|
|
|
|
|
|
| Remote Sensing Inversion of Soil Carbon Emissions in Desertification Mining Areas |
| LIU Ying1, 2, LIU Yu1, YUE Hui1, 2*, BI Yin-li2, 3, PENG Su-ping4, JIA Yu-hao1 |
1. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
2. Institute of Ecological Environment Restoration in Mine Areas of West China, Xi'an University of Science and Technology, Xi'an 710054, China
3. College of Geology and Environment, Xi'an University of Science and Technology, Xi'an 710054, China
4. College of Geoscience and Surveying Engineering, China University of Mining Technology (Beijing), Beijing 100083, China
|
|
|
|
|
Abstract With the policy of “carbon peaking and carbon neutrality” put forward in China, carbon emissions in the mining area have become the focus of attention. The study was based on soil samples taken from the mine areas, combined with 6 mathematical transformation methods (R, R, Log(1/R), 1st, MSC, SNV) and spectral feature screening methods (CC-SPA). This study explored the hyperspectral response characteristics of soil carbon emissions under different land use types in Hongshaquan Open-pit Coal Mine in Xinjiang; combined with soil temperature (ST), Soil moisture (SM) and 6 kinds of spectral indexes (NDVI, RVI, NGLI, SMM, SI-T, ATI), using partial least squares (PLSR), support vector machine (SVM), random forest (RF), genetic optimization neural network (GA-BP) algorithm to obtain the optimal remote sensing of soil carbon emissions inversion model. The main conclusions are as follows: (1) The reflectance of soil in the non-mining affected area is higher than that in the mining affected area under natural conditions, and the southern line is the most affected by coal mining and has the lowest reflectance, which proves that mining activities have an impact on the mining area soil; (2) Spectral characteristics In terms of screening, the number of carbon emission characteristic bands extracted based on the correlation coefficient-continuous projection algorithm (CC-SPA) is much smaller than that of the correlation coefficient method (CC) and the continuous projection algorithm (SPA), and the characteristic bands present a certain clustered distribution. In the wavelength range of 1 600～2 200 nm, the number of characteristic bands during the day is much higher than at night. Compared with the daytime, the characteristic bands at night have the characteristics of obviously shifting to long waves. (3) Adding the spectral index based on reflectivity and the inversion model of ST and SM can significantly improve the accuracy of estimating soil carbon emission rate. The support vector machine (SVM) model based on the first-order differential transformation (1st) can invert the mining area. Comprehensive land use types have the best effect on soil carbon emissions (validation set R2=0.813, RMSE=0.116); the optimal combination of soil carbon emission indices for five different land use types is different, and the introduction of different spectral indices has a significant effect on soil carbon emission rates. The estimation accuracy has been improved to varying degrees (the verification set R2 is above 0.8), and the optimal soil carbon emission inversion model can more accurately estimate the carbon emission rate of different land use types in the Hongshaquan mining area. This study can provide a basis for the remote sensing inversion of soil carbon emissions in desertified mining areas, quantitatively identify the carbon source-sink effect of soil under different land use types and realize the non-destructive detection of carbon emissions in mining areas, providing support for my country's “30·60” double carbon goal. Data support.
|
|
Received: 2023-05-30
Accepted: 2023-12-05
|
|
|
|
Corresponding Authors:
YUE Hui
E-mail: 13720559861@163.com
|
|
[1] WANG Bing, JIANG Yan, GUO Hao, et al(王 兵, 姜 艳, 郭 浩, 等). Chinese Journal of Soil Science(土壤通报), 2011, 42(2): 483.
[2] Raich J W, Potter C S, Bhagawati D. Global Change Biology, 2002, 8(8): 800.
[3] REN Zhi-sheng, QI Rui-peng, WANG Tong-tong, et al(任志胜, 齐瑞鹏, 王彤彤, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(23): 230.
[4] CHEN Xiao-yang, WANG Fang, YAN Jia-ping, et al(陈孝杨, 王 芳, 严家平, 等). Journal of China University of Mining & Technology(中国矿业大学学报), 2016, 45(1): 163.
[5] Pandey B, Mukherjee A, Agrawal M, et al. Pedosphere, 2019, 29(5): 642.
[6] Mukhopadhyay S, Maiti S K. Land Degradation & Development, 2014, 25(3): 216.
[7] Ahirwal J, Maiti S K, Singh A K. Science of the Total Environment, 2017, 583: 153.
[8] Ahirwal J, Maiti S K, Environmental Earth Sciences, 2018, 77: 1.
