↓ Skip to main content

Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Overview of attention for article published in Carbon Balance and Management, August 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#32 of 139)
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
14 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
40 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.
Title
Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA
Published in
Carbon Balance and Management, August 2015
DOI 10.1186/s13021-015-0030-9
Pubmed ID
Authors

Wenli Huang, Anu Swatantran, Kristofer Johnson, Laura Duncanson, Hao Tang, Jarlath O’Neil Dunne, George Hurtt, Ralph Dubayah

Abstract

Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5-92.7 Mg ha(-1)). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0-54.6 Mg ha(-1)) and total biomass (3.5-5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30-80 Tg in forested and 40-50 Tg in non-forested areas. Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 10%
Spain 1 3%
Unknown 35 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 33%
Student > Ph. D. Student 11 28%
Student > Master 4 10%
Other 3 8%
Professor 2 5%
Other 3 8%
Unknown 4 10%
Readers by discipline Count As %
Environmental Science 13 33%
Earth and Planetary Sciences 13 33%
Agricultural and Biological Sciences 5 13%
Engineering 1 3%
Unknown 8 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 05 January 2016.
All research outputs
#1,274,071
of 11,975,937 outputs
Outputs from Carbon Balance and Management
#32
of 139 outputs
Outputs of similar age
#34,913
of 237,114 outputs
Outputs of similar age from Carbon Balance and Management
#1
of 7 outputs
Altmetric has tracked 11,975,937 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 139 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.3. This one has done well, scoring higher than 76% 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 237,114 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 7 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