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High resolution mapping of development in the wildland-urban interface using object based image extraction

Overview of attention for article published in Heliyon, October 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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2 tweeters
facebook
1 Facebook page

Citations

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4 Dimensions

Readers on

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28 Mendeley
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Title
High resolution mapping of development in the wildland-urban interface using object based image extraction
Published in
Heliyon, October 2016
DOI 10.1016/j.heliyon.2016.e00174
Pubmed ID
Authors

Michael D. Caggiano, Wade T. Tinkham, Chad Hoffman, Antony S. Cheng, Todd J. Hawbaker

Abstract

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m(2)) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

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

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 25%
Student > Master 7 25%
Student > Ph. D. Student 5 18%
Lecturer 2 7%
Unspecified 2 7%
Other 5 18%
Readers by discipline Count As %
Environmental Science 11 39%
Agricultural and Biological Sciences 4 14%
Earth and Planetary Sciences 3 11%
Computer Science 3 11%
Business, Management and Accounting 2 7%
Other 5 18%

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 18 November 2016.
All research outputs
#7,064,734
of 12,517,967 outputs
Outputs from Heliyon
#297
of 744 outputs
Outputs of similar age
#131,026
of 284,792 outputs
Outputs of similar age from Heliyon
#5
of 13 outputs
Altmetric has tracked 12,517,967 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 744 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has gotten more attention than average, scoring higher than 57% 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 284,792 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 52% of its contemporaries.
We're also able to compare this research output to 13 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 61% of its contemporaries.