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    Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach

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    remotesensing-10-00632.pdf
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    Author
    Hartfield, Kyle
    van Leeuwen, Willem
    Affiliation
    Univ Arizona, Sch Nat Resources & Environm
    Univ Arizona, Sch Geog & Dev
    Issue Date
    2018-04
    Keywords
    woody cover
    Cubist
    modelling
    Landsat
    NAIP
    CART
    Texas
    Oklahoma
    
    Metadata
    Show full item record
    Publisher
    MDPI
    Citation
    Hartfield KA, van Leeuwen WJD. Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach. Remote Sensing. 2018; 10(4):632.
    Journal
    REMOTE SENSING
    Rights
    © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Woody cover encroachment/expansion/conversion is a complex phenomenon that has environmental and economic impacts around the world. This research demonstrates the development of highly accurate models for estimating percent woody cover using high spatial resolution image data in combination with multi-seasonal Landsat reflectance products. We use a classification and regression tree (CART) approach to classify woody cover using fine resolution multispectral National Agricultural Imaging Program (NAIP) data. A continuous classification and regression tree (Cubist) ingests the aggregated woody cover classification along with the seasonal Landsat data to create a continuous woody cover model. We applied the models, derived by Cubist, across several Landsat scenes to estimate the percentage of woody plant cover, within each Landsat pixel, over a larger regional extent. We measured an average absolute error of 12.1 percent and a correlation coefficient of 0.78 for the models performed. The method of modelling percent woody cover established in this manuscript outperforms currently available woody cover estimates including Landsat Vegetation Continuous Fields (VCF), on average by 26 percent, and Web-Enabled Landsat Data (WELD) products, on average by 16 percent, for the region of interest. Current woody cover products are also limited to certain years and not available pre-2000. This manuscript describes a novel Cubist-based technique to model woody cover for any area of the world, as long as fine (similar to 1-2 m) spatial resolution and Landsat data are available.
    ISSN
    2072-4292
    DOI
    10.3390/rs10040632
    Version
    Final published version
    Sponsors
    NSF's Division of Environmental Biology [1413900]
    Additional Links
    http://www.mdpi.com/2072-4292/10/4/632
    ae974a485f413a2113503eed53cd6c53
    10.3390/rs10040632
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