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        <title type="main" level="a">Mapping Yucca gloriosa in coastal dunes: evaluating the cost and time efficiency of photointerpretation, machine learning and field detection approaches.</title>
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          <persName n="1" ref="https://orcid.org/0009-0003-9396-1352" type="ORCID">
            <forename>Elena</forename>
            <surname>Cini</surname>
            <placeName type="affiliation">Roma Tre University, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0001-5661-4683" type="ORCID">
            <forename>Flavio</forename>
            <surname>Marzialetti</surname>
            <placeName type="affiliation">University of Sassari, Italy</placeName>
          </persName>
          <persName n="3" ref="https://orcid.org/0000-0002-9799-7059" type="ORCID">
            <forename>Marco</forename>
            <surname>Paterni</surname>
            <placeName type="affiliation">Clinical Physiology Institute CNR, Italy</placeName>
          </persName>
          <persName n="4" ref="https://orcid.org/0000-0002-8798-9469" type="ORCID">
            <forename>Andrea</forename>
            <surname>Berton</surname>
            <placeName type="affiliation">Clinical Physiology Institute CNR, Italy</placeName>
          </persName>
          <persName n="5" ref="https://orcid.org/0000-0001-6572-3187" type="ORCID">
            <forename>Alicia Teresa Rosario</forename>
            <surname>Acosta</surname>
            <placeName type="affiliation">Roma Tre University, Italy</placeName>
          </persName>
          <persName n="6" ref="https://orcid.org/0000-0001-9715-9779" type="ORCID">
            <forename>Daniela</forename>
            <surname>Ciccarelli</surname>
            <placeName type="affiliation">University of Pisa, Italy</placeName>
          </persName>
        </author>
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          <resp>This is a section of <title>Tenth InternationaSymposium Monitoring of Mediterranean Coastal Areas: Problems and Measurement Techniques</title>(DOI: <idno type="DOI">10.36253/979-12-215-0556-6</idno>) by </resp>
          <name>Laura Bonora, Marcantonio Catelani, Matteo De Vincenzi, Giorgio Matteucci</name>
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        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2024">2024</date>
        <idno type="DOI">https://doi.org/10.36253/979-12-215-0556-6.14</idno>
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          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
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            <p>Content licence CC BY-NC-SA 4.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>Biological invasions threaten biodiversity and cause significant economic and ecological costs. Effective management of invasive species is crucial, as highlighted by the European Community's Regulation 1143/2014 on Invasive Alien Species (IAS). This study focuses on coastal dune ecosystems, particularly assessing the time and cost-effectiveness of three monitoring methods for detecting and mapping alien plants: photointerpretation, machine learning classification, and field monitoring. Yucca gloriosa L., an invasive species in Regional Park of Migliarino-San Rossore-Massaciuccoli (Tuscany, Italy), served as the target species. Using RGB DJI Phantom 4 Pro v. 2.0 and DJI P4 Multispectral drones, images were analyzed via photointerpretation and machine learning. Photointerpretation, though precise, was time-consuming and subjective. Machine learning minimized human effort but required extensive computing. Field monitoring produced accurate maps but was labor-intensive and limited by accessibility issues. This study concludes that UAV-based monitoring of Y. gloriosa is optimal for balancing cost and time efficiency in coastal dune ecosystems.</p>
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        <keywords>
          <list>
            <item>Alien plants</item>
            <item>Drones</item>
            <item>Monitoring</item>
            <item>RGB and multispectral</item>
            <item>Mapping</item>
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      <p>It is available online at https://doi.org/10.36253/979-12-215-0556-6.14<ref target="https://doi.org/10.36253/979-12-215-0556-6.14" /></p>
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