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Geostatistical Methods as a Tool Supporting Revitalization of Industrially Degraded and Post-Mining Areas

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Post-industrial and post-mining areas have often been under strong anthropogenic pressure for a long time. As a result, such areas, after the ending of industrial activity require taking steps to revitalize them. It may cover many elements of the natural or urban environment, such as water, soil, vegetated areas, urban development etc. To carry out revitalization, it is necessary to determine the initial state of such areas, often using selected chemical, geophysical or ecological. After that it is also important to properly monitor the state of such areas to assess the progress of the revitalization process. For this purpose a variety of change detection technics were developed. Post-industrial areas are very often characterized by a large extent, are difficult to access, have complicated land cover. For this reason, it is particularly important to choose appropriate methods to assess the degree of pollution of such areas. Such methods should be as economical as possible and time-effective. A very desirable feature of such methods is that they should allow a quick assessment of the entire area. Geostatistics supplemented by modern remote sensing can be effective for this purpose. Nowadays, using remote sensing, it is possible to gather information simultaneously from the entire, even vast area, with high spatial, spectral and temporal resolution. Geostatistics in turn provides many tools that are able to enable rapid analysis and inference based on even very complicated often scarce spatial data sets obtained from ground measurement and satellite observations. The goal of the article was to present selected results obtained using geostatistical methods also related to remote sensing, which may be helpful for decision makers in revitalizing post-industrial and post-mining areas. The results described in this paper were based mostly on the previous studies, carried out by authors.
Rocznik
Strony
30--40
Opis fizyczny
Bibliogr. 30 poz., rys.
Twórcy
  • Warsaw University of Technology, Poland
  • Warsaw University of Technology, Poland
  • Warsaw University of Technology, Poland
Bibliografia
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  • 3. D’Emilio M., Coluzzi R., Macchiato M., Imbrenda V., Ragosta M., Sabia S., Simoniello T., (2018). Satellite data and soil magnetic susceptibility measurements for heavy metals monitoring: findings from Agri Valley (Southern Italy). Environmental Earth Sciences, 77(3):63.
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  • 6. Fabijańczyk P., Zawadzki J. (2016). Geostatistical validation and cross-validation of magnetometric measurements of soil pollution with Potentially Toxic Elements in problematic areas. European Geosciences Union General Assembly 2016, Vienna, Austria, 17-22 April 2016.
  • 7. Fabijańczyk P., Zawadzki J. Magiera T.. (2017). Magnetometric assessment of soil contamination in problematic area using empirical Bayesian and indicator kriging: A case study Upper Silesia, Poland. Geoderma, 308, 69-77.
  • 8. Fabijańczyk P., Zawadzki J., Magiera T., (2019). Towards magnetometric characterization of soil pollution with rare earth elements in industrial areas of Upper Silesian Industrial Area, southern Poland. Environmental Earth Sciences 78, 352.
  • 9. Galfalk M., Basteviken D., (2018). Remote sensing of methane and nitrous oxide fluxes from waste incineration. Waste Management, 75, 319-326.
  • 10. He Ch., Gao B., Huang Q., Ma Q., Dou Y., (2017). Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data. Remote Sensing of Environment 193 (2017) 65-75.
  • 11. Isaaks, E.H. and Srivastava, R.M., (1989). An Introduction to Applied Geostatistics. Oxford, UK: Oxford University Press.
  • 12. Isidro C.M., McIntyre N., Lechner A.M., Callow I., (2017). Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region. Remote Sensing, 9, 945.
  • 13. Journel, A.G. and Ying, Z., (2001). The Theoretical Links Between Sequential Gaussian, Gaussian Truncated Simulation, and Probability Field Simulation. Mathematical Geology 33, 31.
  • 14. Magiera T., Zawadzki J., Szuszkiewicz M., Fabijańczyk P., Steines E., Fabian K., Miszczak E., (2018). Impact of an iron mine and nickel smelter in the Barents Region on surface soil magnetic susceptibility, potentially toxic elements content and its spatial correlation. Chemosphere, 195, 48-62.
  • 15. Musse M.A., Barona D.A., Rodriguez L.M.S., (2018). Urban environmental quality assessment using remote sensing and census data. International Journal of Applied Earth Observation and Geoinformation, 71, 95-108.
  • 16. Neocleous K.,Christofe A., Agapiou A., Evagorou E., Themistocleous K., Hadjimitsis D., (2016). Digital mapping of corrosion risk in coastal urban areas using remote sensing and structural condition assessment: case study in Cyprus. Open Geosciences, 8, 662–674.
  • 17. Przeździecki K., Zawadzki J., Miatkowski Z., (2018). Use of the temperature–vegetation dryness index for remote sensing grassland moisture conditions in the vicinity of a lignite open-cast mine. Environmental Earth Sciences, 77: 623.
  • 18. Przeździecki K, Zawadzki J., Cieszewski C., Bettinger P., (2017). Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery, Silva Fennica, 51 (4), article, id 1683.
  • 19. Rubio B., Nombela M.A., Vilas F., (2000). Geochemistry od major and trace elements in sediments of the Ria de Vigo (NW Spain) an assessment of metal pollution. Mar Pollut Bull 40(11), 968-980.
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  • 22. Zawadzki J., (2005). Wykorzystanie Metod Geostatystycznych w Badaniach Środowiska Przyrodniczego. Prace Naukowe Politechniki Warszawskiej, Inżynieria Środowiska, 49, Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa.
  • 23. Zawadzki, J., Cieszewski, C.J., Zasada, M., Lowe, R.C., (2005). Applying geostatistics for investigations of forest ecosystems using remote sensing imagery. Silva Fenn. 39 (4), 599-618.
  • 24. Zawadzki J., (2011). Metody geostatystyczne dla kierunków przyrodniczych i technicznych. Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa.
  • 25. Zawadzki J., Fabijańczyk P. (2015). Three-dimensional analysis of magnetic susceptibility in areas with different type of land use. European Geosciences Union General Assembly 2015, Vienna, Austria, 12-17 April 2015.
  • 26. Zawadzki J., Fabijańczyk P., (2013). Geostatistical evaluation of lead and zinc concentration in soils of an old mining area with complex land management. International Journal of Environmental Science and Technology, 10(4), 729-742.
  • 27. Zawadzki J., Magiera T., Fabijańczyk P., Kusza G., (2012). Geostatistical 3-dimensional integration of measurements of soil magnetic susceptibility. Environmental Monitoring and Assessment, 184(5), 3267-3278.
  • 28. Zawadzki J., Przeździecki K., Miatkowski Z., (2015). Determining the area of influence of depression cone in the vicinity of lignite mine by means of triangle method and LANDSAT TM/ETM satellite images. Journal of Environmental Management, 166, 605-614.
  • 29. Zawadzki J., Szuszkiewicz M., Fabijańczyk P., Magiera T., (2016). Geostatistical discrimination between a long-range and short-range soil pollution using field magnetometry. Chemosphere 164, 668-676.
  • 30. Zawadzki, J., Przeździecki, K., Szymankiewicz, K., Marczewski, W., (2013). Simple Method of Forest Type Inventory by Joining Low Resolution Remote Sensing of Vegetation Indices with Spatial Information from the Corine Land Cover Database, vol. 2013. ISRN Forestry, p.8. http://dx.doi.org/10.1155/2013/529193. Article ID 529193.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c8fb0d19-a034-4ded-ad7e-782c1773a183
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