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dc.contributor.author | Sahar, Awad A. | es_ES |
dc.contributor.author | Rasheed, Muaid J. | es_ES |
dc.contributor.author | Uaid, Dhia A. A.-H. | es_ES |
dc.contributor.author | Jasim, Ammar A. | es_ES |
dc.coverage.spatial | east=45.29938620000001; north=29.9133171; name=Al-Muthanna, Iraq | es_ES |
dc.date.accessioned | 2021-07-22T07:16:51Z | |
dc.date.available | 2021-07-22T07:16:51Z | |
dc.date.issued | 2021-07-21 | |
dc.identifier.issn | 1133-0953 | |
dc.identifier.uri | http://hdl.handle.net/10251/169764 | |
dc.description.abstract | [EN] 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. | es_ES |
dc.description.abstract | [ES] Las áreas arenosas son el principal problema en las regiones de clima árido y semiárido del mundo que amenazan la vida urbana, los edificios, la agricultura e incluso la salud humana. La teledetección es una de las tecnologías que puede utilizarse como una herramienta eficaz en el estudio de características dinámicas de áreas arenosas y acumulaciones de arena. En este estudio, se desarrollaron dos nuevos índices para separar las áreas arenosas de las áreas no arenosas. El primero llamado Índice de áreas arenosas diferenciales normalizadas (NDSAI), que se ha basado en el supuesto de que el área arenosa tiene el contenido de agua (humedad) más bajo que las otras clases de cobertura del suelo. El segundo llamado índice de temperatura superficial de las áreas arenosas (SASTI), que se basa en el supuesto de que la temperatura superficial del suelo arenoso es la más alta. Estos nuevos índices se han comparado con dos índices propuestos previamente por otros investigadores, a saber, el Índice de dunas de arena diferencial normalizado NDSI y el Eolain Mapping Index (EMI). La evaluación de la precisión de los índices arenosos mostró que el índice NDSAI proporciona un buen desempeño con una precisión general del 89 %. El índice SASTI puede extraer muchos píxeles arenosos y no arenosos con una precisión general del 86 %. El rendimiento del índice NDSI es pobre, con una precisión general del 82 %, no puede clasificar o aislar el área de vegetación del área arenosa y tal vez funcione mejor en entornos desérticos. El índice NDSAI calculado con la banda SWIR1 del satélite Landsat generó resultados más precisos que el NDSI calculado con la banda SWIR2 del mismo satélite. El índice EMI utilizado fue menos robusto que los otros métodos ya que no ha logrado extraer áreas arenosas con una precisión aceptable en áreas con diversas coberturas terrestres. Se utilizaron técnicas de detección de cambios para analizar las áreas de las tierras arenosas para los períodos de 1987 a 2017. Los resultados marcaron un aumento en las áreas arenosas durante cuatro décadas. El porcentaje de este aumento fue de aproximadamente 20 % a 30 % durante 2002 y 2017 en comparación con 1987. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | Revista de Teledetección | es_ES |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Remote sensing | es_ES |
dc.subject | Sand dunes | es_ES |
dc.subject | Eolin mapping index | es_ES |
dc.subject | Landsat images | es_ES |
dc.subject | NDSAI | es_ES |
dc.subject | Teledetección | es_ES |
dc.subject | Dunas de arena | es_ES |
dc.subject | Índice de mapeo Eolin | es_ES |
dc.subject | Imágenes Landsat | es_ES |
dc.title | Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq | es_ES |
dc.title.alternative | Cartografiado de áreas arenosas y sus cambios mediante teledetección. Caso de estudio en el noreste de la provincia de Al-Muthanna, sur de Irak | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.4995/raet.2021.13622 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Sahar, AA.; Rasheed, MJ.; Uaid, DAA.; Jasim, AA. (2021). Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq. Revista de Teledetección. 0(58):39-52. https://doi.org/10.4995/raet.2021.13622 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.4995/raet.2021.13622 | es_ES |
dc.description.upvformatpinicio | 39 | es_ES |
dc.description.upvformatpfin | 52 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 0 | es_ES |
dc.description.issue | 58 | es_ES |
dc.identifier.eissn | 1988-8740 | |
dc.relation.pasarela | OJS\13622 | es_ES |
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