[9] Zhou Z, Zhang Z, Zha T, et al. Soil Biology and Biochemistry, 2013, 57: 135.
[10] CUI Hui-jin, LIU Ju, DU Zi-qiang, et al(崔慧瑾, 刘 菊, 杜自强, 等). Research of Soil and Water Conservation(水土保持研究), 2020, 27(3): 15.
[11] Liu Ying, Lin Jiaquan, Yue Hui, et al. Earth Science Informatics, 2023, 16(4): 3433.
[12] BI Yin-li, GUO Chen, XIAO Li, et al(毕银丽, 郭 晨, 肖 礼，等). Journal of China Coal Society(煤炭学报), 2020, 45(12): 4170.
[13] LIU Ying, WEI Jia-li, BI Yin-li, et al(刘 英, 魏嘉莉, 毕银丽, 等). Journal of China Coal Society(煤炭学报), 2022, 47(S1): 214.
[14] YUE Hui, ZHU Rong, LIU Ying, et al(岳 辉, 朱 蓉, 刘 英, 等). Coal Science and Technology(煤炭科学技术), 2022, 50(2): 300.
[15] WU Ke-ning, ZHAO Rui(吴克宁, 赵 瑞). Acta Pedological Sinica(土壤学报), 2019, 56(1): 227.
[16] LIU Ying, LIN Jia-quan, YUE Hui, et al(刘 英, 林家权, 岳 辉, 等). Journal of China Coal Society(煤炭学报), 2023, 48(7): 2893.
[17] SHEN Yan, ZHANG Xiao-ping, LIANG Ai-zhen, et al(申 艳, 张晓平, 梁爱珍, 等). System Sciences and Comprehensive Studies in Agriculture(农业系统科学与综合研究), 2010, 26(2): 174.
[18] DAI Zhao-hua（戴昭华）. Chinese Journal of Environmental Engineering(环境工程学报), 1987，8(1): 3.
[19] NIU Fang-peng, LI Xin-guo, MAMATTURSUN·Eziz, et al(牛芳鹏, 李新国, 麦麦提吐尔逊·艾则孜，等). Journal of Zhejiang University(Agriculture and Life Sciences)[浙江大学学报(农业与生命科学版)], 2021, 47(5): 673.
[20] Jackson R D, Slater P N, Pinter J R. Remote Sensing of Environment, 1983, 13(3): 187.
[21] Rouse Jr J W, Haas R H, Schell J A, et al. NASA Special Publication, 1974, (351): 309.
[22] WANG Shu-guo, LI Xin, HAN Xu-jun, et al(王树果, 李 新, 韩旭军, 等). Remote Sensing Technology and Application(遥感技术与应用), 2009, 24(5): 582.
[23] Allbed A, Kumar L, Aldakheel Y Y. Geoderma, 2014, 230: 1.
[24] Price J C. Journal of Geophysical Research, 1977, 82(18): 2582.
[25] Vasques G M, Grunwald S, Sickman J O. Geoderma, 2008, 146(1-2): 14.
[26] SUN Ya-nan, LI Xian-yue, SHI Hai-bin, et al(孙亚楠, 李仙岳, 史海滨, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(5): 344.
[27] GUO Peng-tao, LI Mao-fen, LUO Wei, et al(郭澎涛, 李茂芬, 罗 微, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(5): 194.
[28] DU Yue, MENG Xiao-chen, ZHU Lian-qing(都 月, 孟晓辰, 祝连庆). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(7): 2066.
[29] Zornoza R, Guerrero C, Mataix-Solera J, et al. Soil Biology and Biochemistry, 2008, 40(7): 1923.
[30] Rossel R A V, Behrens T. Geoderma, 2010, 158(1-2): 46.
[31] Xie X, Liu W T, Tang B. Remote Sensing of Environment, 2008, 112(4): 1846.
[32] CHENG Zhuo-zhi, WANG Wen-jun, YAN Zhuo-xuan, et al(成倬治, 王文君, 闫卓璇，等). Technology Innovation and Application(科技创新与应用), 2020, (17): 36.
