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Object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013.

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Title: Object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013.
Authors: Ellis, Emily A.1, Mathews, Adam J.1 adam.mathews@wmich.edu
Source: Computers, Environment & Urban Systems. Jan2019, Vol. 73, p85-94. 10p.
Subjects: Urban plants, Urban trees
Abstract: Abstract With a burgeoning global population, the pressures of urbanization are increasingly prevalent. The need to quantify urban greenness remains significant due to environmental impact and its relationship with human well-being. Utilizing 1 m discrete-return airborne lidar-derived digital terrain models (DTMs) and digital surface models (DSMs), aerial imagery, and lidar-imagery fusion, this study assesses vegetation, specifically tree canopy, change within Oklahoma City between 2006 and 2013. Specifically, we (1) identify an accurate object-based image analysis (OBIA) method for the detection of urban vegetation outlines, and (2) apply that method to locate and quantify vegetation change and assess spatial patterns in Oklahoma City between 2006 and 2013. The proposed OBIA approach extracts urban vegetation coverage from aerial imagery and lidar-based models with around 89% accuracy. Regarding vegetation change, Oklahoma City lost 9.69 km2 (3.74 mi2) of tree canopy coverage, which accounted for a 2% loss in total greenness. Highlights • An open source, adoptable object-based method was developed to extract tree canopy in the urban environment. • The method was tested using three input datasets: lidar, aerial imagery, and lidar-imagery fusion. • The lidar-imagery fusion data yielded the most accurate segmentation results. • The object-based method was 89% accurate at extracting tree canopy extents. • Between 2006 and 2013, Oklahoma City lost 2% of its urban tree canopy extent. [ABSTRACT FROM AUTHOR]
Copyright of Computers, Environment & Urban Systems is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 132804524
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="DE" term="%22Urban+plants%22">Urban plants</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+trees%22">Urban trees</searchLink>
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  Data: Abstract With a burgeoning global population, the pressures of urbanization are increasingly prevalent. The need to quantify urban greenness remains significant due to environmental impact and its relationship with human well-being. Utilizing 1 m discrete-return airborne lidar-derived digital terrain models (DTMs) and digital surface models (DSMs), aerial imagery, and lidar-imagery fusion, this study assesses vegetation, specifically tree canopy, change within Oklahoma City between 2006 and 2013. Specifically, we (1) identify an accurate object-based image analysis (OBIA) method for the detection of urban vegetation outlines, and (2) apply that method to locate and quantify vegetation change and assess spatial patterns in Oklahoma City between 2006 and 2013. The proposed OBIA approach extracts urban vegetation coverage from aerial imagery and lidar-based models with around 89% accuracy. Regarding vegetation change, Oklahoma City lost 9.69 km2 (3.74 mi2) of tree canopy coverage, which accounted for a 2% loss in total greenness. Highlights • An open source, adoptable object-based method was developed to extract tree canopy in the urban environment. • The method was tested using three input datasets: lidar, aerial imagery, and lidar-imagery fusion. • The lidar-imagery fusion data yielded the most accurate segmentation results. • The object-based method was 89% accurate at extracting tree canopy extents. • Between 2006 and 2013, Oklahoma City lost 2% of its urban tree canopy extent. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Computers, Environment & Urban Systems is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.compenvurbsys.2018.08.006
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 85
    Subjects:
      – SubjectFull: Urban plants
        Type: general
      – SubjectFull: Urban trees
        Type: general
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      – TitleFull: Object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013.
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              Text: Jan2019
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              Y: 2019
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              Value: 73
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