Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models
Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is ma...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| التنسيق: | article |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/21424 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513438444683264 |
|---|---|
| author | Elmahdy, Samy |
| author2 | Ali, Tarig Mohamed, Mohamed Howari, Fares M. Abouleish, Mohamed Simonet, Daniel |
| author2_role | author author author author author |
| author_facet | Elmahdy, Samy Ali, Tarig Mohamed, Mohamed Howari, Fares M. Abouleish, Mohamed Simonet, Daniel |
| author_role | author |
| dc.creator.none.fl_str_mv | Elmahdy, Samy Ali, Tarig Mohamed, Mohamed Howari, Fares M. Abouleish, Mohamed Simonet, Daniel |
| dc.date.none.fl_str_mv | 2020 2021-04-19T09:28:33Z 2021-04-19T09:28:33Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Elmahdy, S. I., Ali, T. A., Mohamed, M. M., Howari, F. M., Abouleish, M., & Simonet, D. (2020). Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the northern emirates, uae using random forest, kernel logistic regression and naive bayes tree models. Frontiers in Environmental Science, 8. https://doi.org/10.3389/fenvs.2020.00102 2296-665X http://hdl.handle.net/11073/21424 10.3389/fenvs.2020.00102 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Frontiers |
| dc.relation.none.fl_str_mv | https://doi.org/10.3389/fenvs.2020.00102 |
| dc.subject.none.fl_str_mv | Northern United Arab Emirates (NUAE) Mangrove FMNF Remote sensing Change detection Landsat |
| dc.title.none.fl_str_mv | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization. |
| format | article |
| id | aus_84c48b9595e98fb6e69ff9840681955a |
| identifier_str_mv | Elmahdy, S. I., Ali, T. A., Mohamed, M. M., Howari, F. M., Abouleish, M., & Simonet, D. (2020). Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the northern emirates, uae using random forest, kernel logistic regression and naive bayes tree models. Frontiers in Environmental Science, 8. https://doi.org/10.3389/fenvs.2020.00102 2296-665X 10.3389/fenvs.2020.00102 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21424 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | Frontiers |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree ModelsElmahdy, SamyAli, TarigMohamed, MohamedHowari, Fares M.Abouleish, MohamedSimonet, DanielNorthern United Arab Emirates (NUAE)MangroveFMNFRemote sensingChange detectionLandsatMangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization.UAE Space AgencyFrontiers2021-04-19T09:28:33Z2021-04-19T09:28:33Z2020Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfElmahdy, S. I., Ali, T. A., Mohamed, M. M., Howari, F. M., Abouleish, M., & Simonet, D. (2020). Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the northern emirates, uae using random forest, kernel logistic regression and naive bayes tree models. Frontiers in Environmental Science, 8. https://doi.org/10.3389/fenvs.2020.001022296-665Xhttp://hdl.handle.net/11073/2142410.3389/fenvs.2020.00102en_UShttps://doi.org/10.3389/fenvs.2020.00102oai:repository.aus.edu:11073/214242024-08-22T12:07:05Z |
| spellingShingle | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models Elmahdy, Samy Northern United Arab Emirates (NUAE) Mangrove FMNF Remote sensing Change detection Landsat |
| status_str | publishedVersion |
| title | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| title_full | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| title_fullStr | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| title_full_unstemmed | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| title_short | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| title_sort | Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models |
| topic | Northern United Arab Emirates (NUAE) Mangrove FMNF Remote sensing Change detection Landsat |
| url | http://hdl.handle.net/11073/21424 |