Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera)
| Autor(a) principal: | |
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| Data de Publicação: | 2022 |
| Outros Autores: | , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Anuário do Instituto de Geociências (Online) |
| Texto Completo: | https://revistas.ufrj.br/index.php/aigeo/article/view/44537 |
Resumo: | The fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics. |
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Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera)Built-up area; approach multi – index ;Google Earth Engine ; SVM algorithm ; Sentinel 2AThe fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics. Universidade Federal do Rio de JaneiroDib, SamiraSouiher, NouariBengusmia, Djamal2022-10-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/4453710.11137/1982-3908_2022_45_44537Anuário do Instituto de Geociências; Vol 45 (2022)Anuário do Instituto de Geociências; Vol 45 (2022)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/44537/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/44537/15907https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/44537/15908https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/44537/15909https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/44537/15910https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/44537/15911https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/44537/17297/*ref*/As-syakur, A.R., Adnyana, I.W.S., Arthana, I.W. & Nuarsa, I.W. 2012, 'Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area', Remote Sens, vol. 4, no. 10, pp. 2957-70, DOI:10.3390/rs4102957./*ref*/Bourcier, A. 1994. 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Zha, Y., Gao, J. & Ni, S. 2003, 'Use of normalized difference built-up index in automatically mapping urban areas from TM imagery', International Journal of Remote Sensing, vol. 24, no. 3, pp. 583-94, DOI:10.1080/01431160304987./*ref*/Zhang, J. & Foody, G.M. 2001, 'Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches', International Journal of Remote Sensing, vol. 22, no. 4, pp. 615-28, DOI:10.1080/01431160050505883.Copyright (c) 2022 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2022-12-28T20:46:28Zoai:www.revistas.ufrj.br:article/44537Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2022-12-28T20:46:28Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false |
| dc.title.none.fl_str_mv |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| title |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| spellingShingle |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) Dib, Samira Built-up area; approach multi – index ;Google Earth Engine ; SVM algorithm ; Sentinel 2A |
| title_short |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| title_full |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| title_fullStr |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| title_full_unstemmed |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| title_sort |
Extraction of Urban Areas Using Spectral Indices Combination and Google Earth Engine in Algerian Highlands (Case Study: Cities of Djelfa, Messaad, Ain Oussera) |
| author |
Dib, Samira |
| author_facet |
Dib, Samira Souiher, Nouari Bengusmia, Djamal |
| author_role |
author |
| author2 |
Souiher, Nouari Bengusmia, Djamal |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
|
| dc.contributor.author.fl_str_mv |
Dib, Samira Souiher, Nouari Bengusmia, Djamal |
| dc.subject.por.fl_str_mv |
Built-up area; approach multi – index ;Google Earth Engine ; SVM algorithm ; Sentinel 2A |
| topic |
Built-up area; approach multi – index ;Google Earth Engine ; SVM algorithm ; Sentinel 2A |
| description |
The fundamental difficulty in mapping urban areas, especially in semi-arid and arid environments, is the separation of built-up areas from bare lands, owing to their similar spectral characteristics. Accordingly, this study aims to identify the suitable spectral index that can provide high differentiation, between urban areas and bare lands, in semi-arid areas of three cities of the province of Djelfa, namely, Djelfa, Messaad, and Ain Oussera (Algerian central highlands), through a selection of four spectral indices including Urban Index (BUI), Band ratio for built-up area (BRBA), Normalized Difference Tillage Index (NDTI) and Dry Bare-soil Index (DBSI). In order to increase the mapping accuracy of the built-up in studied areas, a multi-index approach has been applied focusing on identifying an adequate combination of spectral indices of remote sensing that provides the highest performance compared to the images of sentinel 2A. The multi-index approach was developed using three spectral indices combinations and was created using a layer stack process. For forming bare land layer stacking data, both NDTI and DBSI indices were used, while the built-up area layer stacking data was made with both BUI and BRBA indices. The main process was carried out on the Cloud Computing Platform based on geospatial data of Google Earth Engine (GEE) and using machine learning classification by the Support Vector Machine (SVM) algorithm, based on imagery from sentinel 2A acquired during the dry season. The results indicated that the thresholds of the built-up areas are difficult to delineate and distinguish from bare land efficiently with a single index. The obtained results also revealed that the use of multi-index including BUI index provided the best results as they showed the highest effects with NDTI index and DBSI index compared to BRBA index, where the overall accuracies of the multi-index (DBSI/ NDTI/ BUI) were 98.7% in Djelfa, 96.5% in Messaad, and 97.87 % in Ain Oussera, and the kappa coefficients were 97.3%, 85.4%, and 95.3% respectively. These results show that this multi-index is effective and reliable and can be considered for use in other areas with similar characteristics. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-10-07 |
| dc.type.none.fl_str_mv |
|
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/44537 10.11137/1982-3908_2022_45_44537 |
| url |
https://revistas.ufrj.br/index.php/aigeo/article/view/44537 |
| identifier_str_mv |
10.11137/1982-3908_2022_45_44537 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
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Copyright (c) 2022 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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Copyright (c) 2022 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 |
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openAccess |
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application/pdf |
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Universidade Federal do Rio de Janeiro |
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Universidade Federal do Rio de Janeiro |
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Anuário do Instituto de Geociências; Vol 45 (2022) Anuário do Instituto de Geociências; Vol 45 (2022) 1982-3908 0101-9759 reponame:Anuário do Instituto de Geociências (Online) instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
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Anuário do Instituto de Geociências (Online) |
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Anuário do Instituto de Geociências (Online) |
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Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ) |
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