Forest biomass is increasingly important for calibrating worldwide carbon changes and ensuring sustainable forest management. However, there are no consistent standards for aboveground biomass (AGB) estimation methods. Direct field estimation is costly and destructive. We explored alternative methods for estimating AGB based on different sources of ground-based remote sensing data. We compared alternative methods for estimating AGB based on different sources of ground-based remote sensing data. We compared allometric equations derived from metrics extracted from terrestrial laster scanning (TLS) to equations derived from metrics extracted from spherical images. Spherical image metrics consistently performed better than TLS metrics. Alternatively, we developed sector subsample selection methods that utilized only measurements from spherical photos with a smaller subsample of angle sample counts to correct for tree occlusion. The sector subsampling methods were comparable to widely used big BAF subsampling and were much more efficient for estimating AGB than the allometric equations. Sector subsampling has great potential to reduce costs for AGB estimation and enabling access to monetized carbon markets.
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