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无人机在矿区表土特征及地质灾害监测中的应用

龙林丽 刘英 张旭阳 苏永东 陈孝杨

龙林丽, 刘英, 张旭阳, 苏永东, 陈孝杨. 无人机在矿区表土特征及地质灾害监测中的应用[J]. 煤田地质与勘探, 2021, 49(6): 200-211. doi: 10.3969/j.issn.1001-1986.2021.06.024
引用本文: 龙林丽, 刘英, 张旭阳, 苏永东, 陈孝杨. 无人机在矿区表土特征及地质灾害监测中的应用[J]. 煤田地质与勘探, 2021, 49(6): 200-211. doi: 10.3969/j.issn.1001-1986.2021.06.024
LONG Linli, LIU Ying, ZHANG Xuyang, SU Yongdong, CHEN Xiaoyang. Application of unmanned aerial vehicle in surface soil characterization and geological disaster monitoring in mining areas[J]. COAL GEOLOGY & EXPLORATION, 2021, 49(6): 200-211. doi: 10.3969/j.issn.1001-1986.2021.06.024
Citation: LONG Linli, LIU Ying, ZHANG Xuyang, SU Yongdong, CHEN Xiaoyang. Application of unmanned aerial vehicle in surface soil characterization and geological disaster monitoring in mining areas[J]. COAL GEOLOGY & EXPLORATION, 2021, 49(6): 200-211. doi: 10.3969/j.issn.1001-1986.2021.06.024

无人机在矿区表土特征及地质灾害监测中的应用

doi: 10.3969/j.issn.1001-1986.2021.06.024
基金项目: 

国家自然科学基金项目 4157020161

安徽理工大学校级重点项目(自然科学类) xjzd2020-04

详细信息
    第一作者:

    龙林丽,1998年生,女,四川自贡人,硕士研究生,从事矿山生态环境修复研究. E-mail:long6_6@163.com

    通信作者:

    陈孝杨,1976年生,男,安徽肥西人,博士,教授,博士生导师,从事矿山环境治理与场地污染控制研究. E-mail:chenxy@aust.edu.cn

  • 中图分类号: TD167

Application of unmanned aerial vehicle in surface soil characterization and geological disaster monitoring in mining areas

  • 摘要: 随着无人机的出现和发展,各种传感器的小型化和智能化程度不断提高,装载传感器的无人机成为获得空间数据的高效工具。因其成本低、重访周期短、快速高效、质轻灵活、操作简便、影像获取时空精度高等特点,广泛应用于矿区土地损伤监测。以“无人机(UAV)、反演(Inversion)、土壤监测(Soil Monitoring)、地表塌陷(Surface Collapse)、地裂缝(Ground Fissure)”为关键词,通过总结Web of Science、知网、谷歌学术等检索系统中2010年1月—2021年5月发表的学术论文,对比分析无人机监测技术与其他监测技术的差别,综述无人机监测矿区重金属、土壤含水率、含盐量、地表塌陷、地裂缝及边坡稳定性的一般流程及数据处理方法,并概述无人机在矿区表土特征及地质灾害监测中的应用前景,认为未来可通过集成野外时序跟踪调查、高精度土壤质量监测技术、高空间分辨率无人机监测技术、数字模拟手段和典型工作面的试验监测与分析,研究工作面自开切眼至停采线动态推进中地质灾害与土壤质量演化耦合关系,构建采煤沉陷区土壤质量演化预测理论体系和时序演变模型。从而进一步探讨矿区土壤质量与地质灾害之间的关系,提出减缓、控制及提升矿区土壤质量的措施,为我国煤炭生产基地煤炭资源开采与生态环境的协调可持续发展提供技术支撑。

     

  • 表  1  单波段反演重金属文献总结

    Table  1  Summary of the literature on single-band inversion of heavy metals

    反演方法 元素 特征波段/nm 光谱预处理方法 建模方法 参考文献
    单波段反演 Zn 515 断点修正、平滑处理、包络线去除 MSR、PLSR [27]
    Cr 379 一阶微分变换、二阶微分变换及倒数对数变换 SMLR、PLSR、ANN [28]
    As 1 778
    Cu 2 018
    多波段组合
    反演
    Cd 356、366、415、1 376、1 438、
    1 767、2 203、2 204
    一阶导数变换 MSR、PLSR、BPNN [19]
    Pb 507、548、574、665、893、971、
    1 107、1 531、1 789、2 059、2 105、2 226、2 285、2 328、2 357
    一阶导,二阶导,对数变换,包络线去除 Stacking模型 [34]
    下载: 导出CSV

    表  2  用于土壤含盐量监测的盐分指数与植被指数

    Table  2  Salinity index and vegetation index used for soil salt content monitoring

    计算公式 研究区
    ${\text{SI}} = \sqrt {{\rho _{\text{B}}}{\rho _{\text{R}}}}$[49] 河套灌区沙壕渠灌域
    ${\text{NDSI = }}\left( {{\rho _{\text{R}}} - {\rho _{{\text{NIR}}}}} \right){\text{/}}\left( {{\rho _{\text{R}}}{\text{ + }}{\rho _{{\text{NIR}}}}} \right) $[50] 南埃塞俄比亚Sego灌溉农场
    ${\text{NDVI}} = \left( {{\rho _{{\text{NIR}}}} - {\rho _{\text{R}}}} \right){\text{/}}\left( {{\rho _{\text{R}}}{\text{ + }}{\rho _{{\text{NIR}}}}} \right)$[51] 新疆塔里木盆地南缘
    ${\text{EVI}} = \frac{{{\rho _{\text{G}}}\left( {{\rho _{{\text{NIR}}}} - {\rho _{\text{R}}}} \right)}}{{\left( {{\rho _{{\text{NIR}}}} + 6{\rho _{\text{R}}} - 7.5b + 1} \right)}} $[52] 美国明尼苏达州与达科他州的交界处
    $ {\rm{SAVI}} = \left( {1 + L} \right)\frac{{{\rho _{{\rm{NIR}}}} - {\rho _{\rm{R}}}}}{{{\rho _{{\rm{NIR}}}} + {\rho _{\rm{R}}} + L}}$[53] 沙特阿拉伯的Al-Hassa绿洲
    注:ρGρRρNIRρB分别为绿光、红光、近红外和蓝光波段光谱反射率;L为盖度背景调节因子,取0.5。
    下载: 导出CSV
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  • 收稿日期:  2021-05-28
  • 修回日期:  2021-08-05
  • 发布日期:  2021-12-25
  • 网络出版日期:  2021-12-30

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