铜污染土壤光谱特征及弱差信息的LH-PSD甄测模型

doi:10.3964/j.issn.1000-0593(2019)07-2228-09

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Abstract Soil environmental safety is of great significance. When soil is contaminated by heavy metals, it will affect the safety of crops and foods and endanger human health. Therefore, it is particularly critical to look for ways to rapidly and efficiently measure heavy metal pollution in soil. Traditional chemical analysis methods have some disadvantages such as complicated process, time-consuming and labor-consuming. Hyperspectral remote sensing has obvious advantages in environmental monitoring and other applications because of its high spectral resolution, large amount of information, and rapid losslessness. Due to the complex reflection and radiation process of electromagnetic remote sensing signals, the soil hyperspectral data acquired by the instrument is difficult to directly interpret the information of heavy metal pollution. Therefore, it is very important to find out a method that can effectively excavate heavy metal pollution information in soils. The soil physicochemical properties will change because of different concentrations pollution of Cu, causing slight changes in the soil spectrum, the purpose of this study is to identify, extract and analyze the characteristics and weak difference information in the spectrum of Cu contaminated soil, and then tap the heavy metal pollution information in the spectra. In this paper, the continuum removal(CR) was used to preprocess the spectrum, the LH-PSD analysis model for analyzing soil spectra was constructed bydefining local maximum mean (LMM) and half wave height(HWH), combined with Short-time Fourier transform (STFT) of the time-frequency analysis method and power spectral density (PSD). The extremely similar soil spectrum was processed by the LH-PSD model, and the PSD distribution map visualized the faint differences between the spectra, and significantly distinguished the similar spectra, which verified the ability of the model to discriminate spectral features and weak differential information. At the same time, this model was used to extract and analyze heavy metal pollution information from experimental soil spectra with different Cu pollution gradients. The results of the study show that CR can plan the spectrum to the same background and highlight the differences between spectra, LMM and HWH of LH-PSD detection model can effectively extract the characteristics of the difference between the spectra and appear in a ladder. The visualized PSD map obtained after the model processing can directly and qualitatively discriminate whether the soil is contaminated by heavy metal Cu. Specifically, when the soil is contaminated by heavy metal Cu, at the same sampling frequency, the PSD distribution at frequencies of 100 and 600 Hz will be obviousvacant separation, with the increase of Cu pollution concentration, the distribution of PSD between 100~600 Hz is gradually sparse. The energy value E can be used to quantitatively monitor the degree of soil Cu pollution. That is, as the concentration of Cu in the soil increases, the E value decreases, and the correlation coefficient with the Cu content reaches -0.910 5, which is significantly correlated. In order to test the reliability of the model, the soil spectra of the planted corn crop was combined and analyzed by the LH-PSD detection model. The result of the visualized PSD map was basically similar to that in the experimental analysis, The correlation coefficient of the energy value E with Cu content in soil reaches -0.973 9, which has a significant correlation. The monitoring effect is ideal and the reliability of the model is verified. Therefore, through the LH-PSD analysis model, the dissection of soil spectrum from the spectral domain to the time-frequency domain provides a new idea for deepening the spectral features and weak information of heavy metal pollution spectra.

Key words：Hyperspectral remote sensing; Heavy metal pollution; Characteristic information; Analysis model; Soil pollution monitoring

Received: 2018-05-23 Accepted: 2018-09-28

ZTFLH:

TP7

Corresponding Authors: ZHANG Wei E-mail: CU_zhangwei@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)07-2228-09 OR https://www.gpxygpfx.com/EN/Y2019/V39/I07/2228

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