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Anthropogenic Ignitions
Emily J. Fusco, John Abatzoglou, Jennifer K. Balch, John T. Finn, and Bethany Bradley
This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains a random subset of unburned points. Both ignition and unburned points have associated anthropogenic feature data.
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Ignition Cause
Emily J. Fusco, John Abatzoglou, Jennifer K. Balch, John T. Finn, and Bethany Bradley
This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains the determined cause for each ignition.
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Model of cheatgrass (Bromus tectorum) distribution across the Great Basin, USA
Bethany Bradley
A description of the methods associated with this model can be found in:
Bradley, B.A., C.A. Curtis, E.J. Fusco, J.T. Abatzoglou, J.K. Balch, S. Dadashi, and M.N. Tuanmu. “Cheatgrass (Bromus tectorum) distribution in the intermountain western United States and its relationship to fire frequency, seasonality, and ignitions”, In Press, Biological Invasions
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Lights, Camera...Citizen Science: Assessing the Effectiveness of Smartphone-based Video Training in Invasive Plant Identification dataset
Jared Starr, Charles M. Schweik, Nathan Bush, Lena Fletcher, John T. Finn, Jennifer Fish, and Charles T. Bargeron
The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional “in-person” training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical. This file is the raw data that accompanies the PLoS article.
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Cheatgrass (Bromus tectorum) percent cover data
Bethany Bradley
A compilation of cheatgrass (Bromus tectorum) percent cover data across the western U.S. used to train a regional land cover map as well as assess relationships to fire.
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