The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W7, 1–4, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W7-1-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W7, 1–4, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W7-1-2019

  01 Mar 2019

01 Mar 2019

FEASIBILITY OF USING HYPERSPECTRAL REMOTE SENSING FOR ENVIRONMENTAL HEAVY METAL MONITORING

X. Chen, H. Lee, and M. Lee X. Chen et al.
  • Department of Safety, Health and Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

Keywords: Remote sensing, Hyperspectral data, Heavy metals, Non-destructive analysis, XRF, Pollution management strategy

Abstract. The use of optical properties as key parameters has been widely used in water quality monitoring, which accelerates the advances of remote sensing in the field of environmental monitoring. Current analytical methods for determining heavy metals in water include flame atomic absorption spectrometry (FAAS), atomic adsorption spectrophotometry (AAS) and inductively coupled plasma (ICP) spectroscopy, which typically require use of chemicals for sample processing and pretreatment as well as high capital input for analysis. Therefore, this study aims at investigating the potential of using non-destructive approaches for rapid water monitoring of heavy metal from green chemistry perspective. The proposed non-destructive sensing techniques include X-ray fluorescence spectrometer (XRF) and visible-near infrared spectroradiometer (VNIR). The former is an elemental analyser specifically for elements with relatively high atomic number, and the latter measures the reflectance or transmittance from samples. Heavy metals of lead (Pb), zinc (Zn) and copper (Cu) were selected as the target water constitutes in the study. The results from the analysis were then be used for determining a correlation model through chemometric approaches. Our results demonstrated that both of the target metals could be analysed via the proposed analytical methods. Reasonable agreements between the measurements from XRF and ICP were observed, whereas moderate correlations were perceived for simple linear regression model using spectral information from VNIR. Results from this study are expected to provide useful information on rapid identification of metal-polluting sources.