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Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq

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Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq

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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
dc.description.references Abbas, A. 2010. Desertification Study of Dalmaj Lake Area in Mesopotamian Plain by Using Remote Sensing Techniques. Baghdad University. es_ES
dc.description.references Abdul-Ameer, E.A. 2012. The geomorphological study of dune fields and their environmental effects at Al-Muthana Governorate Iraq. D. Sc. thesis, University of Baghdad, College of Science. 163p. es_ES
dc.description.references Acharya, T.D., Yang, I. 2015. Exploring landsat 8. International Journal of IT, Engineering and Applied Sciences Research, 4(4), 4-10. es_ES
dc.description.references Agapiou, A. 2020. Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies. Remote Sensing, 12(3), 579. https://doi.org/10.3390/rs12030579 es_ES
dc.description.references Al-Khateeb A. 2007. Climatic Changes and it's affect on geodynamic processes in Iraq during (1940-2000). es_ES
dc.description.references Avdan, U., Jovanovska, G. 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, 2016. https://doi.org/10.1155/2016/1480307 es_ES
dc.description.references Azzaoui, M.A., Adnani, M., El Belrhiti, H., Chaouki, I.E., Masmoudi, L. 2019. Detection of crescent sand dunes contours in satellite images using an active shape model with a cascade classifier. ISPAr, 4212, 17-24. https://doi.org/10.5194/isprs-archives-XLII-4-W12-17-2019 es_ES
dc.description.references Bagnold, R.A. 2012. The physics of blown sand and desert dunes. Courier Corporation. es_ES
dc.description.references Baranoski, G.V.G., Kimmel, B.W., Chen, T.F., Miranda, E., Yim, D. 2013. Effects of sand grain shape on the spectral signature of sandy landscapes in the visible domain. 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, 3060-3063. https://doi.org/10.1109/IGARSS.2013.6723472 es_ES
dc.description.references Breed, C.S, Fryberger, S.G., Andrews, S., McCauley, C., Lennartz, F., Gebel, D., Horstman, K. 1979a. Regional studies of sand seas using Landsat (ERTS) imagery. In A study of global sand seas (Vol. 1052, pp. 305-397). US Geological Survey, Professional Paper. es_ES
dc.description.references Breed, C.S, Grolier, M.J., McCauley, J.F. 1979b. Morphology and distribution of common 'sand'dunes on Mars: Comparison with the Earth. Journal of Geophysical Research: Solid Earth, 84(B14), 8183- 8204. https://doi.org/10.1029/JB084iB14p08183 es_ES
dc.description.references Brown, D.G., Arbogast, A.F. 1999. Digital photogrammetric change analysis as applied to active coastal dunes in Michigan. Photogrammetric Engineering and Remote Sensing, 65, 467-474. es_ES
dc.description.references Buday, T. 1980. The regional geology of Iraq: stratigraphy and paleogeography (Vol. 1). State Organization. Christensen, P.R. 1983. Eolian intracrater deposits on Mars: Physical properties and global distribution. Icarus, 56(3), 496-518. https://doi.org/10.1016/0019-1035(83)90169-0 es_ES
dc.description.references Christensen, P.R. 1983. Eolian intracrater deposits on Mars: Physical properties and global distribution. Icarus, 56(3), 496-518. https://doi.org/10.1016/0019-1035(83)90169-0 es_ES
dc.description.references Fabre, S., Briottet, X., Lesaignoux, A. 2015. Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4-2.5 µm domain. Sensors, 15(2), 3262-3281. https://doi.org/10.3390/s150203262 es_ES
dc.description.references Fadhil, A.M. 2009. Land degradation detection using geo-informationtechnology for some sites in Iraq. Journal of Al-Nahrain University-Science, 12(3), 94-108. https://doi.org/10.22401/JNUS.12.3.13 es_ES
