|
|
|
|
|
|
Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal |
YANG En1, WANG Shi-bo2* |
1. School of Intelligent Manufacturing, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221116, China
2. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
|
|
|
Abstract Hyperspectral remote sensing is an effective method for coal mining area detection, and it is of great significance for coal resource surveys and environmental monitoring in the mining area. At the same time, reflectance spectrum characteristics of remotely measured objects such as coal, gangue, vegetation and water body in all directions are the basis of hyperspectral remote sensing in the coal mine. In this paper, the directional reflectance spectra of typical types of coal were studied. Four typical types of coal in the three major coal types anthracite, bituminous and lignite were collected from different mining areas in China. According to the increasing rank, these coals included No.1 anthracite, meager coal, gas coal and No.2 lignite. Spectral reflectance curves of each type of coal in all directions in hemispheric space were measured in the near-infrared band (1 000~2 500 nm) using the spherical coordinate device for directional reflection measurement in the laboratory. By waveforms of spectral reflectance curves acquired, it was found that near-infrared reflectance spectrum curves of the same coal in different reflection directions show similar waveforms. However, there are some differences in overall reflectance and local waveforms, and the rule is that the absorption valleys become more obvious with increasing overall reflectance. With increasing reflection angle, reflectance spectrum curves of all these four types of coal rise on the whole in the forward direction (180° azimuth), but the change is relatively small in the backward direction (0° azimuth). In each directional reflectance spectrum curve in the hemispheric space of each coal, five characteristic wavelength points, including 1 400, 1 700, 1 900, 2 200 and 2 300 nm were selected. By polar nephograms of the spatial distribution of reflectance at the five wavelength points, it was found that all these four types of coal show bidirectional reflection and prominent hot spots in the forward direction and relatively weaker hot spots in the backward direction. The hot spots in the backward direction of No.1 anthracite appear relatively more obvious than those of meager coal, gas coal and No.2 lignite. With decreasing coal rank, meager coal, gas coal, and No.2 lignite show the rule of relatively enhanced hot spots in the backward direction. The correlation between reflectance and reflection angle of backward and forward direction at the five wavelength points of each type of coal were analyzed. It was found that the correlations between reflectance and reflection angle are approximately linear and Gaussian functions in forwarding and backward direction respectively. Moreover, with decreasing coal rank, the peak of the Gaussian fitting curve moves to a larger reflection angle. This study provides the basis for the selection of the optimal detection geometry and reference for precise detection of coal resources in hyperspectral remote sensing of mining areas.
|
Received: 2021-01-19
Accepted: 2021-03-01
|
|
Corresponding Authors:
WANG Shi-bo
E-mail: wangshb@cumt.edu.cn
|
|
[1] Mao Yachun, Ma Baodong, Liu Shanjun, et al. Canadian Journal of Remote Sensing, 2014, 40(5): 327.
[2] Ba Tuan Le, Xiao Dong, Desmond Okello, et al. Spectroscopy Letters, 2017, 50(8): 440.
[3] Tan Kelong, Qiao Junwei. International Journal of Coal Science and Technology, 2020, 7(2): 311.
[4] Milton E J, Schaepman M E, Anderson K, et al. Remote Sensing of Environment, 2009, 113: S92.
[5] Cloutis E A, Pietrasz V B, Kiddell C, et al. Icarus, 2018, 305: 203.
[6] ZHAO Zi-jie, ZHAO Yun-sheng(赵子傑, 赵云升). Acta Physica Sinica(物理学报), 2014, 63(18): 435.
[7] LU Peng, CHEN Sheng-bo, CUI Teng-fei, et al(路 鹏, 陈圣波, 崔腾飞, 等). Acta Petrologica Sinica(岩石学报), 2016, 32(1): 107.
[8] Schopfer J, Dangel S, Kneubühler M, et al. Sensors, 2008, 8: 5120.
[9] Cloutis E A. Fuel, 2003, 82(18): 2239.
[10] YANG En, WANG Shi-bo, GE Shi-rong(杨 恩, 王世博, 葛世荣). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(6): 1717.
[11] Song Zeyang, Kuenzer C. International Journal of Coal Geology, 2017, 171: 142.
[12] Yang En, Ge Shirong, Wang Shibo. Journal of Spectroscopy, 2018, 2018: 1.
[13] Feng Li, Yuan Chuanzhou, Mao Lianzhen, et al. Fuel, 2018, 219: 288.
[14] LU Yan, YANG Kai, XIU Lian-cun(卢 燕, 杨 凯, 修连存). Geological Bulletin of China(地质通报), 2017, 36(10): 1884.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[5] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[6] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[7] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[8] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[9] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[10] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[11] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[12] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[13] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[14] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[15] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
|
|
|
|