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Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest

Overview of attention for article published in Carbon Balance and Management, May 2016
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  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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Title
Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest
Published in
Carbon Balance and Management, May 2016
DOI 10.1186/s13021-016-0048-7
Pubmed ID
Authors

Shaun R. Levick, Dominik Hessenmöller, E-Detlef Schulze

Abstract

Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany. Estimation of wood volume from airborne LiDAR was most robust (R(2) = 0.92, RMSE = 50.57 m(3) ha(-1) ~14.13 Mg C ha(-1)) when trained and tested with 1 ha experimental plot data (n = 50). Predictions based on a more extensive (n = 1100) plot network with considerably smaller (0.05 ha) plots were inferior (R(2) = 0.68, RMSE = 101.01 ~28.09 Mg C ha(-1)). Differences between the 1 and 0.05 ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05 ha plots returned no improvement in R(2) and RMSE variability of the LiDAR-predicted wood volume model. Our results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 67 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 19%
Student > Master 11 16%
Student > Bachelor 5 7%
Student > Postgraduate 5 7%
Student > Ph. D. Student 4 6%
Other 11 16%
Unknown 19 28%
Readers by discipline Count As %
Environmental Science 15 22%
Agricultural and Biological Sciences 12 18%
Earth and Planetary Sciences 11 16%
Engineering 6 9%
Arts and Humanities 1 1%
Other 0 0%
Unknown 23 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 03 July 2016.
All research outputs
#6,661,283
of 25,918,104 outputs
Outputs from Carbon Balance and Management
#106
of 232 outputs
Outputs of similar age
#99,655
of 357,763 outputs
Outputs of similar age from Carbon Balance and Management
#3
of 8 outputs
Altmetric has tracked 25,918,104 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 232 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.9. This one has gotten more attention than average, scoring higher than 54% 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 357,763 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.