↓ Skip to main content

Estimating urban above ground biomass with multi-scale LiDAR

Overview of attention for article published in Carbon Balance and Management, June 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 173)
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

9 news outlets
3 blogs
46 tweeters
1 Facebook page
1 Google+ user
1 Redditor


12 Dimensions

Readers on

101 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Estimating urban above ground biomass with multi-scale LiDAR
Published in
Carbon Balance and Management, June 2018
DOI 10.1186/s13021-018-0098-0
Pubmed ID

Phil Wilkes, Mathias Disney, Matheus Boni Vicari, Kim Calders, Andrew Burt


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.

Twitter Demographics

The data shown below were collected from the profiles of 46 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 18%
Student > Ph. D. Student 15 15%
Researcher 14 14%
Student > Bachelor 12 12%
Student > Doctoral Student 11 11%
Other 16 16%
Unknown 15 15%
Readers by discipline Count As %
Environmental Science 29 29%
Agricultural and Biological Sciences 19 19%
Earth and Planetary Sciences 13 13%
Engineering 6 6%
Computer Science 4 4%
Other 7 7%
Unknown 23 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 112. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 January 2019.
All research outputs
of 15,045,269 outputs
Outputs from Carbon Balance and Management
of 173 outputs
Outputs of similar age
of 275,561 outputs
Outputs of similar age from Carbon Balance and Management
of 1 outputs
Altmetric has tracked 15,045,269 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 173 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,561 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them