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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-archives-XLVIII-M-10-2025-255-2026</article-id>
<title-group>
<article-title>Land-Use Dynamic Change Mapping and Ecological Network Construction from Multi-Temporal Remote Sensing Imagery</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yuan</surname>
<given-names>Qinglie</given-names>
<ext-link>https://orcid.org/0000-0001-7917-2910</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Civil and Architecture Engineering, Panzhihua University, Panzhihua 617000, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>04</day>
<month>05</month>
<year>2026</year>
</pub-date>
<volume>XLVIII-M-10-2025</volume>
<fpage>255</fpage>
<lpage>260</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Qinglie Yuan</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLVIII-M-10-2025/255/2026/isprs-archives-XLVIII-M-10-2025-255-2026.html">This article is available from https://isprs-archives.copernicus.org/articles/XLVIII-M-10-2025/255/2026/isprs-archives-XLVIII-M-10-2025-255-2026.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLVIII-M-10-2025/255/2026/isprs-archives-XLVIII-M-10-2025-255-2026.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLVIII-M-10-2025/255/2026/isprs-archives-XLVIII-M-10-2025-255-2026.pdf</self-uri>
<abstract>
<p>Regional economic growth drives significant land-use changes, impacting urban development, resource allocation, and ecological networks. Challenges such as climate change, ecological degradation, and land-use-ecology conflicts threaten human habitats, elevating the importance of biodiversity conservation. This study investigates the spatio-temporal evolution of land use (1990&amp;ndash;2020) in Meishan City, China, using multi-temporal Landsat imageries, and assesses its impact on ecological security using land-use dynamic index model. Research found that Cultivated land remained dominant but decreased over the study period, while forest and construction land expanded. Construction land primarily developed along rivers, forest land concentrated in Hongya County, and cultivated land prevailed in Renshou County. Utilizing resistance factors, this study identified ecological sources, constructed resistance surfaces, and delineated ecological corridors. Ecological sources covered 18.85% of the study area, with corridors totaling 461.77 km, predominantly located in the central region. Three key ecological pinch points were identified. Optimizing land-use patterns and the ecological network structure are recommended, prioritizing ecological restoration in Dongpo and Pengshan Districts. This research provides a scientific basis for sustainable land-resource utilization in Meishan and offers valuable insights for similar regions.</p>
</abstract>
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