Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB. Camden has an estimated median AGB density of 51.6 Mg ha–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤ 25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB. Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

中文翻译：

---

使用多尺度激光雷达估算城市地上生物量。


城市树木长期以来因其提供生态系统服务（缓解“热岛”效应、抑制空气污染等）而受到重视；最近，城市森林储存大量地上生物量（AGB）的潜力也得到了认识。然而，由于树木形态的可塑性、物种多样性高以及土地覆盖的异质性和复杂性，城市地区在评估 AGB 时面临着特殊的挑战。遥感，特别是光探测和测距（LiDAR），为通过直接测量树木结构来评估城市 AGB 提供了独特的机会。在这项研究中，地面激光雷达测量被用来为伦敦卡姆登区推导出新的异速测量，其中包含了城市环境中典型的各种树木结构。然后，利用全方位的机载 LiDAR 数据集，通过新的单棵树检测 (ITD) 方法识别了整个行政区的单棵树。新的异速生长随后应用于已识别的树木，生成全行政区 AGB 的估计值。卡姆登的 AGB 密度中值估计为 51.6 Mg ha–1，其中最大的 AGB 密度出现在林地中；陆地激光雷达得出的 AGB 估计表明这些区域与温带和热带森林相当。地面 LiDAR 得出的最大高度和预计树冠面积的多元线性回归解释了树木体积的 93% 差异，突出了这些指标在表征不同树木结构方面的效用。本地衍生的异速生长提供了树木体积的准确估计，而全行政区的异速生长往往高估了林地地区的 AGB。 新的ITD方法成功识别了个体树木；然而，由于 ITD 无法解决树冠重叠问题，与地面 LiDAR 相比，AGB 被低估了 ≤ 25%。蒙特卡罗不确定性分析发现，在估算 AGB 时，指定木材密度值是最大的不确定性来源。预计在未来一个世纪，全球人口将日益城市化，导致城市土地覆盖范围空前扩大。城市地区将变得更加重要，因为因此需要碳汇和评估这些地区碳密度的有效工具。使用多尺度激光雷达提供了实现这一目标的机会，提供了城市森林结构和 AGB 的空间明确地图。