On the site-level suitability of biomass models
Tree biomass estimates in environmental studies are based on allometric models, which are known to vary with species, site, and other forest characteristics. The UNFCCC published a guideline to evaluate the appropriateness of biomass models before application, but it misleads the concept of model suitability and does also allow the selection of models with systematic deviations in the predictions. Here we present an alternative approach based on non-parametric techniques. The approach was tested for pure stands, but this methodology is likewise applicable to mixed forests. The proposed tests perform well in rejecting a model if the predictions for the targeted population are systematically deviant. It is demonstrated that the suitability of an allometric model is a matter of accuracy. The proposed method also allows localizing the model. The presented approach can improve the transparency of global forest monitoring systems and can be implemented with relatively small effort. © 2015 Elsevier Ltd.
| Main Authors: | , , , , , |
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| Format: | journal article biblioteca |
| Language: | eng |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.12792/3439 |
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| Summary: | Tree biomass estimates in environmental studies are based on allometric models, which are known to vary with species, site, and other forest characteristics. The UNFCCC published a guideline to evaluate the appropriateness of biomass models before application, but it misleads the concept of model suitability and does also allow the selection of models with systematic deviations in the predictions. Here we present an alternative approach based on non-parametric techniques. The approach was tested for pure stands, but this methodology is likewise applicable to mixed forests. The proposed tests perform well in rejecting a model if the predictions for the targeted population are systematically deviant. It is demonstrated that the suitability of an allometric model is a matter of accuracy. The proposed method also allows localizing the model. The presented approach can improve the transparency of global forest monitoring systems and can be implemented with relatively small effort. © 2015 Elsevier Ltd. |
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