摘要
煤炭大规模高强度开采致使地表产生裂隙,裂隙破坏土壤结构,影响土壤质量与生长力。为对采煤拉张裂隙区土壤有机质(SOM)含量快速估测,以淮北市朱庄煤矿3522工作面采煤拉张裂隙区为研究区,采集了裂隙区土壤样本,并测定了土壤样本光谱与有机质含量。对原始光谱进行倒数之对数(LR)、一阶导数(FD)和连续统去除(CR)等3种光谱变换后,通过计算光谱中任意两个波段组合的差值指数、比值指数和归一化指数,应用皮尔逊相关系数(PCC)结合最大相关最小冗余算法(mRMR)分别提取一维光谱波段和二维光谱指数,最后基于偏最小二乘(PLSR)和极端梯度提升(XGBoost)等两种算法,构建了裂隙区SOM估测模型,并对模型精度进行检验和评价。实验结果表明:(1)高强度采煤导致地表裂隙产生,加速了土壤中SOM、细小土壤颗粒流失,研究区SOM变异系数达61.32%;(2)无论是一维光谱波段还是光谱指数,FD预处理光谱的模型精度最优;(3)对比一维光谱波段,差值指数(DI)、比值指数(RI)和归一化指数(NDI)与SOM相关性更优,FD-DI指数相关性最高,其最大相关系数为0.88;(4)使用PCC结合mRMR算法筛选光谱波段及光谱指数,减少输入变量数量的同时保持了模型性能,基于XGBoost的模型精度优于PLSR模型,其中FD-NDI-XGBoost模型精度最佳,其R^(2)、RMSE和RPD分别为0.83、0.49 mg·kg^(-1)和2.44。实验研究结果可为采煤拉张裂隙区土壤有机质的高光谱估测提供一定的技术参考。
The large-scale and high-intensity mining of coal leads to the formation of cracks in the ground,which destroy the structure of the soil and affect the soil quality.To quickly estimate the content of soil organic matter(SOM)in the coal stretching fissure area,soil samples were collected from the fissure area at Zhuzhuang coal mine,Huaibei City,China.The spectrum and SOM content of the soil samples were then determined.Inverse log reflectance(LR),first order differential reflectance(FD),and continuum removal(CR)were performed on the original spectrum.Then,the difference index,ratio index,and normalized difference index of any two band combinations were calculated.The Pearson correlation coefficient(PCC)was combined with the maximum relevance minimum redundancy(mRMR)algorithm to extract one-dimensional spectral bands and two-dimensional spectral indices,respectively.In the end,the two algorithms,PLSR and eXtreme Gradient Boosting(XGBoost),were used to construct a SOM content estimation model for the mining fissure area.The accuracy of the model was then tested and evaluated.The results show that:(1)The high intensity mining of coal leads to the formation of cracks in the ground,which destroys the structure of the soil and affects the soil quality.It also accelerates the loss of SOM and fine soil particles in the soil,and the coefficient of variation of SOM in the study area reaches 61.32%;(2)Regardless of one-dimensional spectral band or spectral index,the model based on FD spectrum has the highest accuracy;(2)The correlation between the two dimensional spectral index and SOM content is significantly better than that of the one-dimensional spectral band,and the prediction model based on the spectral index has higher prediction accuracy;(3)Compared with the one-dimensional spectral band,the difference index(DI),ratio index(RI),and normalized difference index(NDI)have a stronger correlation with SOM,and the FD-DI index has the highest correlation,with a correlation coefficient of 0.88;(4)The accuracy of XGBoost based model is better than PLSR model,among which FD-NDI-XGBoost model has the highest accuracy,and its R^(2),RMSE and RPD are 0.83,0.49 mg·kg^(-1)and 2.44,respectively.The experimental results can provide a technical reference for the hyperspectral estimation of SOM content in the coal mining tensile fracture area.
作者
郭辉
韩紫薇
吴斗庆
GUO Hui;HAN Zi-wei;WU Dou-qing(School of Geomatics,Anhui University of Science and Technology,Huainan 232001,China;Coal Industry Engineering Research Center of Mining Area Environment and Disaster Cooperative Monitoring,Anhui Univer sity of Science and Technology,Huainan 232001,China)
出处
《光谱学与光谱分析》
北大核心
2025年第9期2569-2577,共9页
Spectroscopy and Spectral Analysis
基金
矿山环境与灾害协同监测煤炭行业工程研究中心安徽理工大学开放基金项目(KSXTJC202202)
国家自然科学基金项目(41971401)资助。
关键词
土壤有机质
采煤拉张裂隙
光谱指数
高光谱遥感
土壤质量
Soil organic matter
Coal mining tensile cracks
Spectral index
Hyperspectral inversion
Soil quality
作者简介
郭辉,1979年生,安徽理工大学空间信息与测绘工程学院副教授,e-mail:147186529@qq.com;通讯作者:韩紫薇,e-mail:2022201759@aust.edu.cn。
