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Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea—a potential tool for assessing the hazard degree of dust and salt storms

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Abstract

With the recession of the Aral Sea in Central Asia, once the world’s fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000–2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36 %. In parts of the Aralkum desalinization of soils was found to take place within 4–8 years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed.

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References

  • Atkinson, P. M., & Tatnall, A. R. L. (1997). Neural networks in remote sensing. International Journal of Remote Sensing, 18(4), 699–709.

    Article  Google Scholar 

  • Breckle, S. W., & Geldyeva, G. V. (2012). Dynamics of the Aral Sea in geological and historical times. In: Breckle, S.-W., Wucherer, W., Dimeyeva, L. A., & Ogar, N. P. (Eds.) Aralkum—a Man-Made Desert: The desiccated Floor of the Aral Sea (Central Asia), Ecological Studies. Berlin: Springer, 218:13–35.

  • Breckle, S.-W., & Wucherer, W. (2012). Climatic conditions in the Aralkum. In: Breckle, S.-W., Wucherer, W., Dimeyeva, L. A. & Ogar, N. P. (Eds.) Aralkum—a Man-Made Desert: The Desiccated Floor of the Aral Sea (Central Asia), Ecological Studies. Berlin: Springer, 218:49–72.

  • Breckle, S.-W., Wucherer, W., Agachanjanz, O., & Geldyer, B. (2001). The Aral Sea Crisis Region. In S.-W. Breckle, M. Veste, & W. Wucherer (Eds.), Sustainable land use in deserts (pp. 27–37). Berlin: Springer.

    Chapter  Google Scholar 

  • Briem, G. J., Benediktsson, J. A., & Sveinsson, J. R. (2002). Multiple classifiers applied to multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 40(10), 2291–2299.

    Article  Google Scholar 

  • Colditz, R. R., Conrad, C., Wehrmann, T., Schmidt, M., & Dech, S. (2008). TiSeG: flexible software tool for time-series generation of MODIS data utilizing the quality assessment science data set. IEEE Transactions on Geoscience and Remote Sensing, 46(10), 3296–3308.

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

    Article  Google Scholar 

  • Conrad, C., Dech, S. W., Hafeez, M., Lamers, J., Martius, C., & Strunz, G. (2007). Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products. Irrigation and Drainage Systems, 21(3–4), 197–218.

    Article  Google Scholar 

  • Datcu, M., Seidel, K., & Walessa, M. (1998). Spatial information retrieval from remote-sensing images—Part I: information theoretical perspective. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1431–1445.

    Article  Google Scholar 

  • DeFries, R. S., Hansen, M. C., Townshend, J. R. G., & Sohlberg, R. (1998). Global land cover classification at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(16), 3141–3168.

    Article  Google Scholar 

  • DiGregorio, A. D. (2005). Land cover classification system—Classification concepts and user manual for Software version 2. FAO Environment and Natural Ressources Service Series, No. 8, Rome.

  • Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M.-H., Féménias, P., Frerick, J., et al. (2012). The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sensing of Environment, 120, 37–57.

    Article  Google Scholar 

  • Dukhovny, V. A., Navratil, P., Rusiev, I., Stulina, G., & Roshenko, Y. E. (Eds.). (2008). Comprehensive remote sensing and ground based studies of the dried Aral Sea bed. Tashkent: Scientific-Information Center ICWC.

    Google Scholar 

  • Friedl, M. A., Brodley, C. E., & Strahler, A. H. (1999). Maximising land cover classification accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 969–977.

    Article  Google Scholar 

  • Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335–1343.

    Article  Google Scholar 

  • Fitzpatrick-Lins, K. (1981). Comparison of sampling procedures and data analysis for land-use and land-cover map. Photogrammetric Engineering and Remote Sensing, 47(3), 343–351.

    Google Scholar 

  • Fletcher, K. (2012). Sentinel-3: ESA’s global land and ocean mission for GMES operational services. ESA Communications, 97.

  • Fuller, R. M., Smith, G. M., & Devereux, B. J. (2003). The characterization and measurement of land cover change through remote sensing: problems in operational applications. International Journal of Applied Earth Observation and Geoinformation, 4, 243–253.

    Article  Google Scholar 

  • Huber, S. G., Kotte, K., Schoeler, H. F., & Williams, J. (2009). Natural abiotic formation of trihalomethanes (THM) in soil: results from laboratory studies and field samples. Environmental Science and Technologie, 43, 4934–4939.

