Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve
This present study aims to evaluate land use and land cover changes using five machine-learning algorithms in Google Earth Engine. The performance of these machine learning algorithms was evaluated using user accuracy, producer accuracy, overall accuracy, and kappa coefficient. Additionally, it seek...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024011095 |
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| author | Bhaskar Mandal |
| author_facet | Bhaskar Mandal |
| author_sort | Bhaskar Mandal |
| collection | DOAJ |
| description | This present study aims to evaluate land use and land cover changes using five machine-learning algorithms in Google Earth Engine. The performance of these machine learning algorithms was evaluated using user accuracy, producer accuracy, overall accuracy, and kappa coefficient. Additionally, it seeks to understand the evolving pattern of land surface temperature and its correlation with biophysical characteristics in the Brahmani-Dwarka interfluve region from 1991 to 2021. The results demonstrate that RF algorithms outperform other algorithms in terms of performance, with RF algorithms averaging an 86 % overall accuracy and a Kappa coefficient of 0.82, while GTB comes in second with an 85 % overall accuracy and a Kappa value of 0.81. SVM performed moderately, while CART and MD algorithms struggled to perform in this study. The analysis of land transformation from 1991 to 2021 indicates that the stone crushing industry, built-up, and waterbodies have shown an upward trend, while vegetation and fallow land have decreased in their geographical extent. The results of the LST analysis indicated the study area had an LST rise of 8.72 °C over the last 30 years, or 0.29 °C per year, with the stone crushing and mining activities exhibiting the highest increase of 11.67 °C. Further, correlation analysis reveals LST has a highly significant positive correlation with NDBI and NDBaI and a very significant negative correlation with NDVI, MNDWI, and NDLI throughout the study period. The findings emphasize the importance of long-term planning and environmentally friendly development, ensuring responsible stone crushing activities, sustainable techniques, biodiversity conservation, and sustainable utilization of natural resources. |
| format | Article |
| id | doaj-art-f4d9cd1ac4fb4a849404e7db05e8871f |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-f4d9cd1ac4fb4a849404e7db05e8871f2024-12-19T10:57:07ZengElsevierResults in Engineering2590-12302024-12-0124102854Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluveBhaskar Mandal0Research Scholar, Department of Geography, Institute of Science, Banaras Hindu University, Varanasi, 221005, IndiaThis present study aims to evaluate land use and land cover changes using five machine-learning algorithms in Google Earth Engine. The performance of these machine learning algorithms was evaluated using user accuracy, producer accuracy, overall accuracy, and kappa coefficient. Additionally, it seeks to understand the evolving pattern of land surface temperature and its correlation with biophysical characteristics in the Brahmani-Dwarka interfluve region from 1991 to 2021. The results demonstrate that RF algorithms outperform other algorithms in terms of performance, with RF algorithms averaging an 86 % overall accuracy and a Kappa coefficient of 0.82, while GTB comes in second with an 85 % overall accuracy and a Kappa value of 0.81. SVM performed moderately, while CART and MD algorithms struggled to perform in this study. The analysis of land transformation from 1991 to 2021 indicates that the stone crushing industry, built-up, and waterbodies have shown an upward trend, while vegetation and fallow land have decreased in their geographical extent. The results of the LST analysis indicated the study area had an LST rise of 8.72 °C over the last 30 years, or 0.29 °C per year, with the stone crushing and mining activities exhibiting the highest increase of 11.67 °C. Further, correlation analysis reveals LST has a highly significant positive correlation with NDBI and NDBaI and a very significant negative correlation with NDVI, MNDWI, and NDLI throughout the study period. The findings emphasize the importance of long-term planning and environmentally friendly development, ensuring responsible stone crushing activities, sustainable techniques, biodiversity conservation, and sustainable utilization of natural resources.http://www.sciencedirect.com/science/article/pii/S2590123024011095Stone crushing industryLULC classificationLSTBiophysical indicesMachine learningGoogle earth engine |
| spellingShingle | Bhaskar Mandal Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve Results in Engineering Stone crushing industry LULC classification LST Biophysical indices Machine learning Google earth engine |
| title | Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve |
| title_full | Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve |
| title_fullStr | Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve |
| title_full_unstemmed | Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve |
| title_short | Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-Dwarka interfluve |
| title_sort | leveraging machine learning for analyzing the nexus between land use and land cover change land surface temperature and biophysical indices in an eco sensitive region of brahmani dwarka interfluve |
| topic | Stone crushing industry LULC classification LST Biophysical indices Machine learning Google earth engine |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024011095 |
| work_keys_str_mv | AT bhaskarmandal leveragingmachinelearningforanalyzingthenexusbetweenlanduseandlandcoverchangelandsurfacetemperatureandbiophysicalindicesinanecosensitiveregionofbrahmanidwarkainterfluve |