↓ Skip to main content

Methodological considerations of terrestrial laser scanning for vegetation monitoring in the sagebrush steppe

Overview of attention for article published in Environmental Monitoring & Assessment, October 2017
Altmetric Badge

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 (65th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
15 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
Methodological considerations of terrestrial laser scanning for vegetation monitoring in the sagebrush steppe
Published in
Environmental Monitoring & Assessment, October 2017
DOI 10.1007/s10661-017-6300-0
Pubmed ID
Authors

Kyle E. Anderson, Nancy F. Glenn, Lucas P. Spaete, Douglas J. Shinneman, David S. Pilliod, Robert S. Arkle, Susan K. McIlroy, DeWayne R. Derryberry

Abstract

Terrestrial laser scanning (TLS) provides fast collection of high-definition structural information, making it a valuable field instrument to many monitoring applications. A weakness of TLS collections, especially in vegetation, is the occurrence of unsampled regions in point clouds where the sensor's line-of-sight is blocked by intervening material. This problem, referred to as occlusion, may be mitigated by scanning target areas from several positions, increasing the chance that any given area will fall within the scanner's line-of-sight from at least one position. Because TLS collections are often employed in remote regions where the scope of sampling is limited by logistical factors such as time and battery power, it is important to design field protocols which maximize efficiency and support increased quantity and quality of the data collected. This study informs researchers and practitioners seeking to optimize TLS sampling methods for vegetation monitoring in dryland ecosystems through three analyses. First, we quantify the 2D extent of occluded regions based on the range from single scan positions. Second, we measure the efficacy of additional scan positions on the reduction of 2D occluded regions (area) using progressive configurations of scan positions in 1 ha plots. Third, we test the reproducibility of 3D sampling yielded by a 5-scan/ha sampling methodology using redundant sets of scans. Analyses were performed using measurements at analysis scales of 5 to 50 cm across the 1-ha plots, and we considered plots in grass and shrub-dominated communities separately. In grass-dominated plots, a center-scan configuration and 5 cm pixel size sampled at least 90% of the area up to 18 m away from the scanner. In shrub-dominated plots, sampling at least 90% of the area was only achieved within a distance of 12 m. We found that 3 and 5 scans/ha are needed to sample at least ~ 70% of the total area (1 ha) in the grass and shrub-dominated plots, respectively, using 5 cm pixels to measure sampling presence-absence. The reproducibility of 3D sampling provided by a 5 position scan layout across 1-ha plots was 50% (shrub) and 70% (grass) using a 5-cm voxel size, whereas at the 50-cm voxel scale, reproducibility of sampling was nearly 100% for all plot types. Future studies applying TLS in similar dryland environments for vegetation monitoring may use our results as a guide to efficiently achieve sampling coverage and reproducibility in datasets.

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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 27%
Student > Master 4 27%
Student > Ph. D. Student 3 20%
Professor 2 13%
Student > Bachelor 1 7%
Other 1 7%
Readers by discipline Count As %
Earth and Planetary Sciences 6 40%
Agricultural and Biological Sciences 4 27%
Unspecified 2 13%
Engineering 2 13%
Business, Management and Accounting 1 7%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 October 2017.
All research outputs
#6,322,251
of 12,050,803 outputs
Outputs from Environmental Monitoring & Assessment
#446
of 1,199 outputs
Outputs of similar age
#112,016
of 284,728 outputs
Outputs of similar age from Environmental Monitoring & Assessment
#14
of 41 outputs
Altmetric has tracked 12,050,803 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,199 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 62% 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 284,728 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 41 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 65% of its contemporaries.