    Article  CAS  Google Scholar 

  • Hansen, M. C., DeFries, R. S., Townshend, J. R. G., & Sohlberg, R. (2000). Global land-cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6–7), 1331–1364.

    Article  Google Scholar 

  • Indoitu, R., Orlovsky, L., & Orlovsky, N. (2012). Dust storms in Central Asia: spatial and temporal variations. Journal of Arid Environments, 85, 62–70.

    Article  Google Scholar 

  • Justice, C. O., Vermote, E. F., Townshend, J. R. G., DeFries, R. S., Roy, D. P., Hall, D. K., et al. (1998). The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228–1249.

    Article  Google Scholar 

  • Kotte, K., Löw, F., Huber, S. G., Krause, T., Mulder, I., & Schöler, H. F. S. (2012). Organohalogen emissions from saline environments—spatial extrapolation using remote sensing as most promising tool. Biogeosciences, 9, 1225–1235.

    Article  CAS  Google Scholar 

  • Lambin, E. F. (1996). Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photogrammetric Engineering and Remote Sensing, 62(8), 931–938.

    Google Scholar 

  • Létolle, R., & Mainguet, M. (Eds.) (1993). Aral. Paris: Springer.

  • Loh, W., & Shih, Y. (1997). Split selection methods for classification trees. Statistica Sinica, 7, 815–840.

    Google Scholar 

  • Löw, F., Navratil, P., & Bubenzer, O. (2012). Landscape Dynamics in the Southern Aralkum: Using MODIS Time Series for Land Cover Change Analysis. In S.-W. Breckle, W. Wucherer, L. A. Dimeyeva, & N. P. Ogar (Eds.), Aralkum—a Man-Made Desert (pp. 83–95). Berlin: Springer.

    Chapter  Google Scholar 

  • Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J., & Worthy, L. D. (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105, 142–154.

    Article  Google Scholar 

  • McIver, D. K., & Friedl, M. A. (2002). Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sensing of Environment, 81(2–3), 253–261.

    Article  Google Scholar 

  • Mees, F., & Singer, A. (2006). Surface crusts on soils/sediments of the southern Aral Sea basin, Uzbekistan. Geoderma, 136, 152–159.

    Article  CAS  Google Scholar 

  • Metternich, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85, 1–20.

    Article  Google Scholar 

  • Micklin, P. P. (2008). Using satellite remote sensing to study and monitor the Aral Sea and adjacent zone. In: Qi, J., & Evered, K. T. (Eds.) Environmental Problems of Central Asia and Their Economical, Social and Security Impacts (pp. 31–49). Heidelberg: Springer.

  • Micklin, P. P. (2010). The Past, Present, and Future Aral Sea. Lakes & Reservoirs: Research and Management, 5, 193–213.

    Google Scholar 

  • Orlovsky, N., Glantz, M., & Orlovsky, L. (2001). Irrigation and Land Degradation in the Aral Sea Basin. In: Breckle, S. W., Veste, M., & Wucherer, W. (Eds.) Sustainable Land Use in Deserts (pp. 52–69). Heidelberg: Springer.

  • Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, 554–565.

    Article  Google Scholar 

  • Ptichnikov, A. (1996). Environmental and landscape changes in the Aral Sea region as detected from remote sensing. In: Micklin, P., & Williams, D. W. (Eds.) The Aral Sea Basin. Heidelberg: Springer.

  • Razakov, R., & Kosnazarov, K. (1996). Dust and salt transfer from the exposed bed of the Aral Sea and measures to decrease its environmental impact. In: Micklin, P. P., & Williams, W. D. (Eds.) The Aral Sea Basin. NATO ASI Series, 2. Env. 12, 95–103.

  • Reed, B. C., Brown, J. F., VanderZee, D., Loveland, T. R., Merchant, J. W., & Ohlen, D. O. (1994). Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5), 703–714.

    Article  Google Scholar 

  • Roy, D. P., Borak, J. S., Devadiga, S., Wolfe, R. E., Zheng, M., & Descloitres, J. (2002). The MODIS land product quality assessment approach. Remote Sensing of Environment, 83(1–2), 62–76.

    Article  Google Scholar 

  • Singer, A., Zobeck, T., Poberrezky, L., & Argaman, E. (2003). The PM10 and PM2.5 dust generation potential of soils/sediments in the Southern Aral Sea Basin, Uzbekistan. Journal of Arid Environments, 54, 705–728.

