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Wetland areas are valuable ecological systems whose importance lies not only in sustenance of resources for water as well as foods but also ensures biodiversity. Because of increased human activities and changing climatic effects, wetlands are being fast depleted, meaning that there's a need to have effective monitor and management methodologies. This article presents an idea of using HSV color model imagery processing for effective monitoring of satellite images on change in wetlands. Further, the K-Means clustering technique is used for image segmentation followed by further feature extraction through Principal Component Analysis and classification using Support Vector Machines. The proposed methodology identifies, segments, and measures wetland areas at different points in time from multi-temporal satellite images effectively. The results indicate the ability of this technique to track wetland changes, making it a very useful tool in environmental management and wetland conservation. This fully automated approach based on image preprocessing, segmentation, and morphological operations enhances the accuracy of wetland monitoring and provides a feasible solution for tracking spatial changes in wetland ecosystems.
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Cite Article:
"Analysis Of Wetland Changes Using The HSV Color Model In Rural Areas", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a65-a76, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503010.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator