Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering

Tongshan, China

Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering

Tongshan, China
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Teng J.-Y.,China University of Mining and Technology | Teng J.-Y.,Mapping and Geo information NASG Key Laboratory of Land Environment and Disaster Monitoring | Teng J.-Y.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Qin K.,China University of Mining and Technology | And 9 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2017

The atmospheric aerosols have significant influence on human health, the environment and the climate system. The atmospheric boundary layer (ABL) reflects processes of the near-surface atmosphere and concentration of pollutants. Ground-based laser radar can monitor the vertical distribution of atmospheric aerosols stably and continuously. It provides dynamic information for timing observations of the ABL and environmental forecasting, if aerosols can be monitored and evaluated using lidar technology. There is a gap in the study of ABL observations during the presence of a residual layer and aerosol intrusion, as well as deficiencies in the accuracy and poor computational efficiency of the gradient method. This paper combines the physical meaning of the latter method with characteristics of a lidar timing chart and local optimum model, which based on space-time proximity. Then a polarization-Mie scattering lidar system is used to observe the vertical distribution of aerosols over time at Taihu observation site, which is in a newly developed area of the city of Wuxi, Jiangsu Province, China. Observation and analysis is carried out for two cases in terms of pollution at the end of 2012. Then corresponding estimation model was built with gradient method and local optimum model based on range-corrected signals. In the case of steady weather and mixed pollution, results of the gradient method and local optimum model were very similar. However, the gradient method has more error in the case of pollution intrusion with the residual layer. The local optimum model based on the space-time proximity theory considers vertical eigenvalues and horizontal correlations, thereby greatly reducing the effects of low clouds, signal interference, weak signals, bi-layered aerosols, and residual layer condition. Compared with the gradient method, the local optimum model had a smaller O(n) and greater stability in computer automatic identification. ABL identification in the case with the residual layer and aerosol intrusion was solved with use of lidar technology and the local optimum model. The accuracy and computational efficiency problems of the gradient method were resolved using automatic operation. © 2017, Peking University Press. All right reserved.


Lin L.,China University of Mining and Technology | Lin L.,Key Laboratory of Land Environment and Disaster Monitoring | Lin L.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Wang Y.,China University of Mining and Technology | And 6 more authors.
Environmental Monitoring and Assessment | Year: 2016

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation—partial least squares regression (PLSR) method effectively solves the information loss problem of correlation—multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400–1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R2 = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions. © 2016, Springer International Publishing Switzerland.


Lin L.,China University of Mining and Technology | Lin L.,Key Laboratory of Land Environment and Disaster Monitoring | Lin L.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Wang Y.,China University of Mining and Technology | And 4 more authors.
Environmental monitoring and assessment | Year: 2016

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.


Lin L.-X.,China University of Mining and Technology | Lin L.-X.,Mapping and Geo Information NASG Key Laboratory of Land Environment and Disaster Monitoring | Lin L.-X.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Wang Y.-J.,China University of Mining and Technology | And 5 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2014

Soil available nitrogen content is an important index reflecting soil fertility. It provides dynamic information for land reclamation and ecological restoration if soil available nitrogen can be monitored and evaluated using hyperspectral technology. Facing the study blank of soil available nitrogen in National Mine Park and the deficiency of poor computational efficiency of partial least squares regression (PLSR) method, the present paper presents the relationship between soil spectrum and soil available nitrogen based on spectrum curves (ranging from 350 to 2 500 nm) of 30 salinized chestnut soil samples, which were collected from southern mountain coal waste scenic spot, located in Jinhuagong mine in Datong city, Shanxi Province, China (one part of Jinhuagong national mine park). Soil reflection spectrum was mathematically manipulated into first derivative and inverse-log spectral curves, then a corresponding estimation model was built and examined by PLSR and Enter-partial least squares regression (Enter-PLSR) based on characteristic absorption. The result indicated that Enter-PLSR corresponding estimation model greatly increased the computation efficiency by reducing the number of independent variables to 12 from 122 in case of a close accuracy of PLS corresponding estimation model. By using hyperspectral technology and Enter-PLSR method, the study blank of soil available nitrogen in National Mine Park was filled. At the same time, the computation efficiency problem of PLSR was resolved.

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