留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] 胡振琪, 肖武. 关于煤炭工业绿色发展战略的若干思考: 基于生态修复视角[J]. 煤炭科学技术, 2020, 48(4): 35–42.

    HU Zhenqi, XIAO Wu. Some thoughts on green development strategy of coal industry: From aspects of ecological restoration[J]. Coal Science and Technology, 2020, 48(4): 35–42.
    [2] 文博杰, 陈毓川, 王高尚, 等. 2035年中国能源与矿产资源需求展望[J]. 中国工程科学, 2019, 21(1): 68–73.

    WEN Bojie, CHEN Yuchuan, WANG Gaoshang, et al. China's demand for energy and mineral resources by 2035[J]. Engineering Sciences, 2019, 21(1): 68–73.
    [3] 王丹爽. 无人机技术在施工现场安全评价中的应用研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

    WANG Danshuang. Application of UAV technology in construction site safety evaluation[D]. Harbin: Harbin Institute of Technology, 2017.
    [4] 张凯, 李全生, 戴华阳, 等. 矿区地表移动"空天地"一体化监测技术研究[J]. 煤炭科学技术, 2020, 48(2): 207–213.

    ZHANG Kai, LI Quansheng, DAI Huayang, et al. Research on integrated monitoring technology and practice of "space-sky-ground" on surface movement in mining area[J]. Coal Science and Technology, 2020, 48(2): 207–213.
    [5] 姚一飞. 基于北斗增强的实时PPP及在矿山地表监测的研究[D]. 徐州: 中国矿业大学, 2017.

    YAO Yifei. Research on real-time precise point positioning based on Beidou augmentation and its application in mining surface monitoring[D]. Xuzhou: China University of Mining and Technology, 2017.
    [6] 仝云霄. InSAR矿区地表形变监测及大气延迟校正研究[D]. 徐州: 中国矿业大学, 2020.

    TONG Yunxiao. Study on surface deformation monitoring in mining area and atmospheric delay correction of InSAR[D]. Xuzhou: China University of Mining and Technology, 2020.
    [7] 廉旭刚, 蔡音飞, 胡海峰. 我国矿山测量领域三维激光扫描技术的应用现状及存在问题[J]. 金属矿山, 2019(3): 35–40. https://www.cnki.com.cn/Article/CJFDTOTAL-JSKS201903006.htm

    LIAN Xugang, CAI Yinfei, HU Haifeng. Application status and existing problems of 3D laser scanning technique in mine surveying in China[J]. Metal Mine, 2019(3): 35–40. https://www.cnki.com.cn/Article/CJFDTOTAL-JSKS201903006.htm
    [8] 徐坤, 屈莹, 王宝山. 倾斜摄影测量技术在矿产资源监测中的应用[J]. 测绘工程, 2020, 29(4): 38–43.

    XU Kun, QU Ying, WANG Baoshan. Application of oblique photogrammetry technology to the dynamic monitoring of mineral resources[J]. Engineering of Surveying and Mapping, 2020, 29(4): 38–43.
    [9] 王昆, 杨鹏, 吕文生, 等. 无人机遥感在矿业领域应用现状及发展态势[J]. 工程科学学报, 2020, 42(9): 1085–1095.

    WANG Kun, YANG Peng, LYU Wensheng, et al. Current status and development trend of UAV remote sensing applications in the mining industry[J]. Chinese Journal of Engineering, 2020, 42(9): 1085–1095.
    [10] 白洋, 康会涛, 张文超, 等. 无人机在露天矿山监测中的应用[J]. 测绘通报, 2020(9): 85–88.

    BAI Yang, KANG Huitao, ZHANG Wenchao, et al. Application of UAV in open-pit mine monitoring[J]. Bulletin of Surveying and Mapping, 2020(9): 85–88.
    [11] 许吉仁, 董霁红, 杨源譞, 等. 基于支持向量机的矿区复垦农田土壤–小麦镉含量高光谱估算[J]. 光子学报, 2014, 43(5): 1–8. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201405019.htm

    XU Jiren, DONG Jihong, YANG Yuanxuan, et al. Support vector machine model for predicting the cadmium concentration of soil-wheat system in mine reclamation farmland using hyperspectral data[J]. Acta Photonica Sinica, 2014, 43(5): 1–8. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201405019.htm
    [12] LIU Meiling, LIU Xiangnan, WU Ling, et al. Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance[J]. Computers and Geosciences, 2011, 37(9): 1254–1263.. doi: 10.1016/j.cageo.2010.11.019
    [13] ROMAN M B, SERGEY V S. Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data[J]. Analytica Chimica Acta, 2011, 692(1): 63–72. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0003267011003539&originContentFamily=serial&_origin=article&_ts=1427250259&md5=d6e58e72ca0c4b2c2f3418f870023578
    [14] 叶元元. 多金属矿区土壤重金属的高光谱定量估算研究[D]. 徐州: 中国矿业大学, 2014.

