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    <journal>
      <journal_metadata>
        <full_title>REGION</full_title>
        <abbrev_title>REGION</abbrev_title>
        <issn media_type="electronic">2409-5370</issn>
      </journal_metadata>
      <journal_issue>
        <publication_date media_type="online">
          <month>07</month>
          <day>22</day>
          <year>2026</year>
        </publication_date>
        <journal_volume>
          <volume>13</volume>
        </journal_volume>
        <issue>2</issue>
      </journal_issue>
      <journal_article metadata_distribution_opts="any" publication_type="full_text">
        <titles>
          <title>Spatiotemporal Assessment of Expressway-Induced Land Cover and Landscape Transformation in Sri Lanka</title>
        </titles>
        <contributors>
          <person_name sequence="first" contributor_role="author" language="en">
            <given_name>Piyoda</given_name>
            <surname>Wijesekara</surname>
          </person_name>
          <person_name sequence="additional" contributor_role="author" language="en">
            <given_name>Amila </given_name>
            <surname>Jayasinghe</surname>
          </person_name>
        </contributors>
        <jats:abstract>
          <jats:p><p>Since the twentieth century, the sustainable use of land has emerged as a critical concern, with land use patterns shifting rapidly from agricultural and settlement expansions to infrastructure and urban development. Transport infrastructures are tightly connected in urban expansions, while exerting positive and negative impacts on land, society, economy and biodiversity. In addressing the need for sustainable land management, land cover change detection studies offer valuable insights that help scholars and policymakers overcome contemporary challenges in environmental conservation. This study focuses on the Southern Expressway Development Project (STDP) of Sri Lanka, where large-scale linear development projects represent a relatively new phenomenon. The study detects the change within a 5km buffer zone of the first phase of the STDP between the period 2001-2021, utilizing Geographic Information System (GIS) and Remote Sensing (RS) techniques. ArcGIS 10.8 and FRAGSTATS 4.2 were mainly used while the Landsat 8 images (path-141 and row-055 and row-056, with less than 10% cloud cover and of 30m-by-30m spatial resolution) were obtained from US Geological Survey's Natural Resources Observation Terminal Database website (https://earthexplorer.usgs.gov) for both years. Seven land classes were identified using supervised image classification. The overall accuracy of the images were 84% in 2001 and 85% in 2021. Analysis depicted that natural vegetation and built-up land cover have increased significantly, while cultivated lands, paddy and homesteads decreased by 5%, 7.73% and 6.44%. Water areas do not depict a considerable change. The Transfer Matrix depicted that 11066.14 ha of cultivated lands were transferred to natural vegetation. While a large amount of paddy lands was converted into cultivated lands (8530.64 ha) and natural vegetation (7218.78 ha). Results obtained through FRAGSTATS analysis revealed that the patch density of the area was increased from 112.57 to 169.42 and the number of patches of the overall landscape was increased by 58459. Class level analysis revealed though the natural vegetation and urban lands increased in total area, both land uses were highly fragmented. The area means of all the land uses were also reduced. Accordingly, the study concludes that the findings of the study are valuable for urban planners in two fronts. On one hand, the study provides a quantitative analysis of the impact of STDP on land use and land cover changes. On the other hand, the GIS &amp;amp; RS based method and technical process adopted in the study would be effective and useful for urban and regional planners to evaluate the land use changes and fragmentation.</p></jats:p>
        </jats:abstract>
        <publication_date media_type="online">
          <month>08</month>
          <day>24</day>
          <year>2026</year>
        </publication_date>
        <pages>
          <first_page>81</first_page>
          <other_pages>100</other_pages>
        </pages>
        <ai:program name="AccessIndicators">
          <ai:license_ref>http://creativecommons.org/licenses/by-nc/4.0</ai:license_ref>
        </ai:program>
        <doi_data>
          <doi>10.18335/region.v13i2.642</doi>
          <resource>http://openjournals.wu.ac.at/ojs/index.php/region/article/view/642</resource>
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