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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Overview of attention for article published in Journal of Visualized Experiments, October 2016
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2 tweeters

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21 Dimensions

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82 Mendeley
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Title
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published in
Journal of Visualized Experiments, October 2016
DOI 10.3791/54578
Pubmed ID
Authors

Amanda M. West, Paul H. Evangelista, Catherine S. Jarnevich, Nicholas E. Young, Thomas J. Stohlgren, Colin Talbert, Marian Talbert, Jeffrey Morisette, Ryan Anderson

Abstract

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 81 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 24%
Student > Ph. D. Student 17 21%
Researcher 12 15%
Student > Doctoral Student 7 9%
Student > Bachelor 6 7%
Other 12 15%
Unknown 8 10%
Readers by discipline Count As %
Environmental Science 30 37%
Agricultural and Biological Sciences 18 22%
Engineering 5 6%
Earth and Planetary Sciences 5 6%
Computer Science 2 2%
Other 7 9%
Unknown 15 18%

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 04 March 2020.
All research outputs
#11,088,350
of 17,145,811 outputs
Outputs from Journal of Visualized Experiments
#3,608
of 7,201 outputs
Outputs of similar age
#225,228
of 396,581 outputs
Outputs of similar age from Journal of Visualized Experiments
#82
of 163 outputs
Altmetric has tracked 17,145,811 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,201 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 41st percentile – i.e., 41% 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 396,581 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 163 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.