Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach
Land Cover and Land Use studies play an important role in regional socioeconomic development and natural resource management. They support sustainable development by tracking changes in vegetation, freshwater quantity and quality, land resources, and coastal areas. Iraq's Land Use and Land Cove...
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| Format: | Article |
| Language: | Arabic |
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University of Information Technology and Communications
2025-06-01
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| Series: | Iraqi Journal for Computers and Informatics |
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| Online Access: | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/567 |
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| author | Rasha Abbas Ali Mahdi Nasif Jasim |
| author_facet | Rasha Abbas Ali Mahdi Nasif Jasim |
| author_sort | Rasha Abbas Ali |
| collection | DOAJ |
| description | Land Cover and Land Use studies play an important role in regional socioeconomic development and natural resource management. They support sustainable development by tracking changes in vegetation, freshwater quantity and quality, land resources, and coastal areas. Iraq's Land Use and Land Cover Monitoring with Remote Sensing Data in the Period 2019–2023. This paper performed land use/land cover LULC type classification and time series analysis using Sentinel-2 satellite imagery for the years 2019 and 2023 to identify changes over time. Remote sensing data is used in this paper to address the challenge of detecting land cover change in Iraq through SVM classification. This goal aims to develop a fundamental method of mapping and monitoring these changes, encouraging sustainable land use practices, and achieving the United Nations Sustainable Development Goals. Land cover classes were categorized into five main types: Water, Barren, Building, Vegetation, and Rangeland. The study showed a marked increase in urbanization, and most of this occurring in previously bare soils at the edges of cities. This urbanization was primarily driven by population growth and economic development. What is beneficial for the environment can also be beneficial for us as people humanity as these findings have major implications for urban planning, green space management, and sustainable city development. It seems that there was no change to the existing barren land and buildings, which increased by 8% and 11% respectively, as noted from the data up to October 2023. However, vegetation coverage decreased by 27%, indicating a significant loss of green area. The water category was also up 9%. Results showed satisfactory accuracy assessment (OA: 93.11%) from applying a Support Vector Machine SVM for the LULC classification. The study lays the foundation for ongoing monitoring of LULC changes in Iraq. |
| format | Article |
| id | doaj-art-102b6ad39e1a43d8aff0e82d60f4d93a |
| institution | OA Journals |
| issn | 2313-190X 2520-4912 |
| language | Arabic |
| publishDate | 2025-06-01 |
| publisher | University of Information Technology and Communications |
| record_format | Article |
| series | Iraqi Journal for Computers and Informatics |
| spelling | doaj-art-102b6ad39e1a43d8aff0e82d60f4d93a2025-08-20T02:07:16ZaraUniversity of Information Technology and CommunicationsIraqi Journal for Computers and Informatics2313-190X2520-49122025-06-0151110811810.25195/ijci.v51i1.567530Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing ApproachRasha Abbas Ali0Mahdi Nasif Jasim1University of Information Technology and CommunicationsUniversity of Information Technology and CommunicationsLand Cover and Land Use studies play an important role in regional socioeconomic development and natural resource management. They support sustainable development by tracking changes in vegetation, freshwater quantity and quality, land resources, and coastal areas. Iraq's Land Use and Land Cover Monitoring with Remote Sensing Data in the Period 2019–2023. This paper performed land use/land cover LULC type classification and time series analysis using Sentinel-2 satellite imagery for the years 2019 and 2023 to identify changes over time. Remote sensing data is used in this paper to address the challenge of detecting land cover change in Iraq through SVM classification. This goal aims to develop a fundamental method of mapping and monitoring these changes, encouraging sustainable land use practices, and achieving the United Nations Sustainable Development Goals. Land cover classes were categorized into five main types: Water, Barren, Building, Vegetation, and Rangeland. The study showed a marked increase in urbanization, and most of this occurring in previously bare soils at the edges of cities. This urbanization was primarily driven by population growth and economic development. What is beneficial for the environment can also be beneficial for us as people humanity as these findings have major implications for urban planning, green space management, and sustainable city development. It seems that there was no change to the existing barren land and buildings, which increased by 8% and 11% respectively, as noted from the data up to October 2023. However, vegetation coverage decreased by 27%, indicating a significant loss of green area. The water category was also up 9%. Results showed satisfactory accuracy assessment (OA: 93.11%) from applying a Support Vector Machine SVM for the LULC classification. The study lays the foundation for ongoing monitoring of LULC changes in Iraq.https://ijci.uoitc.edu.iq/index.php/ijci/article/view/567remote sensing; lulc classification iraq environment; lulc machine learning; geospatial analysis svm classifier. |
| spellingShingle | Rasha Abbas Ali Mahdi Nasif Jasim Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach Iraqi Journal for Computers and Informatics remote sensing; lulc classification iraq environment; lulc machine learning; geospatial analysis svm classifier. |
| title | Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach |
| title_full | Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach |
| title_fullStr | Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach |
| title_full_unstemmed | Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach |
| title_short | Land Cover Change Detection in Iraq Using SVM Classification: A Remote Sensing Approach |
| title_sort | land cover change detection in iraq using svm classification a remote sensing approach |
| topic | remote sensing; lulc classification iraq environment; lulc machine learning; geospatial analysis svm classifier. |
| url | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/567 |
| work_keys_str_mv | AT rashaabbasali landcoverchangedetectioniniraqusingsvmclassificationaremotesensingapproach AT mahdinasifjasim landcoverchangedetectioniniraqusingsvmclassificationaremotesensingapproach |