Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations

2026-06-24

AI Summary

This study addresses the lack of high-resolution, fine-scale quantification of urban tree biomass, which hinders the characterization of individual-tree heterogeneity. The authors propose a self-supervised dual-stream cross-attention network that fuses airborne LiDAR with near-infrared RGB imagery to generate semantic labels, enabling annotation-free crown delineation through multiscale watershed segmentation. Aboveground biomass is then estimated using species-specific allometric equations. The work introduces the first publicly available bitemporal non-forest tree biomass database and incorporates deep ensemble uncertainty maps to guide model refinement. On an independent test set, biomass predictions achieve R² values of 0.570–0.609. Applied to an 810 km² area in Ontario from 2018 to 2023, the approach reveals a net carbon stock increase of 39 Gg C, with localized densities reaching up to 140 Mg/ha.

Abstract

Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse products that cannot resolve individual crowns or fine-scale heterogeneity. We present a crown-level above-ground biomass (AGB) framework for an 810~km$^2$ landscape in Ontario, Canada, using leaf-off airborne LiDAR (8--10~pulses~m$^{-2}$) and near-infrared RGB orthophotography (0.16--0.20~m) from 2018 and 2023. A dual-stream cross-attention network trained on rule-based pseudo-labels produced semantic marks for buildings, needleleaf trees, and deciduous trees, supporting crown delineation and functional-type assignment. On independently annotated withheld tiles, global/mean precision, recall, and Dice scores were 0.86, 0.83, and 0.84. Crowns were delineated with multiscale watershed segmentation in mapped tree areas, and AGB was estimated from a crown area--height power-law proxy calibrated to species-specific allometry (Lambert et al., 2005) for 21,921 inventory trees. For 18,713 inventory--segment matched pairs from a 90,726-tree held-out test set, AGB prediction achieved $R^2=0.609$ using inventory crown geometry and $R^2=0.570$ under operational segmentation, identifying crown delineation as the remaining uncertainty source. Aggregated to 30~m, estimates yielded total AGB stocks of 1.73~Tg in 2018 and 1.81~Tg in 2023 (811--850~Gg~C), local densities up to ${\sim}140$~Mg~ha$^{-1}$ along the Niagara Escarpment, and a net carbon gain of 39~Gg~C over five years. Deep-ensemble uncertainty maps highlighted high-epistemic-uncertainty areas linked to underrepresented land covers and guided assignment of uncertain crowns to a pooled allometric equation. The framework uses standard provincial data, requires no manual annotation, and produces a public bitemporal crown-level AGB database for trees outside forests at management-relevant resolution.

Problem

• urban biomass
• crown-level estimation
• spatially explicit quantification
• tree allometry
• fine-scale heterogeneity

Innovation

• self-supervised learning
• crown-level biomass
• cross-attention network
• multiscale watershed segmentation
• deep ensemble uncertainty