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| Titel |
Detecting tropical forest biomass dynamics from repeated airborne lidar measurements |
| VerfasserIn |
V. Meyer, S. S. Saatchi, J. Chave, J. W. Dalling, S. Bohlman, G. A. Fricker, C. Robinson, M. Neumann, S. Hubbell |
| 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. 8 ; Nr. 10, no. 8 (2013-08-14), S.5421-5438 |
| Datensatznummer |
250085294
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| Publikation (Nr.) |
 copernicus.org/bg-10-5421-2013.pdf |
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| Zusammenfassung |
| Reducing uncertainty of terrestrial carbon cycle depends strongly on the
accurate estimation of changes of global forest carbon stock. However, this
is a challenging problem from either ground surveys or remote sensing
techniques in tropical forests. Here, we examine the feasibility of
estimating changes of tropical forest biomass from two airborne
lidar measurements of forest height acquired about 10 yr apart
over Barro Colorado Island (BCI), Panama. We used the forest inventory data
from the 50 ha Center for Tropical Forest Science (CTFS) plot collected
every 5 yr during the study period to calibrate the estimation. We compared
two approaches for detecting changes in forest aboveground biomass (AGB):
(1) relating changes in lidar height metrics from two sensors directly to
changes in ground-estimated biomass; and (2) estimating biomass from each
lidar sensor and then computing changes in biomass from the difference of two
biomass estimates, using two models, namely one model based on five relative
height metrics and the other based only on mean canopy height (MCH). We
performed the analysis at different spatial scales from 0.04 ha to 10 ha.
Method (1) had large uncertainty in directly detecting biomass changes at
scales smaller than 10 ha, but provided detailed information about changes
of forest structure. The magnitude of error associated with both the mean
biomass stock and mean biomass change declined with increasing spatial
scales. Method (2) was accurate at the 1 ha scale to estimate AGB stocks
(R2 = 0.7 and RMSEmean = 27.6 Mg ha−1).
However, to predict biomass changes, errors became comparable to ground
estimates only at a spatial scale of about 10 ha or more. Biomass changes
were in the same direction at the spatial scale of 1 ha in 60 to 64% of
the subplots, corresponding to p values of respectively 0.1 and 0.033.
Large errors in estimating biomass changes from lidar data resulted from the
uncertainty in detecting changes at 1 ha from ground census data,
differences of approximately one year between the ground census and lidar
measurements, and differences in sensor characteristics. Our results indicate
that the 50 ha BCI plot lost a significant amount of biomass
(−0.8 Mg ha−1 yr−1 ± 2.2(SD)) over the past decade
(2000–2010). Over the entire island and during the same period, mean
AGB change was 0.2 ± 2.4 Mg ha−1 yr−1 with old growth
forests losing −0.7 Mg ha−1 yr−1 ± 2.2 (SD), and
secondary forests gaining +1.8 Mg ha yr−1 ± 3.4 (SD) biomass.
Our analysis suggests that repeated lidar surveys, despite taking measurement
with different sensors, can estimate biomass changes in old-growth tropical
forests at landscape scales (>10 ha). |
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