 |
| Titel |
Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data |
| VerfasserIn |
J. Zhang, S. Huang, E. H. Hogg, V. Lieffers, Y. Qin, F. He |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 10 ; Nr. 11, no. 10 (2014-05-27), S.2793-2808 |
| Datensatznummer |
250117424
|
| Publikation (Nr.) |
 copernicus.org/bg-11-2793-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| Uncertainties in the estimation of tree biomass carbon storage across large
areas pose challenges for the study of forest carbon cycling at regional and
global scales. In this study, we attempted to estimate the present
aboveground biomass (AGB) in Alberta, Canada, by taking advantage of a
spatially explicit data set derived from a combination of forest inventory
data from 1968 plots and spaceborne light detection and ranging (lidar)
canopy height data. Ten climatic variables, together with elevation, were
used for model development and assessment. Four approaches, including spatial
interpolation, non-spatial and spatial regression models, and
decision-tree-based modeling with random forests algorithm (a
machine-learning technique), were compared to find the "best" estimates. We
found that the random forests approach provided the best accuracy for biomass
estimates. Non-spatial and spatial regression models gave estimates similar
to random forests, while spatial interpolation greatly overestimated the
biomass storage. Using random forests, the total AGB stock in Alberta forests
was estimated to be 2.26 × 109 Mg (megagram), with an average
AGB density of 56.30 ± 35.94 Mg ha−1. At the species level,
three major tree species, lodgepole pine, trembling aspen and white spruce,
stocked about 1.39 × 109 Mg biomass, accounting for nearly
62% of total estimated AGB. Spatial distribution of biomass varied with
natural regions, land cover types, and species. Furthermore, the relative
importance of predictor variables on determining biomass distribution varied
with species. This study showed that the combination of ground-based
inventory data, spaceborne lidar data, land cover classification, and
climatic and environmental variables was an efficient way to estimate the
quantity, distribution and variation of forest biomass carbon stocks across
large regions. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|