Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

Tian, Lingwen; Liu, Xiangnan; Zhang, Biyao; Liu, Ming; Wu, Ling

doi:10.3390/ijerph14091018

Cited by 24 publications

(32 citation statements)

References 43 publications

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“…In China, the last two decades of anthropogenic activities, such as industrial pollution, livestock wastewater, mine drainage, and chemical pesticides, have led to heavy metal pollution in soil. Especially in farmlands, heavy metal pollution not only destroys the normal function of soils and hinders crop growth, but also endangers human health through the food chain [1,2]. As one of the most rapidly developing areas in China, Guangdong province is facing the serious problem of soil contamination, where it has been estimated that 40% of the soils in the Pearl River Delta are polluted by heavy metals [3].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and of 26 croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

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Section: Introductionmentioning

confidence: 99%

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

et al. 2019

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“…In 2004, Ensemble Empirical Mode Decomposition was proposed by Wu et al to deal with the mode mixing problem during EMD [ 45 ]. After 10 years of rapid development, EEMD is now widely applied in feature extraction [ 46 ], fault diagnosis [ 47 ], pattern recognition [ 48 ], etc. Assuming that the sound series can be decomposed into M intrinsic mode functions (IMFs) and a residue, the normalized energy of each IMF is calculated as the feature vector of the series.…”

Section: Algorithm Designmentioning

confidence: 99%

Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence–Based Variable Translation Wavelet Neural Network

Wang

Tan

et al. 2018

Sensors

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As a sound signal has the advantages of non-contacted measurement, compact structure, and low power consumption, it has resulted in much attention in many fields. In this paper, the sound signal of the coal mining shearer is analyzed to realize the accurate online cutting pattern identification and guarantee the safety quality of the working face. The original acoustic signal is first collected through an industrial microphone and decomposed by adaptive ensemble empirical mode decomposition (EEMD). A 13-dimensional set composed by the normalized energy of each level is extracted as the feature vector in the next step. Then, a swarm intelligence optimization algorithm inspired by bat foraging behavior is applied to determine key parameters of the traditional variable translation wavelet neural network (VTWNN). Moreover, a disturbance coefficient is introduced into the basic bat algorithm (BA) to overcome the disadvantage of easily falling into local extremum and limited exploration ability. The VTWNN optimized by the modified BA (VTWNN-MBA) is used as the cutting pattern recognizer. Finally, a simulation example, with an accuracy of 95.25%, and a series of comparisons are conducted to prove the effectiveness and superiority of the proposed method.

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“…Researchers have attempted to measure heavy metal stress levels by using physiological and spectral features, because heavy metal contaminants have direct or indirect influences on physiological parameters such as leaf area, dry weight, photosynthetic efficiency, and transpiration rate; these influences, in turn affect several spectral values in remote sensing images. Indices based on hyperspectral [6] or multispectral [7] images reflecting stress levels in rice, canopy-air temperature difference [8], crop growth models, like the WOrld FOod STudies (WOFOST) model [9], and the components of time series decomposition [10] have been proposed to discriminate heavy metal stress in rice on the basis of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Index Based on Time Series of Leaf Area Index for Identifying Heavy Metal Stress in Rice under Complex Stressors

Tang

Liu

et al. 2020

IJERPH

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Crops under various types of stresses, such as stress caused by heavy metals, drought and pest/disease exhibit similar changes in physiological-biochemical parameters (e.g., leaf area index [LAI] and chlorophyll). Thus, differentiating between heavy metal stress and nonheavy metal stress presents a great challenge. However, different stressors in crops do cause variations in spatiotemporal characteristics. This study aims to develop a spatiotemporal index based on LAI time series to identify heavy metal stress under complex stressors on a regional scale. The experimental area is located in Zhuzhou City, Hunan Province. The situ measured data and Sentinel-2A images from 2017 and 2018 were collected. First, a series of LAI in rice growth stages was simulated based on the WOrld FOod STudies (WOFOST) model incorporated with Sentinel 2 images. Second, the local Moran’s I and dynamic time warping (DTW) of LAI were calculated. Third, a stress index based on spatial and temporal features (SIST) was established to assess heavy metal stress levels according to the spatial autocorrelation and temporal dissimilarity of LAI. Results revealed the following: (1) The DTW of LAI is a good indicator for distinguishing stress levels. Specifically, rice subjected to high stress levels exhibits high DTW values. (2) Rice under heavy metal stress is well correlated with high-high SIST clusters. (3) Rice plants subjected to high pollution are observed in the northwest of the study regions and rice under low heavy metal stress is found in the south. The results suggest that SIST based on a sensitive indicator of rice biochemical impairment can be used to accurately detect regional heavy metal stress in rice. Combining spatial-temporal features and spectral information appears to be a highly promising method for discriminating heavy metal stress from complex stressors.

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Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

Cited by 24 publications

References 43 publications

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence–Based Variable Translation Wavelet Neural Network

Spatio-temporal Index Based on Time Series of Leaf Area Index for Identifying Heavy Metal Stress in Rice under Complex Stressors

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