Related Experiment Video
Updated: Jun 14, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Model error propagation in a compatible tree volume, biomass, and carbon prediction system.
James A Westfall1, Philip J Radtke2, David M Walker2
1U.S. Forest Service, Northern Research Station, York, PA, USA. james.westfall@usda.gov.
Carbon Balance and Management
|June 10, 2025
Summary
This study evaluated a compatible tree volume, biomass, and carbon prediction system. Results show minimal additional uncertainty (under 5%) in population estimates, benefiting forest inventory data users.
Area of Science:
- Forestry
- Ecological modeling
- Biometrics
Background:
- Individual tree attributes like volume, biomass, and carbon are highly correlated.
- Compatible prediction systems are preferred but raise concerns about model error propagation.
- Evaluating uncertainty propagation in tree attribute prediction is crucial for accurate forest inventories.
Purpose of the Study:
- To assess how model prediction uncertainty propagates through a compatible tree volume, biomass, and carbon prediction system.
- To examine the contribution of model uncertainty to population estimates.
- To determine the reliability of compatible prediction frameworks for forest inventory.
Main Methods:
- A compatible prediction system for tree volume, biomass, and carbon was evaluated.
- Error propagation from volume to biomass and then to carbon was analyzed.
- Uncertainty in population estimates was quantified based on model predictions.
Main Results:
- Uncertainty increased from volume to biomass to carbon, with carbon being most affected by error propagation.
- Tree branches exhibited higher model uncertainty than stem components due to data limitations.
- Direct prediction of whole tree biomass and harmonized components reduced uncertainty compared to constituent parts.
Conclusions:
- Increases in the standard error of population estimates due to model uncertainty were consistently low (<5%, usually <3%).
- Forest inventory data users can rely on compatible prediction systems with minimal added uncertainty.
- The implicit compatibility among tree volume, biomass, and carbon attributes offers benefits without significant uncertainty increase.
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