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Modeling vegetation heights from high resolution stereo aerial photography: An application for broad-scale rangeland monitoring

Overview of attention for article published in Journal of Environmental Management, November 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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3 tweeters
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1 Facebook page

Citations

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14 Dimensions

Readers on

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73 Mendeley
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Title
Modeling vegetation heights from high resolution stereo aerial photography: An application for broad-scale rangeland monitoring
Published in
Journal of Environmental Management, November 2014
DOI 10.1016/j.jenvman.2014.05.028
Pubmed ID
Authors

Jeffrey K. Gillan, Jason W. Karl, Michael Duniway, Ahmed Elaksher

Abstract

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Mexico 1 1%
South Africa 1 1%
Belgium 1 1%
Japan 1 1%
Norway 1 1%
Unknown 66 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 23%
Student > Ph. D. Student 15 21%
Researcher 10 14%
Student > Bachelor 9 12%
Other 5 7%
Other 13 18%
Unknown 4 5%
Readers by discipline Count As %
Environmental Science 27 37%
Agricultural and Biological Sciences 17 23%
Earth and Planetary Sciences 8 11%
Engineering 4 5%
Social Sciences 3 4%
Other 3 4%
Unknown 11 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 July 2014.
All research outputs
#6,495,057
of 12,019,953 outputs
Outputs from Journal of Environmental Management
#995
of 1,941 outputs
Outputs of similar age
#73,620
of 191,727 outputs
Outputs of similar age from Journal of Environmental Management
#4
of 15 outputs
Altmetric has tracked 12,019,953 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,941 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 191,727 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 59% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.