dc.description.references Fadhil, A.M. 2013. Sand dunes monitoring using remote sensing and GIS techniques for some sites in Iraq. PIAGENG 2013: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 8762, 876206. https://doi.org/10.1117/12.2019735 es_ES
dc.description.references Fenton, L.K., Mellon, M.T. 2006. Thermal properties of sand from Thermal Emission Spectrometer (TES) and Thermal Emission Imaging System (THEMIS): spatial variations within the Proctor Crater dune field on Mars. Journal of Geophysical Research: Planets, 111(E6). https://doi.org/10.1029/2004JE002363 es_ES
dc.description.references Frey, C.M., Kuenzer, C. 2015. Analysing a 13 years MODIS land surface temperature time series in the Mekong Basin. In Remote Sensing Time Series (pp. 119-140). Springer. https://doi.org/10.1007/978-3-319-15967-6_6 es_ES
dc.description.references Gao, B.C. 1996. NDWI -A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3 es_ES
dc.description.references Hexagon Geospatial. 2015. Erdas Imagine. Hexagon AB: Stockholm, Switzerland. es_ES
dc.description.references Ghulam, A., Hall, M. 2010. Calculating surface temperature using Landsat thermal imagery. Department of Earth & Atmospheric Sciences, and Create for Environmental Sciences. Saint Louis University. es_ES
dc.description.references USGS. 2018. Landsat 8 surface reflectance code (LaSRC) product. Available at https://Landsat.Usgs. Gov/Sites/Default/Files/Documents/Lasrc_product_ guide.Pdf (Accessed on 26 December 2018). es_ES
dc.description.references Haubrock, S.N., Chabrillat, S., Kuhnert, M., Hostert, P., Kaufmann, H. 2008. Surface soil moisture quantification and validation based on hyperspectral data and field measurements. Journal of applied remote sensing, 2(1), 023552. https://doi.org/10.1117/1.3059191 es_ES
dc.description.references Hillel, D., Hatfield, J.L. 2005. Encyclopedia of Soils in the Environment (Vol. 3). Elsevier Amsterdam. es_ES
dc.description.references Hugenholtz, C.H., Levin, N., Barchyn, T.E., Baddock, M.C. 2012. Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook. Earth-Science Reviews, 111(3-4), 319-334. https://doi.org/10.1016/j.earscirev.2011.11.006 es_ES
dc.description.references Jasim AL-a'araage, A.A. 2012. Monitoring Desertification in Badra Area Eastern Iraq by Using Landsat Image Data. Baghdad University. es_ES
dc.description.references Jassim, S.Z., Goff, J.C. 2006. Geology of Iraq. DOLIN, sro, distributed by Geological Society of London. es_ES
dc.description.references Khiry, M.A. 2007. Spectral mixture analysis for monitoring and mapping desertification processes in semi-arid areas in North Kordofan State, Sudan. Published PhD Thesis, University of Dresden, Germany. es_ES
dc.description.references Kourdian, R. 2009. Analyse de la traficabilité en zone tropicale par imagerie spatiale optique et radar: application au Tchad méridional. École Nationale Supérieure des Mines de Paris. es_ES
dc.description.references Landsat, U. 2019. Surface Reflectance Code (LASRC) Product Guide. USGS and NASA: Reston, VA, USA. es_ES
dc.description.references Lee, J.K., Acharya, T.D., Lee, D.H. 2018. Exploring land cover classification accuracy of Landsat 8 image using spectral index layer stacking in hilly region of South Korea. Sensors and Materials, 30(12), 2927- 2941. https://doi.org/10.18494/SAM.2018.1934 es_ES
dc.description.references Levin, N., Ben-Dor, E. 2004. Monitoring sand dune stabilization along the coastal dunes of Ashdod-Nizanim, Israel, 1945-1999. Journal of Arid Environments, 58(3), 335-355. https://doi.org/10.1016/j.jaridenv.2003.08.007 es_ES
dc.description.references Lillesand, T.M., Kiefer, R.W. 2000. Remote sensing and image interpretation. John Wiley & Sons. es_ES