[33] Wu C, Gaumont-Guay D, Black T A, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 94: 80.
|
| [1] |
YUE Ji-bo1, LENG Meng-die1, TIAN Qing-jiu2, GUO Wei1, LIU Yang3, FENG Hai-kuan4, QIAO Hong-bo1*. Estimation of Leaf Physical and Chemical Parameters Based on Hyperspectral Remote Sensing and Deep Learning Technologies[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2873-2883. |
| [2] |
JIANG Yu-heng1, YAN Bo1, ZHUANG Qing-yuan1, WANG Ai-ping1, CAO Shuang1, TIAN An-hong1, 2, FU Cheng-biao1*. Quantitative Inversion Model of Soil Heavy Metals Zn and Ni Based on Fractional Order Derivative[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2850-2857. |
| [3] |
DENG Yun1, 2, WU Wei1, 2, SHI Yuan-yuan3, CHEN Shou-xue1, 2*. Red Soil Organic Matter Content Prediction Model Based on Dilated
Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2941-2952. |
| [4] |
LIANG Xin-yue1, 2, ZENG Jing2, AI Chang-peng3, LUO Jie-cheng3, FAN Xiao-jing4, WU Sheng-nan3*, GU Ying1, 2, 3*. Differences in the Effects of Night Blue Light and White Light Exposure on Liver Circadian Rhythm and Inflammatory Response[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2865-2872. |
| [5] |
FAN Jie-jie1, 2, QIU Chun-xia1, FAN Yi-guang2, CHEN Ri-qiang2, LIU Yang2, BIAN Ming-bo2, MA Yan-peng2, YANG Fu-qin4, FENG Hai-kuan2, 3*. Wheat Yield Prediction Based on Continuous Wavelet Transform and
Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2890-2899. |
| [6] |
GUAN Cheng1, LIU Ming-yue1, 2, 3, 4*, MAN Wei-dong1, 2, 3, 4, ZHANG Yong-bin1, ZHANG Qing-wen1, FANG Hua1, LI Xiang1, GAO Hui-feng1. Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Based on Continous Wavelet Transformation and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2993-3000. |
| [7] |
LIU Yu-juan1, 2, 3, LIU Yan-da1, 2, 3, YAN Zhen1, 4, ZHANG Zhi-yong1, 2, 3, CAO Yi-ming1, 2, 3, SONG Ying1, 2, 3*. Classification of Hybrid Convolution Hyperspectral Images Based on
Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2916-2922. |
| [8] |
LI Xuan1, GAN Shu1, 2*, YUAN Xi-ping2, 3, 4, YANG Min3, 4, GONG Wei-zhen1. Spectral Characteristic and Identification Modelling of Three Typical Wetland Vegetation Along the Seashore of the East Coast of the Erhai Lake[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2439-2444. |
| [9] |
WANG Yan-cang1, 4, 5, 6, ZHU Yu-chen3*, QI Yan-xin1, ZHANG Zhi-tong1, CAO Hui-qiong1, WANG Jin-gao2, GU Xiao-he4, TANG Rui-yin1, HE Yue-jun1, LI Xiao-fang2, LUO Wei1. Hyperspectral Estimation of Leaf Moisture Content in Winter Wheat After Discrete Wavelet Denoising[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2559-2567. |
| [10] |
WEI Yun-peng1, HU Hui-qiang1, MAO Xiao-bo1*, ZHAO Yu-ping2*, ZHANG Lei3, SHENG Wen-tao4. Identification for Different Growth Years of Plastrum Testudinis via Hyperspectral Imaging Technique and Heterogeneous Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2613-2619. |
| [11] |
SUN Jia-qi, YIN Yong*, YU Hui-chun, YUAN Yun-xia, GUO Lin-ge. Hyperspectral Prediction Method for Perilla Aldehyde Content in Perilla Frutescens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2667-2674. |
| [12] |
SUN Yu-tong1, 2, LÜ Shu-qiang1, 2, HOU Miao-le1, 2*. A Mixed Pigment Identification Method Based on Spectra Interval[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2357-2364. |
| [13] |
KONG Li-qin1, 2, NIU Xiao-hu1, 2, WANG Cheng-lei1, 2, FENG Yao-ze1, 2, 3*, ZHU Ming1, 2. Application of Hyperspectral Imaging Technology in the Identification of Composite Adulteration Type in Beef Balls[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2183-2191. |
| [14] |
WANG Hao-yu1, 2, 3, WEI Zi-yuan1, 2, 3, YANG Yong-xia1, 2, 3, HOU Jun-ying1, 2, 3, SUN Zhang-tong1, 2, 3, HU Jin1, 2, 3*. Estimation of Eggplant Leaf Nitrogen Content Based on Hyperspectral Imaging and Convolutional Auto-Encoders Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2208-2215. |
| [15] |
LI Hao1, YU Hao1, CAO Yong-yan1, HAO Zi-yuan1, 2, YANG Wei1, 2*, LI Min-zan1, 2. Hyperspectral Prediction of Soil Organic Matter Content Using
CARS-CNN Modelling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2303-2309. |
|
|
|
|
