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Tytuł pozycji:

Estimation and Spatial Analysis of Heavy Metals in Metal Tailing Pond Based on Improved PLS With Multiple Factors

Tytuł:
Estimation and Spatial Analysis of Heavy Metals in Metal Tailing Pond Based on Improved PLS With Multiple Factors
Autorzy:
Wang Rui
Wu Shuang
Wu Kan
Huang Shiqiao
Wu Ruijie
Liu Bo
Lin Min
Li Liang
Zhou Dawei
Diao Xinpeng
Temat:
Multi-spectral remote sensing image
heavy metals in soil
partial least-squares regression
fusion of multiple factors
spatial evolution analysis
GF-2
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Źródło:
IEEE Access, Vol 9, Pp 64880-64894 (2021)
Wydawca:
IEEE, 2021.
Rok publikacji:
2021
Kolekcja:
LCC:Electrical engineering. Electronics. Nuclear engineering
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2169-3536
Relacje:
https://ieeexplore.ieee.org/document/9406793/; https://doaj.org/toc/2169-3536
DOI:
10.1109/ACCESS.2021.3073933
Dostęp URL:
https://doaj.org/article/bfbadcbd3709440581b8a37c90a42069  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.bfbadcbd3709440581b8a37c90a42069
Czasopismo naukowe
With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.

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