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- W2495264522 abstract "Forests are a major component in the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne LiDAR is increasingly recognized as outstanding data source for high-fidelity mapping of carbon stocks at regional scales. Most LiDAR-based approaches to carbon mapping work with summary statistics derived from the point cloud within pixels, which are calibrated with field-plot data. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual-tree carbon stocks are calculated." @default.
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- W2495264522 date "2015-01-01" @default.
- W2495264522 modified "2023-09-27" @default.
- W2495264522 title "Tree-centric mapping of forest carbon density from airborne LiDAR and hyperspectral data" @default.
- W2495264522 hasPublicationYear "2015" @default.
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