    Article  Google Scholar 

  • Singh, A. (1989). Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing, 10(6), 989–1003.

    Article  Google Scholar 

  • Sivanpillai, R., & Latchininsky, V. (2007). Mapping locust habitats in the Amudarya river delta, Uzbekistan with multi-temporal MODIS imagery. Environmental Management, 39, 876–886.

    Article  Google Scholar 

  • Spivak, L., Terechov, A., Vitkovskaya, I., Batyrbayera, M., & Orlovsky, L. (2012). Dynamics of dust transfer from the desiccated Aral Sea bottom analysed by remote sensing. In: Breckle, S.-W., Wucherer, W., Dimeyeva, L. A., & Ogar, N. P. (Eds.). Aralkum—a Man-Made Desert: The Desiccated Floor of the Aral Sea (Central Asia), Ecological Studies. Berlin: Springer, 218:97–106.

  • Semenov, O. E. (2012). Dust storms and sandstorms and aerosol long-distance transport. In: Breckle, S.-W., Wucherer, W., Dimeyeva, L. A., & Ogar, N. P. (Eds.) Aralkum—a Man-Made Desert: The Desiccated Floor of the Aral Sea (Central Asia), Ecological Studies. Berlin: Springer, 218:73–82.

  • Surkova, G. V. (2010). Regional climate variability. In: Kostianoy, A. G., & Kosarev, A. N. (Eds.) The Aral Sea Environment, The Handbook of Environmental Chemistry. Berlin: Springer, 7:83–100).

  • Stulina, G., & Sektimenko, V. (2004). The change in soil cover on the exposed bed of the Aral Sea. Journal of Marine Systems, 47, 121–125.

    Article  Google Scholar 

  • Tucker, J. C. (1979). Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.

    Article  Google Scholar 

  • Vermote, E. F., el Saleous, N. Z., Justice, C. O., Kaufman, Y. J., Privette, J. L., Remer, L., et al. (1997). Atmospheric correction of visible to middle-infrared EOS—MODIS data over land surfaces: background, operational algorithm and validation. Journal of Geophysical Research, 102(D14), 17131–17141.

    Article  Google Scholar 

  • Wardlow, B. D., & Egbert, S. L. (2008). Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the US Central Great Plains. Remote Sensing of Environment, 112(3), 1096–1116.

    Article  Google Scholar 

  • Weissflog, L., Lange, C. A., Pfennigsdorf, A., Kotte, K., Elansky, N., Lisitzyna, L., et al. (2005). Sediments of salt lakes as new source of volatile highly chlorinated C1/C2 hydrocarbons. Geophysical Research Letters, 32(1), L01401.

    Article  Google Scholar 

  • Wiggs, G. F. S., O’Hara, S. L., Wegerdt, J., Van Der Meer, J., Small, I., & Hubbard, R. (2003). The dynamics and characteristics of aeolian dust in dryland Central Asia: possible impacts on human exposure and respiratory health in the Aral Sea basin. Geographical Journal, 169, 142–157.

    Article  Google Scholar 

  • Wucherer, W., & Breckle, S.-W. (2001). Vegetation dynamics on the dry sea floor of the Aral Sea. In: Breckle, S.-W., Veste, M., & Wucherer, W. (Eds.) Sustainable Land Use in Deserts. Heidelberg: Springer, 52–69.

  • Yuan, D., Elvidge, C. D., & Lunetta, R. S. (1998). Survey of multispectral methods for land cover change analysis. Remote sensing change detection: Environmental monitoring methods and applications. New York: Ann Arbor Press, 21–39.

  • Zhan, X., Sohlberg, R. A., Townshend, J. R. G., di Miceli, C., Carroll, M. L., Eastman, J. C., et al. (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83, 336–350.

    Article  Google Scholar 

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Acknowledgments

This study was carried out within the context of the HALOPROC project funded by the DfG (German Research Foundation, Research Unit 763). We would like to thank the Friedrich-Ebert Foundation for foundation of the research by way of a scholarship to the first author, and the GIZ (German Agency for International Cooperation) for logistical support of the ground surveys in Uzbekistan.

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Löw, F., Navratil, P., Kotte, K. et al. Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea—a potential tool for assessing the hazard degree of dust and salt storms. Environ Monit Assess 185, 8303–8319 (2013). https://doi.org/10.1007/s10661-013-3174-7

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