دورية أكاديمية
Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data
| العنوان: | Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data |
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| المؤلفون: | J. Zhang, S. Huang, E. H. Hogg, V. Lieffers, Y. Qin, F. He |
| المصدر: | Biogeosciences, Vol 11, Iss 10, Pp 2793-2808 (2014) |
| بيانات النشر: | Copernicus Publications, 2014. |
| سنة النشر: | 2014 |
| المجموعة: | LCC:Ecology LCC:Life LCC:Geology |
| مصطلحات موضوعية: | Ecology, QH540-549.5, Life, QH501-531, Geology, QE1-996.5 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1726-4170 1726-4189 |
| Relation: | http://www.biogeosciences.net/11/2793/2014/bg-11-2793-2014.pdf; https://doaj.org/toc/1726-4170; https://doaj.org/toc/1726-4189 |
| DOI: | 10.5194/bg-11-2793-2014 |
| URL الوصول: | https://doaj.org/article/ea7188c707554f778c79456e61a1bacb |
| رقم الأكسشن: | edsdoj.7188c707554f778c79456e61a1bacb |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17264170 17264189 |
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| DOI: | 10.5194/bg-11-2793-2014 |