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Performance of non-parametric algorithms for spatial mapping of tropical forest structure

Overview of attention for article published in Carbon Balance and Management, August 2016
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Title
Performance of non-parametric algorithms for spatial mapping of tropical forest structure
Published in
Carbon Balance and Management, August 2016
DOI 10.1186/s13021-016-0062-9
Pubmed ID
Authors

Liang Xu, Sassan S. Saatchi, Yan Yang, Yifan Yu, Lee White

Abstract

Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. By using airborne lidar data as the "truth" and focusing on the mean canopy height (MCH) as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME) and random forest (RF) for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha) pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Student > Ph. D. Student 10 20%
Professor 4 8%
Student > Postgraduate 4 8%
Other 3 6%
Other 8 16%
Unknown 9 18%
Readers by discipline Count As %
Environmental Science 20 39%
Earth and Planetary Sciences 11 22%
Agricultural and Biological Sciences 4 8%
Engineering 3 6%
Unknown 13 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 August 2016.
All research outputs
#15,381,871
of 22,884,315 outputs
Outputs from Carbon Balance and Management
#170
of 236 outputs
Outputs of similar age
#217,914
of 341,481 outputs
Outputs of similar age from Carbon Balance and Management
#5
of 6 outputs
Altmetric has tracked 22,884,315 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 236 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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