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Remote Sens. 2016, 8(12), 1003; doi:10.3390/rs8121003

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

1
Department of Agroecology, Faculty of Science and Technology, Aarhus University, Blichers Allé 20, P.O. Box 50, DK-8830 Tjele, Denmark
2
Department of Chemistry, College of resources and environment, Zunyi Normal College, Shanghai Road 830, Zunyi 563002, China
3
Department of Soil Science, FD Hole Soils Lab, University of Wisconsin-Madison, Madison, WI 53706, USA
4
Space Research Centre, Polish Academy of Sciences, Bartycka 18A, Warsaw 00-716, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Clement Atzberger and Prasad S. Thenkabail
Received: 12 September 2016 / Revised: 21 November 2016 / Accepted: 28 November 2016 / Published: 9 December 2016
View Full-Text   |   Download PDF [36093 KB, uploaded 9 December 2016]   |  

Abstract

After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0–30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals’ monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes. View Full-Text
Keywords: toxic metals; cubist; multi-spectral images; spectral indices; seasonal dependence; digital soil mapping toxic metals; cubist; multi-spectral images; spectral indices; seasonal dependence; digital soil mapping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Peng, Y.; Kheir, R.B.; Adhikari, K.; Malinowski, R.; Greve, M.B.; Knadel, M.; Greve, M.H. Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data. Remote Sens. 2016, 8, 1003.

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