Highlights: What are the main findings?

Tundra vegetation, as well as shrubs, half-shrubs, and low-stature trees (<2 m height), in drylands are the least studied lifeforms in the selected remote sensing literature.
Incorporation of spectral and structural predictors did not improve aboveground biomass model performance within the reviewed studies (n = 50) compared to the use of spectral or structural predictors alone.

What are the implications of the main findings?

As species and structural diversity increase within shrubland or savanna systems, species-specific allometric equations and more complex UAV remote sensing data captures (LiDAR, hyperspectral, multispectral, and RGB) may be necessary to estimate aboveground biomass within REDD+ standards with <10% uncertainty.
Increased research investments should be made into development of allometric models for shrubs and multi-branching tree species based on variables that can be estimated with UAV-mounted sensors and associated models.

Abstract

Global biomass estimates suggest that plants hold 81% of the Earth’s 550 GT C, yet carbon stocks in non-forested and dryland ecosystems remain the largest source of uncertainty in the global carbon budget. Small uncrewed aerial vehicle (UAV) platforms are increasingly used to estimate aboveground biomass at landscape scales. We conducted a systematic review of the remote sensing literature to determine: (1) which plant traits and related remote sensing indicators were used to develop aboveground biomass models; (2) statistical approaches; and (3) the key sources of uncertainty among these methods and models. We found that tundra, dryland, and savanna ecosystems were most underrepresented in the remote sensing literature. Within our systematic review process, we found no consistent UAV sensor combination, platform, or workflow that improved the accuracy and reduced the uncertainty in aboveground biomass estimates. Machine learning and regression models resulted in similar uncertainty levels in shrubland and savanna ecosystems. Expanding allometric equation development in shrublands and savanna ecosystems could reduce uncertainty and improve aboveground biomass estimation. Improved reporting on UAV logistics and workflows would further strengthen comparability. Standardized and validated UAV methods for estimating biomass, carbon stocks, and fuel loads will be essential for producing consistent datasets and enabling robust future meta-analyses.

Shane, T., Waaswa, A., Williams, P., Reeves, M.C., Washington-Allen, R., Perryman, B. 2026, Estimating the Aboveground Biomass of Shrubland and Savanna Ecosystems Using High-Resolution Small UAV Systems: A Systematic Review, Remote Sensing. 2026; 18(6):942

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Extension Associated Contacts

Barry Perryman

Tracy Shane

Andrew Waaswa

Robert Washington-Allen

Perry Williams

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Estimating the Aboveground Biomass of Shrubland and Savanna Ecosystems Using High-Resolution Small UAV Systems: A Systematic Review

Highlights: What are the main findings?

Tundra vegetation, as well as shrubs, half-shrubs, and low-stature trees (<2 m height), in drylands are the least studied lifeforms in the selected remote sensing literature.
Incorporation of spectral and structural predictors did not improve aboveground biomass model performance within the reviewed studies (n = 50) compared to the use of spectral or structural predictors alone.

What are the implications of the main findings?

As species and structural diversity increase within shrubland or savanna systems, species-specific allometric equations and more complex UAV remote sensing data captures (LiDAR, hyperspectral, multispectral, and RGB) may be necessary to estimate aboveground biomass within REDD+ standards with <10% uncertainty.
Increased research investments should be made into development of allometric models for shrubs and multi-branching tree species based on variables that can be estimated with UAV-mounted sensors and associated models.

Abstract

Published by: Shane, T., Waaswa, A., Williams, P., Reeves, M.C., Washington-Allen, R., Perryman, B., 2026, Estimating the Aboveground Biomass of Shrubland and Savanna Ecosystems Using High-Resolution Small UAV Systems: A Systematic Review, Remote Sensing. 2026; 18(6):942