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| Titel |
Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data |
| VerfasserIn |
S. O. Los, J. A. B. Rosette, N. Kljun, P. R. J. North, L. Chasmer, J. C. Suárez, C. Hopkinson, R. A. Hill, E. Gorsel, C. Mahoney, J. A. J. Berni |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 5, no. 2 ; Nr. 5, no. 2 (2012-03-27), S.413-432 |
| Datensatznummer |
250002449
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| Publikation (Nr.) |
 copernicus.org/gmd-5-413-2012.pdf |
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| Zusammenfassung |
We present new coarse resolution (0.5° × 0.5°) vegetation
height and vegetation-cover fraction data sets between 60° S and
60° N for use in climate models and ecological models. The data sets
are derived from 2003–2009 measurements collected by the Geoscience Laser
Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite
(ICESat), the only LiDAR instrument that provides close to global coverage.
Initial vegetation height is calculated from GLAS data
using a development of the model of Rosette
et al. (2008) with with further
calibration on desert sites. Filters are developed to identify and eliminate
spurious observations in the GLAS data, e.g. data that are affected by
clouds, atmosphere and terrain and as such result in erroneous estimates of
vegetation height or vegetation cover. Filtered GLAS vegetation height
estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals
for each 0.5° × 0.5°. The GLAS vegetation height product is
evaluated in four ways. Firstly, the Vegetation height data and data filters
are evaluated using aircraft LiDAR measurements of the same for ten sites in
the Americas, Europe, and Australia. Application of filters to the GLAS
vegetation height estimates increases the correlation with aircraft data from
r = 0.33 to r = 0.78, decreases the root-mean-square error by a factor 3
to about 6 m (RMSE) or 4.5 m (68% error distribution) and decreases the
bias from 5.7 m to −1.3 m.
Secondly, the global aggregated
GLAS vegetation height product is tested for sensitivity towards the choice
of data quality filters; areas with frequent cloud cover and areas with steep
terrain are the most sensitive to the choice of thresholds for the filters.
The changes in height estimates by applying different filters are, for the
main part, smaller than the overall uncertainty of 4.5–6 m established from
the site measurements. Thirdly, the GLAS global vegetation height product is
compared with a global vegetation height product typically used in a climate
model, a recent global tree height product, and a vegetation greenness
product and is shown to produce realistic estimates of vegetation height.
Finally, the GLAS bare soil cover fraction is compared globally with the
MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction
estimates derived from AVHRR NDVI data (r = 0.67); the GLAS tree-cover
fraction is compared with the MODIS tree-cover fraction (r = 0.79). The
evaluation indicates that filters applied to the GLAS data are conservative
and eliminate a large proportion of spurious data, while only in a minority
of cases at the cost of removing reliable data as well.
The new GLAS vegetation height product appears more realistic than previous
data sets used in climate models and ecological models and hence should
significantly improve simulations that involve the land surface. |
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