Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq
Sandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulat...
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Universitat Politècnica de València
2021-07-01
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| Series: | Revista de Teledetección |
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| Online Access: | https://polipapers.upv.es/index.php/raet/article/view/13622 |
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| author | Awad A. Sahar Muaid J. Rasheed Dhia A. A.-H. Uaid Ammar A. Jasim |
| author_facet | Awad A. Sahar Muaid J. Rasheed Dhia A. A.-H. Uaid Ammar A. Jasim |
| author_sort | Awad A. Sahar |
| collection | DOAJ |
| description | Sandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. In this study, two new indices were developed to separate the sandy areas from the non-sandy areas. The first one is called the Normalized Differential Sandy Areas Index (NDSAI) that has been based on the assumption that the sandy area has the lowest water content (moisture) than the other land cover classes. The second other is called the Sandy Areas Surface Temperature index (SASTI) which was built on the assumption that the surface temperature of sandy soil is the highest. The results of proposed indices have been compared with two indices that were previously proposed by other researchers, namely the Normalized Differential Sand Dune Index NDSI and the Eolain Mapping Index (EMI). The accuracy assessment of the sandy indices showed that the NDSAI provides very good performance with an overall accuracy of 89 %. The SASTI can isolate many sandy and non-sandy pixels with an overall accuracy about 86 %. The performance of the NDSI is low with an overall accuracy about 82 %. It fails to classify or isolate the vegetation area from the sandy area and might have better performance in desert environments. The performing of NDSAI that is calculated with the SWIR1 band of the Landsat satellite is better than the performing of NDSI that is calculated with the SWIR2 band of the same satellite. EMI performance is less robust than other methods as it is not useful for extracting sandy surfaces in area with different land covers. Change detection techniques were used by comparing the areas of the sandy lands for the periods from 1987 to 2017. The results showed an increase in sandy areas over four decades. The percentage of this increase was about 20 % to 30 % during 2002 and 2017 compared to 1987. |
| format | Article |
| id | doaj-art-c76dfa57f3f44f0fb909f2680a49063d |
| institution | Kabale University |
| issn | 1133-0953 1988-8740 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Revista de Teledetección |
| spelling | doaj-art-c76dfa57f3f44f0fb909f2680a49063d2025-01-02T07:46:19ZengUniversitat Politècnica de ValènciaRevista de Teledetección1133-09531988-87402021-07-01058395210.4995/raet.2021.136228846Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of IraqAwad A. Sahar0Muaid J. Rasheed1Dhia A. A.-H. Uaid2Ammar A. Jasim3Middle Technical UniversityBaghdad UniversityWasit UniversityRemote Sensing CenterSandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. In this study, two new indices were developed to separate the sandy areas from the non-sandy areas. The first one is called the Normalized Differential Sandy Areas Index (NDSAI) that has been based on the assumption that the sandy area has the lowest water content (moisture) than the other land cover classes. The second other is called the Sandy Areas Surface Temperature index (SASTI) which was built on the assumption that the surface temperature of sandy soil is the highest. The results of proposed indices have been compared with two indices that were previously proposed by other researchers, namely the Normalized Differential Sand Dune Index NDSI and the Eolain Mapping Index (EMI). The accuracy assessment of the sandy indices showed that the NDSAI provides very good performance with an overall accuracy of 89 %. The SASTI can isolate many sandy and non-sandy pixels with an overall accuracy about 86 %. The performance of the NDSI is low with an overall accuracy about 82 %. It fails to classify or isolate the vegetation area from the sandy area and might have better performance in desert environments. The performing of NDSAI that is calculated with the SWIR1 band of the Landsat satellite is better than the performing of NDSI that is calculated with the SWIR2 band of the same satellite. EMI performance is less robust than other methods as it is not useful for extracting sandy surfaces in area with different land covers. Change detection techniques were used by comparing the areas of the sandy lands for the periods from 1987 to 2017. The results showed an increase in sandy areas over four decades. The percentage of this increase was about 20 % to 30 % during 2002 and 2017 compared to 1987.https://polipapers.upv.es/index.php/raet/article/view/13622remote sensingsand duneseolin mapping indexlandsat imagesndsai |
| spellingShingle | Awad A. Sahar Muaid J. Rasheed Dhia A. A.-H. Uaid Ammar A. Jasim Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq Revista de Teledetección remote sensing sand dunes eolin mapping index landsat images ndsai |
| title | Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq |
| title_full | Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq |
| title_fullStr | Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq |
| title_full_unstemmed | Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq |
| title_short | Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq |
| title_sort | mapping sandy areas and their changes using remote sensing a case study at north east al muthanna province south of iraq |
| topic | remote sensing sand dunes eolin mapping index landsat images ndsai |
| url | https://polipapers.upv.es/index.php/raet/article/view/13622 |
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