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| Titel |
Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR |
| VerfasserIn |
J. Jubanski, U. Ballhorn, K. Kronseder, J. Franke, F. Siegert |
| 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 ; 10, no. 6 ; Nr. 10, no. 6 (2013-06-17), S.3917-3930 |
| Datensatznummer |
250018292
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| Publikation (Nr.) |
 copernicus.org/bg-10-3917-2013.pdf |
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| Zusammenfassung |
| Quantification of tropical forest above-ground biomass (AGB) over large
areas as input for Reduced Emissions from Deforestation and forest
Degradation (REDD+) projects and climate change models is challenging.
This is the first study which attempts to estimate AGB and its variability
across large areas of tropical lowland forests in Central Kalimantan
(Indonesia) through correlating airborne light detection and ranging (LiDAR)
to forest inventory data. Two LiDAR height metrics were analysed, and
regression models could be improved through the use of LiDAR point densities
as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density
per square metre of about 4 resulted in the best cost / benefit ratio. We
estimated AGB for 600 km of LiDAR tracks and showed that there exists a
considerable variability of up to 140% within the same forest type due to
varying environmental conditions. Impact from logging operations and the
associated AGB losses dating back more than 10 yr could be assessed by LiDAR
but not by multispectral satellite imagery. Comparison with a Landsat
classification for a 1 million ha study area where AGB values were based on
site-specific field inventory data, regional literature estimates, and
default values by the Intergovernmental Panel on Climate Change (IPCC)
showed an overestimation of 43%, 102%, and 137%, respectively. The
results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission
estimates due to deforestation in climate models. For REDD+ projects this
leads to inaccurate carbon stock estimates and consequently to significantly
wrong REDD+ based compensation payments. |
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