Are inventory based and remotely sensed above-ground biomass estimates consistent?

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Carbon emissions from deforestation and forest degradation are poorly quantified.
• Uncertainty in above-ground biomass (AGB) estimates is a major knowledge gap.
• Sophisticated remote sensing methods are assumed to improve AGB accuracy.

Purpose of the Study:

• To evaluate if increased methodological sophistication in AGB estimation improves precision.
• To assess the accuracy and precision of AGB density (AGBD) estimates across different scales.
• To identify limitations in current AGB estimation techniques.

Main Methods:

• Calculated and compared AGBD estimates from nine studies with varying methodological sophistication.
• Analyzed estimates at continental (Africa), national (Mozambique), and local (1160 km²) scales.
• Assessed precision by grouping studies, as accuracy cannot be determined without true values.

Main Results:

• Poor precision in AGBD estimates was found at all scales, even among similar methodologies.
• Mean AGBD estimates showed wide variations: Africa (19.9–44.3 Mg ha⁻¹), Mozambique (12.7–68.3 Mg ha⁻¹), local area (35.6–102.4 Mg ha⁻¹).
• Increased methodological sophistication did not improve estimate precision; uncertainty estimates were often smaller than inter-study differences.

Conclusions:

• Current sophisticated methods, including remote sensing, do not guarantee improved AGBD precision or accuracy.
• Inadequate uncertainty quantification masks potential improvements and hinders reliable carbon emission calculations.
• There is a critical need for more robust AGBD estimation methods and comprehensive uncertainty assessments.