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dc.contributor.authorHartfield, Kyle
dc.contributor.authorvan Leeuwen, Willem
dc.date.accessioned2018-11-26T23:03:20Z
dc.date.available2018-11-26T23:03:20Z
dc.date.issued2018-04
dc.identifier.citationHartfield 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.en_US
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs10040632
dc.identifier.urihttp://hdl.handle.net/10150/631059
dc.description.abstractWoody 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.en_US
dc.description.sponsorshipNSF's Division of Environmental Biology [1413900]en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urlhttp://www.mdpi.com/2072-4292/10/4/632en_US
dc.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.en_US
dc.subjectwoody coveren_US
dc.subjectCubisten_US
dc.subjectmodellingen_US
dc.subjectLandsaten_US
dc.subjectNAIPen_US
dc.subjectCARTen_US
dc.subjectTexasen_US
dc.subjectOklahomaen_US
dc.titleWoody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approachen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Sch Nat Resources & Environmen_US
dc.contributor.departmentUniv Arizona, Sch Geog & Deven_US
dc.identifier.journalREMOTE SENSINGen_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleRemote Sensing
dc.source.volume10
dc.source.issue4
dc.source.beginpage632
refterms.dateFOA2018-11-26T23:03:20Z


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