Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq
DOI:
https://doi.org/10.4995/raet.2021.13622Keywords:
remote sensing, sand dunes, Eolin mapping index, Landsat images, NDSAIAbstract
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.
Downloads
References
Abbas, A. 2010. Desertification Study of Dalmaj Lake Area in Mesopotamian Plain by Using Remote Sensing Techniques. Baghdad University.
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.
Acharya, T.D., Yang, I. 2015. Exploring landsat 8. International Journal of IT, Engineering and Applied Sciences Research, 4(4), 4-10.
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
Al-Khateeb A. 2007. Climatic Changes and it's affect on geodynamic processes in Iraq during (1940-2000).
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
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
Bagnold, R.A. 2012. The physics of blown sand and desert dunes. Courier Corporation.
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
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.
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
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.
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
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
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
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
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
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
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
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
Hexagon Geospatial. 2015. Erdas Imagine. Hexagon AB: Stockholm, Switzerland.
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.
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).
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
Hillel, D., Hatfield, J.L. 2005. Encyclopedia of Soils in the Environment (Vol. 3). Elsevier Amsterdam.
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
Jasim AL-a'araage, A.A. 2012. Monitoring Desertification in Badra Area Eastern Iraq by Using Landsat Image Data. Baghdad University.
Jassim, S.Z., Goff, J.C. 2006. Geology of Iraq. DOLIN, sro, distributed by Geological Society of London.
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.
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.
Landsat, U. 2019. Surface Reflectance Code (LASRC) Product Guide. USGS and NASA: Reston, VA, USA.
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
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
Lillesand, T.M., Kiefer, R.W. 2000. Remote sensing and image interpretation. John Wiley & Sons.
Loyd, C. 2013. Landsat 8 Bands «Landsat Science. https://landsat.gsfc.nasa.gov/landsat-8/landsat-8- bands/
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
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
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
Pye, K., Tsoar, H. 2008. Aeolian sand and sand dunes. Springer Science & Business Media. https://doi.org/10.1007/978-3-540-85910-9
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
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
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.
State Company for Geological Survey and mining. 2012. Geological Map of Al-Nasiriya Quadrangle.
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
USGS. 2016. Landsat Surface Reflectance Level-2 Science Products | Landsat Missions. https://landsat. usgs.gov/landsat-surface-reflectance-data-products
Walker, R.A. 2009. The country in the city: the greening of the San Francisco Bay Area. University of Washington Press.
Wasson, R.J., Hyde, R. 1983. Factors determining desert dune type. Nature, 3045924, 337-339. https://doi.org/10.1038/304337a0
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
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
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.
Zanter, K. 2016. Landsat 8 (L8) data users handbook. Landsat Science Official Website.
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
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
Downloads
Published
Issue
Section
License
This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