dc.description.references Loyd, C. 2013. Landsat 8 Bands «Landsat Science. https://landsat.gsfc.nasa.gov/landsat-8/landsat-8- bands/ es_ES
dc.description.references McKee, E.D. 1979. Introduction to a study of global sand seas. In A study of global sand seas (Vol. 1052, pp. 1-19). Professional Paper. https://doi.org/10.3133/pp1052 es_ES
dc.description.references Paisley, E.C.I., Lancaster, N., Gaddis, L.R., Greeley, R. 1991. Discrimination of active and inactive sand from remote sensing: Kelso Dunes, Mojave Desert, California. Remote Sensing of Environment, 37(3), 153-166. https://doi.org/10.1016/0034-4257(91)90078-K es_ES
dc.description.references Pease, P.P., Bierly, G.D., Tchakerian, V.P., Tindale, N.W. 1999. Mineralogical characterization and transport pathways of dune sand using Landsat TM data, Wahiba Sand Sea, Sultanate of Oman. Geomorphology, 29(3-4), 235-249. https://doi.org/10.1016/S0169-555X(99)00029-X es_ES
dc.description.references Pye, K., Tsoar, H. 2008. Aeolian sand and sand dunes. Springer Science & Business Media. https://doi.org/10.1007/978-3-540-85910-9 es_ES
dc.description.references Ramsey, M.S., Christensen, P.R., Lancaster, N., Howard, D.A. 1999. Identification of sand sources and transport pathways at the Kelso Dunes, California, using thermal infrared remote sensing. Geological Society of America Bulletin, 111(5), 646-662. https://doi.org/10.1130/0016-7606(1999)111<0646:IOSSAT>2.3.CO;2 es_ES
dc.description.references Rokni, K., Ahmad, A., Selamat, A., Hazini, S. 2014. Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing, 6(5), 4173-4189. https://doi.org/10.3390/rs6054173 es_ES
dc.description.references Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309. es_ES
dc.description.references State Company for Geological Survey and mining. 2012. Geological Map of Al-Nasiriya Quadrangle. es_ES
dc.description.references Tsoar, H., Karnieli, A. 1996. What determines the spectral reflectance of the Negev-Sinai sand dunes. International Journal of Remote Sensing, 17(3), 513- 525. https://doi.org/10.1080/01431169608949024 es_ES
dc.description.references USGS. 2016. Landsat Surface Reflectance Level-2 Science Products | Landsat Missions. https://landsat. usgs.gov/landsat-surface-reflectance-data-products es_ES
dc.description.references Walker, R.A. 2009. The country in the city: the greening of the San Francisco Bay Area. University of Washington Press. es_ES
dc.description.references Wasson, R.J., Hyde, R. 1983. Factors determining desert dune type. Nature, 3045924, 337-339. https://doi.org/10.1038/304337a0 es_ES
dc.description.references Wilson, E.H., Sader, S.A. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385-396. https://doi.org/10.1016/S0034- 4257(01)00318-2 es_ES
dc.description.references Wolfe, S.A., Hugenholtz, C.H. 2009. Barchan dunes stabilized under recent climate warming on the northern Great Plains. Geology, 37(11), 1039-1042. https://doi.org/10.1130/G30334A.1 es_ES
dc.description.references Yamani M., Karami, F. 2011. Main Processes to Form and Move Morphology of Dunes in Khuzestan Plain (Case Study: Ahvaz North Sand). Geographical Studies of Arid Places, 2. es_ES
dc.description.references Zanter, K. 2016. Landsat 8 (L8) data users handbook. Landsat Science Official Website. es_ES
dc.description.references 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, 24(3), 583-594. https://doi.org/10.1080/01431160304987 es_ES
dc.description.references Zhang, Y.F., Wang, X.P., Pan, Y.X., Hu, R. 2012. Diurnal relationship between the surface albedo and surface temperature in revegetated desert ecosystems, Northwestern China. Arid Land Research and Management, 26(1), 32-43. https://doi.org/10.1080/15324982.2011.631687 es_ES


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