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Guo H, Yang K, Wu F, Chen Y, Shen J. Regional Inversion of Soil Heavy Metal Cr Content in Agricultural Land Using Zhuhai-1 Hyperspectral Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:8756. [PMID: 37960456 PMCID: PMC10650011 DOI: 10.3390/s23218756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/22/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023]
Abstract
With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite images are susceptible to environmental factors such as atmospheric and soil background, presenting a significant challenge in the accurate estimation of soil heavy metal concentrations. In this study, typical chromium (Cr)-contaminated agricultural land in Shaoguan City, Guangdong Province, China, was taken as the study area. Soil sample collection, Cr content determination, laboratory spectral measurements, and hyperspectral satellite image collection were carried out simultaneously. The Zhuhai-1 hyperspectral satellite image spectra were corrected to match laboratory spectra using the direct standardization (DS) algorithm. Then, the corrected spectra were integrated into an optimal model based on laboratory spectral data and sample Cr content data for regional inversion of soil heavy metal Cr content in agricultural land. The results indicated that the combination of standard normal variate (SNV)+ uninformative variable elimination (UVE)+ support vector regression (SVR) model performed best with laboratory spectral data, achieving a high accuracy with an R2 of 0.97, RMSE of 5.87, MAE of 4.72, and RPD of 4.04. The DS algorithm effectively transformed satellite hyperspectral image data into spectra resembling laboratory measurements, mitigating the impact of environmental factors. Therefore, it can be applied for regional inversion of soil heavy metal content. Overall, the study area exhibited a low-risk level of Cr content in the soil, with the majority of Cr content values falling within the range of 36.21-76.23 mg/kg. Higher concentrations were primarily observed in the southeastern part of the study area. This study can provide useful exploration for the promotion and application of Zhuhai-1 image data in the regional inversion of soil heavy metals.
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Affiliation(s)
- Hongxu Guo
- School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China; (H.G.); (K.Y.); (F.W.)
| | - Kai Yang
- School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China; (H.G.); (K.Y.); (F.W.)
| | - Fan Wu
- School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China; (H.G.); (K.Y.); (F.W.)
| | - Yu Chen
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Jinxiang Shen
- School of Land and Space Information, Yunnan Land and Resources Vocational College, Kunming 652501, China;
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Sun W, Liu S, Wang M, Zhang X, Shang K, Liu Q. Soil copper concentration map in mining area generated from AHSI remote sensing imagery. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160511. [PMID: 36442635 DOI: 10.1016/j.scitotenv.2022.160511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/16/2022] [Accepted: 11/22/2022] [Indexed: 06/16/2023]
Abstract
Hyperspectral remote sensing has the advantages to predict and map soil heavy metal concentration over conventional monitoring methods and multispectral remote sensing. In quantitative applications of hyperspectral remote sensing imagery, the contribution of hyperspectral bands is different, and abnormal prediction values resulted from incorrectly classified bare soil images are a major problem. In this study, a variable weighting method was proposed to weight the hyperspectral bands, and a probability threshold was used to improve the classification to mitigate the problem of abnormal prediction values. The variable weighting was conducted by using the absorption depths obtained by continuum removal. Soil samples were collected from a mining area in southwestern China. Hyperspectral remote sensing imagery was acquired by the Advanced Hyperspectral Imager (AHSI) abroad on Geofen-5 (GF-5) satellite. Genetic algorithm and partial least squares regression (PLSR) were adopted to calibrate prediction models. In prediction of soil copper (Cu) concentration, root mean square error (RMSE) and coefficient of determination (R2) were 21.59 mg kg-1 and 0.60 for the prediction using raw reflectance spectra, and the values were improved to 18.33 mg kg-1 and 0.71 by using the weighted reflectance spectra. The developed prediction model was applied to the AHSI imagery to predict Cu concentration in bare soil areas. In prediction of Cu concentration using the AHSI imagery, negative prediction values were eliminated by using the bare soil image extracted by the improved classification. Based on the prediction, soil Cu concentration map was generated by kriging spatial interpolation. The result indicates that the proposed variable weighting method is effective and the problem of abnormal prediction values could be mitigated by using improved bare soil images. Further analysis indicates that some indices with proper thresholds also could be used to get improved bare soil images.
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Affiliation(s)
- Weichao Sun
- Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China.
| | - Shuo Liu
- Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Mengfei Wang
- China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Ministry of Natural Resources, No.31 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Xia Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Kun Shang
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, No.1 Baisheng Village, Haidian District, Beijing 100048, China
| | - Qingjie Liu
- Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
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Guo B, Wu H, Pei L, Zhu X, Zhang D, Wang Y, Luo P. Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign. ENVIRONMENT INTERNATIONAL 2022; 170:107606. [PMID: 36335896 DOI: 10.1016/j.envint.2022.107606] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 μg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 μg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 μg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 μg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, Shaanxi 710068, China; School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710043, China.
| | - Xiaowei Zhu
- Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA.
