Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sen...
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42986/ http://umpir.ump.edu.my/id/eprint/42986/1/Machine%20learning%20and%20remote%20sensing%20applications%20for%20assessing%20land%20use%20and%20land%20cover%20changes%20for%20under-monitored%20basin.pdf |
| _version_ | 1848826750991073280 |
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| author | Muhammad Amiruddin, Zulkifli Anak Gisen, Jacqueline Isabella Syarifuddin, Misbari Shairul Rohaziawati, Samat Yu, Qian |
| author_facet | Muhammad Amiruddin, Zulkifli Anak Gisen, Jacqueline Isabella Syarifuddin, Misbari Shairul Rohaziawati, Samat Yu, Qian |
| author_sort | Muhammad Amiruddin, Zulkifli |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals. |
| first_indexed | 2025-11-15T03:49:47Z |
| format | Article |
| id | ump-42986 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:49:47Z |
| publishDate | 2024 |
| publisher | Universiti Putra Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-429862024-11-27T07:59:30Z http://umpir.ump.edu.my/id/eprint/42986/ Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin Muhammad Amiruddin, Zulkifli Anak Gisen, Jacqueline Isabella Syarifuddin, Misbari Shairul Rohaziawati, Samat Yu, Qian TA Engineering (General). Civil engineering (General) Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals. Universiti Putra Malaysia 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42986/1/Machine%20learning%20and%20remote%20sensing%20applications%20for%20assessing%20land%20use%20and%20land%20cover%20changes%20for%20under-monitored%20basin.pdf Muhammad Amiruddin, Zulkifli and Anak Gisen, Jacqueline Isabella and Syarifuddin, Misbari and Shairul Rohaziawati, Samat and Yu, Qian (2024) Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin. Pertanika Journal of Science & Technology (JST), 32 (6). pp. 2699-2722. ISSN 0128-7680. (Published) https://doi.org/10.47836/pjst.32.6.15 10.47836/pjst.32.6.15 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Muhammad Amiruddin, Zulkifli Anak Gisen, Jacqueline Isabella Syarifuddin, Misbari Shairul Rohaziawati, Samat Yu, Qian Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| title | Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| title_full | Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| title_fullStr | Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| title_full_unstemmed | Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| title_short | Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| title_sort | machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/42986/ http://umpir.ump.edu.my/id/eprint/42986/ http://umpir.ump.edu.my/id/eprint/42986/ http://umpir.ump.edu.my/id/eprint/42986/1/Machine%20learning%20and%20remote%20sensing%20applications%20for%20assessing%20land%20use%20and%20land%20cover%20changes%20for%20under-monitored%20basin.pdf |