimates fine-scale forest heterogeneity. ICESat is a global sampling satellite, but does not provide wall-to-wall coverage. Comprehensive, wall-to-wall ecosystem state characterization is achieved through spatial extrapolation using the random forest machine-learning algorithm. This forest description allows for effective initialization of individual-based terrestrial biosphere models making regional carbon flux predictions. Within 42/43.5 N and 73/71.5 W, aboveground carbon was estimated at 92.47 TgC or 45.66 MgC ha−1, and net carbon fluxes were estimated at 4.27 TgC yr−1 or 2.11 MgC ha−1 yr−1. This carbon sequestration potential was valued at 47% of fossil fuel emissions in eight central New England counties. In preparation for new lidar and hyperspectral satellites, linking satellite data and terrestrial biosphere models are crucial in improving estimates of carbon sequestration potential counteracting anthropogenic sources of carbon.

Item Type: Article
Schools and Departments: School of Global Studies > Geography
Research Centres and Groups: climate@sussex
Subjects: G Geography. Anthropology. Recreation > GB Physical geography
Q Science > QK Botany > QK0900 Plant ecology
Related URLs:
Depositing User: Alexander Antonarakis
Date Deposited: 08 Sep 2017 12:40
Last Modified: 27 Nov 2017 12:49

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Project NameSussex Project NumberFunderFunder Ref
Predictions of Current Carbon Stocks in a Temperate Forest4290Sussex Research Development FundUnset