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Pingping Luo
- School of Water and Environment, Chang'an University, Xi'an, Shaanxi 710054, China.
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Wang Y, Guo B, Pei L, Guo H, Zhang D, Ma X, Yu Y, Wu H. The influence of socioeconomic and environmental determinants on acute myocardial infarction (AMI) mortality from the spatial epidemiological perspective. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:63494-63511. [PMID: 35460483 DOI: 10.1007/s11356-022-19825-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
Plenty of epidemiological approaches have been explored to detect the effects of environmental and socioeconomic factors on acute myocardial infarction (AMI) mortality. Whereas, identifying the influence of potential affecting factors on AMI mortality based on a spatial epidemiological perspective was strongly desired. Moreover, the interaction effects of two potential factors on the diseases were always neglected previously. Here, the Geodetector and geographically & temporally weighted regression model (GTWR) combined with multi-source spatiotemporal datasets were introduced to quantitatively determine the relationship between AMI mortality and potential influencing factors across Xi'an during 2014-2016. Besides, Moran's I was adopted to diagnose the spatial autocorrelation of AMI mortality. Some findings were achieved. The number of AMI mortality cases increased from 5075 in 2014 to 6774 in 2016. Air pollutants, meteorological factors, economic status, and topography factors exhibited a significant effect on AMI mortality. The AMI mortality demonstrated an obvious spatial autocorrelation feature during 2014-2016. POP and PE represented the most obvious impact on AMI mortality, respectively. Moreover, the interaction of any two factors was larger than that of the single factor on AMI mortality, and the factors with the strongest interaction vary according to lag groups and ages. The effects of factors on AMI mortality were POP (- 628.925) > PE (140.102) > RD (79.145) > O3 (- 58.438) > E_NH3 (42.370) for male, and POP (- 751.206) > RD (132.935) > E_NH3 (58.758) > PE (- 45.434) > O3 (- 21.256) for female, respectively. This work reminds the local government to continuously control air pollution, strengthen urban planning, and improve the health care of the rural areas for alleviating AMI mortality. Meanwhile, the scheme of the current study supplies a scientific reference for examining the effects of potential impact factors on related diseases using the spatial epidemiological perspective.
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Affiliation(s)
- Yan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Lin Pei
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hongjun Guo
- Weinan Central Hospital, Weinan, Shaanxi, China.
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Yan Yu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
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Zhang B, Guo B, Zou B, Wei W, Lei Y, Li T. Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 300:118981. [PMID: 35150799 DOI: 10.1016/j.envpol.2022.118981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/21/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Soil heavy metals pollution has been becoming one of the severely environmental issues globally. Previous studies reported laboratory-measured spectra could be used to infer soil heavy metals concentrations to some extent. However, using field-obtained spectra to estimate soil heavy metals concentrations is still a great challenge due to the low precision and weak efficiency at large scales. The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy metals concentrations of samples were measured under laboratory conditions. The direct standardization (DS) algorithm was introduced to calibrate the Gaofen-5 (GF-5) hyperspectral image based on the measured spectra of samples. The spectral reflectance of the GF-5 hyperspectral image was reconstructed using continuous wavelet transform (CWT) at different scales. The characteristic bands of GF-5 for estimating heavy metals concentrations were selected by the Boruta algorithm. Finally, the random forest (RF), the extreme learning machine (ELM), the support vector machine (SVM), and the back-propagation neural network (BPNN) algorithms were used to predict the heavy metals concentrations. Some findings were achieved. First, CWT can effectively eliminate the noise of satellite hyperspectral data. The characteristic bands of Zn (480-677, 827-1029, 1241-1334, 1435-1797, and 1949-2500 nm), Ni (514-630, 835-985, 1258-1325, 1460-1578, and 1949-2319 nm), and Cu (822-831; 1029-1300, 1486-1595, and 1730-2294 nm) can be effectively retrieved via the Boruta algorithm. Second, the estimation accuracy was significantly improved by using the DS algorithm. For zinc (Zn), nickel (Ni), and copper (Cu), the determination coefficients of the validation dataset (Rv2) were 0.77 (RF), 0.62 (RF), and 0.56 (ELM), respectively. Third, the distribution trends of heavy metals were almost consistent with the results of actual ground measurements. This paper revealed that the GF-5 can be one of the reliable satellite hyperspectral imagery for mapping soil heavy metals.
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Affiliation(s)
- Bo Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China
| | - Wei Wei
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yongzhi Lei
- China Power Construction Group Northwest Survey, Design and Research Institute Co, Ltd, Xi'an, 710065, China
| | - Tianqi Li
- China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, 100083, China
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