Estimating urban above ground biomass with multi-scale LiDAR

Abstract

BackgroundUrban 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.ResultsCamden 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.ConclusionOver 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.

Terrestrial LiDAR: a three‐dimensional revolution in how we look at trees

“…(b) Terrestrial laser scanning ( TLS ) scans of plane trees ( Platanus × Hispanica ) in Russell Square, London ( UK ), showing height and mass (t), after Wilkes et al . (). (c) TLS scans of two coastal redwoods ( Sequoia sempervirens ), Armstrong State Park, CA ( USA ), over 60 m tall.…”

Developing General Equations for Urban Tree Biomass Estimation with High-Resolution Satellite Imagery

“…Such equations have been developed for 35 natural forest-grown genera on a national scale [39,40]. However, studies focused on developing allometric equations for general urban tree groups are scant [49], because urban-based equations over the entire range of site conditions are not available for compilation and synthesis [55].Another approach to overcome the prohibitive cost associated with the development of allometric equations is to utilize remote sensing and geospatial techniques [49,[56][57][58][59][60]. Remote sensing obtains information about the Earth's environment from a distance without the need for extensive field surveys over large areas.…”

“…Remote sensing obtains information about the Earth's environment from a distance without the need for extensive field surveys over large areas. It has been used in several different ways as a non-destructive and cost-effective method for biomass estimation with varying degrees of success.Generated with Light Detection and Ranging (LiDAR) technology, point cloud data have been regularly used to measure urban tree biomass directly [58,[60][61][62] or extract basic silvicultural variables such as tree height and crown diameter, which are then applied to derive DBH for use in allometric equations [54,58,[62][63][64]. The relationships of tree height and crown diameter to DBH, however, have not been well analyzed for urban species [49,63] and biomass estimation could easily be affected by data quality [56].LiDAR data-derived structural characteristics have also been integrated with canopy photochemical properties retrieved from hyperspectral imagery to improve urban tree species identification [65], which would be helpful in selecting appropriate allometric equations for specific species [66].…”

“…However, automatic crown delineation is challenging because of the complexity of urban spaces [80]. Although sophisticated algorithms have been frequently used for crown segmentation [66,69,70,78,79], within-crown variations in brightness and difficulties in separating overlapping adjacent crowns could result in significant errors in both the number and sizes of crowns [60,81].As the discussion above attests, the estimation of urban tree biomass is easily affected by errors in estimating spatial cover, species composition, and structural characteristics. However, it is not well Sustainability 2019, 11, 4347 4 of 19 understood how these errors propagate and collectively affect biomass estimation [63,65].…”

“…Another approach to overcome the prohibitive cost associated with the development of allometric equations is to utilize remote sensing and geospatial techniques [49,[56][57][58][59][60]. Remote sensing obtains information about the Earth's environment from a distance without the need for extensive field surveys over large areas.…”