用机载激光与遥感影像自动估算城市树木生物量，精度达60%以上。
Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations
- 用双流注意力网络结合伪标签，自动识别树种和建筑区域
- 在9万棵树上验证，生物量预测$R^2$达0.609，五年碳储量增39吉克
- 无需人工标注，可生成公开的城市树冠级生物量数据集
城市树木生物量的精细量化仍远落后于管理林区，因多数估算依赖普查或粗分辨率产品，难以分辨单个树冠和细尺度异质性。本文基于加拿大安大略省810平方公里区域的2018与2023年机载激光雷达（8–10脉冲/平方米）与近红外真彩色正射影像（0.16–0.20米分辨率），提出一种树冠级地上生物量（AGB）框架。采用基于规则的伪标签训练双流交叉注意力网络，实现建筑物、针叶树与阔叶树的语义标记，支持树冠分割与功能类型判定。在独立标注的保留图块上，全局精确率、召回率与Dice分数分别为0.86、0.83和0.84。通过多尺度分水岭分割在识别出的树区划定树冠，并利用21,921棵实测树的物种特异性生物量方程（Lambert et al., 2005）校准冠幅-高度幂律代理模型。对90,726棵树中18,713对匹配样本进行测试，基于实测冠形几何的预测$R^2=0.609$，实际分割下$R^2=0.570$，表明树冠分割仍是主要不确定性来源。聚合至30米分辨率后，2018年总生物量为1.73太克，2023年增至1.81太克（811–850吉克碳），尼加拉悬崖沿线局部密度达约140兆克/公顷，五年净碳增加39吉克。深度集成不确定性图揭示高认知不确定性区域，与代表性不足的地表覆盖相关，指导不确定树冠归入统一生物量方程。该框架仅使用省级标准数据，无需人工标注，生成可用于管理决策的公开双时相树冠级非林地生物量数据库。
原文摘要 · Abstract (English)
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.
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