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| Titel |
High-resolution mapping of forest carbon stocks in the Colombian Amazon |
| VerfasserIn |
G. P. Asner, J. K. Clark, J. Mascaro, G. A. Galindo García, K. D. Chadwick, D. A. Navarrete Encinales, G. Paez-Acosta, E. Cabrera Montenegro, T. Kennedy-Bowdoin, Á. Duque, A. Balaji, P. Hildebrand, L. Maatoug, J. F. Phillips Bernal, A. P. Yepes Quintero, D. E. Knapp, M. C. García Dávila, J. Jacobson, M. F. Ordóñez |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 9, no. 7 ; Nr. 9, no. 7 (2012-07-25), S.2683-2696 |
| Datensatznummer |
250007193
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| Publikation (Nr.) |
 copernicus.org/bg-9-2683-2012.pdf |
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| Zusammenfassung |
| High-resolution mapping of tropical forest carbon stocks can assist forest
management and improve implementation of large-scale carbon retention and
enhancement programs. Previous high-resolution approaches have relied on
field plot and/or light detection and ranging (LiDAR) samples of aboveground
carbon density, which are typically upscaled to larger geographic areas
using stratification maps. Such efforts often rely on detailed vegetation
maps to stratify the region for sampling, but existing tropical forest maps
are often too coarse and field plots too sparse for high-resolution carbon
assessments. We developed a top-down approach for high-resolution carbon
mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon –
a remote landscape seldom documented. We report on three advances for
large-scale carbon mapping: (i) employing a universal approach to airborne
LiDAR-calibration with limited field data; (ii) quantifying environmental
controls over carbon densities; and (iii) developing stratification- and
regression-based approaches for scaling up to regions outside of LiDAR
coverage. We found that carbon stocks are predicted by a combination of
satellite-derived elevation, fractional canopy cover and terrain ruggedness,
allowing upscaling of the LiDAR samples to the full 16.5 million ha region.
LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and
the regional map based on stratification has 28% uncertainty in any given
hectare. High-resolution approaches with quantifiable pixel-scale
uncertainties will provide the most confidence for monitoring changes in
tropical forest carbon stocks. Improved confidence will allow resource
managers and decision makers to more rapidly and effectively implement
actions that better conserve and utilize forests in tropical regions. |
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