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  <doc>
    <id>4480</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Land-use changes in coastal areas of Using Multi-Time satellite data</title>
    <abstract language="eng">The use of coastal lands is constantly changing due to the regression and progress of water 10 areas. This coastal Land use changes the need for continuous monitoring at regular intervals to 11 efficiently manage coastal resources. In this study, the effect of Gorgan gulf ( is location in the 12 southeastern part of the Caspian Sea in Golestan province of IRAN country ) progress and re-13 gression on land-use change in the surrounding lands has been modeled and estimated using 14 remote sensing images. For this purpose, two satellite images of Landsat 5 and Sentinel 2 were 15 taken in 1992 and 2017, respectively, and after preparing and applying the necessary prepro-16 cessing, the most similar classification and land uses of the study area were extracted. Overall 17 accuracy values of 0.94 and kappa coefficient of 0.92 indicated the appropriate accuracy of land 18 use maps. The classified images and the topographic maps, and the network of passages of the 19 study area were designated as land modeling inputs to detect land-use changes over 25 years. The 20 study results showed that the water area of the Gorgan gulf had faced a water level decline of 21 150 square kilometers, which has led to an increase in the area of wastelands. Also, agriculture 22 and garden, and rangelands have been significantly reduced during the study period 23&#13;
Keywords: Change Detection, Landsat 5, Sentinel 2, Remote Sensing, Gorgan gulf</abstract>
    <parentTitle language="eng">Euro-Mediterranean Journal for Environmental Integration University of Sfax, Tunisia</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-44800</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Mehran Dizbadi</author>
    <author>Majid Taghipour</author>
    <author>Behrooz Arastoo</author>
    <author>Ansgar Brunn</author>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/4480/print-acceptance-letter.pdf</file>
  </doc>
  <doc>
    <id>2235</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>6</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine</title>
    <abstract language="eng">Forest classification is needed to solve a wide range of environmental issues related to of forest classes and succession processes, the extent of afforestation and deforestation and global environmental change. These applications require a very accurate mapping and monitoring of forest types. This article investigates the combination of modern open geographic information systems and remote sensing data in forest management tasks for a specific part of the Ukrainian state area. Based on the existing afforestation plans, the results of the unsupervised classification of Sentinel-2 images and the selection of forest species fragments with closed crowns as training data for supervised classification, classifiers of forest species of the study object were developed with and without taking into account age groups. A supervised classification of research objects is realized and the accuracy of the obtained results is evaluated. It is established that the accuracy of determining forest species on the basis of the proposed method is 90.3 and 91.4%, taking into account age groups and without taking them into account, respectively. Thus, it is found that the modeling of the age groups does not improve the classification result for the test area.</abstract>
    <parentTitle language="eng">Frontiers in Forests and Global Change</parentTitle>
    <identifier type="url">https://doi.org/10.3389/ffgc.2023.1041882</identifier>
    <identifier type="issn">2624-893X</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Pavlo Manko</author>
    <author>Ansgar Brunn</author>
    <author>Oleksandr Melnyk</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>remote sensing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tree species</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>supervised classification</value>
    </subject>
    <collection role="institutes" number="fkv">Fakultät Kunststofftechnik und Vermessung</collection>
    <collection role="Regensburger_Klassifikation" number="ZG - ZS">Technik</collection>
    <collection role="ddc" number="526">Mathematische Geografie</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/2235/Manko_Remote_sensing.pdf</file>
  </doc>
</export-example>
