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Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis

Overview of attention for article published in Sensors (14248220), November 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

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1 tweeter
patent
2 patents
q&a
2 Q&A threads

Citations

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20 Dimensions

Readers on

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74 Mendeley
Title
Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis
Published in
Sensors (14248220), November 2014
DOI 10.3390/s141222643
Pubmed ID
Authors

Martin Karlson, Heather Reese, Madelene Ostwald

Abstract

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35-100 m2) and large (≥100 m2) trees compared to small (<35 m2) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Australia 1 1%
Iran, Islamic Republic of 1 1%
Unknown 71 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 22%
Student > Master 15 20%
Researcher 13 18%
Student > Bachelor 7 9%
Professor > Associate Professor 5 7%
Other 18 24%
Readers by discipline Count As %
Environmental Science 25 34%
Earth and Planetary Sciences 21 28%
Agricultural and Biological Sciences 12 16%
Unspecified 9 12%
Engineering 2 3%
Other 5 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 19 June 2018.
All research outputs
#1,193,213
of 13,099,076 outputs
Outputs from Sensors (14248220)
#161
of 6,098 outputs
Outputs of similar age
#29,816
of 294,362 outputs
Outputs of similar age from Sensors (14248220)
#5
of 107 outputs
Altmetric has tracked 13,099,076 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,098 research outputs from this source. They receive a mean Attention Score of 2.6. 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 294,362 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 89% of its contemporaries.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.