Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model
This research emphasized on “Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model”. It mainly emphasized on the identification of pixel-based magnitude and direction of changes in vegetation cover due to disturbances caused by the Rohingya inhabitants based...
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| Format: | Online |
| Idioma: | anglès |
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Jahangirnagar University
2025
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| Accés en línia: | http://journals.juniv.edu/index.php/tjr/article/view/230 |
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| author | Khan, Ebadullah Ali, Mohammad Sultana, Ali Ashrafi |
| author_facet | Khan, Ebadullah Ali, Mohammad Sultana, Ali Ashrafi |
| author_sort | Khan, Ebadullah |
| collection | OJS |
| description | This research emphasized on “Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model”. It mainly emphasized on the identification of pixel-based magnitude and direction of changes in vegetation cover due to disturbances caused by the Rohingya inhabitants based on Normalized Difference Vegetation Index derived from Landsat satellite imageries from 1984 to 2024. Ukhia is an upazila located under Cox's Bazar district, is home to a substantial natural environment and vegetation cover. However, the local ecosystem and ecology have experienced considerable changes due to intensified human activities, including the establishment of large Rohingya refugee camps, illegal logging, and the impacts of climate change. Randomly selected reference points from camp area indicated a substantial decline of NDVI value (magnitude) from 0.8 to 0.1 which indicates a significant forest loss for the year of 2017 and 2018 which was not recovered in the subsequent years anymore. Hence, there is an abrupt or structural changes occurred in vegetation cover due to identified vegetation disturbance. For most of the sites, RMSE value was lower than 0.05 which indicates a better performance of the fitted LandTrendr model. It has been observed that severe vegetation loss and moderate vegetation loss occupy 16350 hectares and 53456 hectares including 24.99 % and 72.39% respectively regarding the total percentage of vegetation disturbance area. This research can give a proper direction to the concerned authorities in terms of identifying the sites where vegetation disturbances are continuously occurring including their magnitude and direction of change. |
| format | Online |
| id | ojs-230 |
| institution | My University |
| language | eng |
| publishDate | 2025 |
| publisher | Jahangirnagar University |
| record_format | ojs |
| spelling | ojs-2302025-10-16T03:16:16Z Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model Khan, Ebadullah Ali, Mohammad Sultana, Ali Ashrafi vegetation disturbance LandTrendr RMSE magnitude recovery This research emphasized on “Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model”. It mainly emphasized on the identification of pixel-based magnitude and direction of changes in vegetation cover due to disturbances caused by the Rohingya inhabitants based on Normalized Difference Vegetation Index derived from Landsat satellite imageries from 1984 to 2024. Ukhia is an upazila located under Cox's Bazar district, is home to a substantial natural environment and vegetation cover. However, the local ecosystem and ecology have experienced considerable changes due to intensified human activities, including the establishment of large Rohingya refugee camps, illegal logging, and the impacts of climate change. Randomly selected reference points from camp area indicated a substantial decline of NDVI value (magnitude) from 0.8 to 0.1 which indicates a significant forest loss for the year of 2017 and 2018 which was not recovered in the subsequent years anymore. Hence, there is an abrupt or structural changes occurred in vegetation cover due to identified vegetation disturbance. For most of the sites, RMSE value was lower than 0.05 which indicates a better performance of the fitted LandTrendr model. It has been observed that severe vegetation loss and moderate vegetation loss occupy 16350 hectares and 53456 hectares including 24.99 % and 72.39% respectively regarding the total percentage of vegetation disturbance area. This research can give a proper direction to the concerned authorities in terms of identifying the sites where vegetation disturbances are continuously occurring including their magnitude and direction of change. Jahangirnagar University 2025-06-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Conference Proceedings Submission application/pdf http://journals.juniv.edu/index.php/tjr/article/view/230 10.59185/tjr.v49i2.230 The Jahangirnagar Review; Vol. 49 No. 2 (2025); 55-75 1682-7422 10.59185/tjr.v49i2 eng http://journals.juniv.edu/index.php/tjr/article/view/230/173 Copyright (c) 2025 The Jahangirnagar Review |
| spellingShingle | vegetation disturbance LandTrendr RMSE magnitude recovery Khan, Ebadullah Ali, Mohammad Sultana, Ali Ashrafi Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model |
| title | Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model |
| title_full | Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model |
| title_fullStr | Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model |
| title_full_unstemmed | Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model |
| title_short | Detecting Vegetation Disturbance in Kutupalong Rohingya Camp Area, Ukhia using LandTrendr Model |
| title_sort | detecting vegetation disturbance in kutupalong rohingya camp area ukhia using landtrendr model |
| topic | vegetation disturbance LandTrendr RMSE magnitude recovery |
| topic_facet | vegetation disturbance LandTrendr RMSE magnitude recovery |
| url | http://journals.juniv.edu/index.php/tjr/article/view/230 |
| work_keys_str_mv | AT khanebadullah detectingvegetationdisturbanceinkutupalongrohingyacampareaukhiausinglandtrendrmodel AT alimohammad detectingvegetationdisturbanceinkutupalongrohingyacampareaukhiausinglandtrendrmodel AT sultanaaliashrafi detectingvegetationdisturbanceinkutupalongrohingyacampareaukhiausinglandtrendrmodel |