    YE Yuanyuan. Quantitative estimating of soil heavy metals with hyper-spectrum in polymetallic mining areas[D]. Xuzhou: China University of Mining and Technology, 2014.
    [15] 王惠敏. 基于光谱吸收特征的土壤有机质与重金属含量估算研究[D]. 徐州: 中国矿业大学, 2019.

    WANG Huimin. Study on the estimation of soil organic matter and heavy metal contents based on spectral absorption characteristics[D]. Xuzhou: China University of Mining and Technology, 2019.
    [16] 谭琨, 叶元元, 杜培军, 等. 矿区复垦农田土壤重金属含量的高光谱反演分析(英文)[J]. 光谱学与光谱分析, 2014, 34(12): 3317–3322.. doi: 10.3964/j.issn.1000-0593(2014)12-3317-06

    TAN Kun, YE Yuanyuan, DU Peijun, et al. Estimation of heavy metal concentrations in reclaimed mining soils using reflectance spectroscopy(English)[J]. Spectroscopy and Spectral Analysis, 2014, 34(12): 3317–3322.. doi: 10.3964/j.issn.1000-0593(2014)12-3317-06
    [17] ZHANG Shiwen, SHEN Qiang, NIE Chaojia, et al. Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 211: 393–400.. doi: 10.1016/j.saa.2018.12.032
    [18] 邬登巍, 吴昀昭, 马宏瑞. 基于中红外漫反射光谱的土壤重金属元素含量预测研究[J]. 光谱学与光谱分析, 2010, 30(6): 1498–1502.. doi: 10.3964/j.issn1000-0593(2010)06-1498-05

    WU Dengwei, WU Yunzhao, MA Hongrui. Study on the prediction of soil heavy metal elements content based on mid-infrared diffuse reflectance spectra[J]. Spectroscopy and Spectral Analysis, 2010, 30(6): 1498–1502.. doi: 10.3964/j.issn1000-0593(2010)06-1498-05
    [19] 左玲. 黑土区土壤重金属高光谱遥感监测方法探究[D]. 北京: 中国地质大学(北京), 2020.

    ZUO Ling. Research on hyperspectral remote sensing monitoring method of heavy metals in black soil region[D]. Beijing: China University of Geosciences(Beijing), 2020.
    [20] ARAÚJO S R, DEMATTÊ J A M, VICENTE S. Soil contaminated with chromium by tannery sludge and identified by vis-NIR-mid spectroscopy techniques[J]. International Journal of Remote Sensing, 2014, 35(10): 3579–3593.. doi: 10.1080/01431161.2014.907940
    [21] URSZULA S, LAKHMI C J. Feature selection for data and pattern recognition[M]. Heidelberg: Springer, 2015.
    [22] JAIN A, ZONGKER D. Feature selection: Evaluation, application, and small sample performance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(2): 153–158.. doi: 10.1109/34.574797
    [23] YU Lei, LIU Huan. Efficient feature selection via analysis of relevance and redundancy[J]. The Journal of Machine Learning Research, 2004: 1205–1224. http://www.researchgate.net/profile/Huan_Liu6/publication/220321053_Efficient_Feature_Selection_via_Analysis_of_Relevance_and_Redundancy/links/0046351a386da91751000000
    [24] 刘英. 半干旱煤矿区受损植被引导型恢复研究[D]. 徐州: 中国矿业大学, 2020.

    LIU Ying. Study on guided restoration of damaged vegetation in semi-arid coal mine area[D]. Xuzhou: China University of Mining and Technology, 2020.
    [25] 胡忠正. 机载高光谱影像反演土壤重金属含量的经验模型选择与特征提取[D]. 北京: 中国地质大学(北京), 2019.

    HU Zhongzheng. Empirical model selection and feature extraction for retrieving soil heavy metal concentration from airborne hyperspectral imagery[D]. Beijing: China University of Geosciences(Beijing), 2019.
    [26] BRUCE L M, KOGER C H, LI Jiang. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(10): 2331–2338.. doi: 10.1109/TGRS.2002.804721
    [27] 宋婷婷. 土壤重金属含量反演与矿区环境遥感监测与评价研究[D]. 北京: 北京化工大学, 2017.

    SONG Tingting. Study on inversion of heavy metal content in soil and remote sensing monitoring and evaluation of mine environment[D]. Beijing: Beijing University of Chemical Technology, 2017.
    [28] 徐良骥, 李青青, 朱小美, 等. 煤矸石充填复垦重构土壤重金属含量高光谱反演[J]. 光谱学与光谱分析, 2017, 37(12): 3839–3844. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201712037.htm

    XU Liangji, LI Qingqing, ZHU Xiaomei, et al. Hyperspectral inversion of heavy metal content in coal gangue filling reclamation land[J]. Spectroscopy and Spectral Analysis, 2017, 37(12): 3839–3844. https://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201712037.htm
    [29] 宋练, 简季, 谭德军, 等. 万盛采矿区土壤As, Cd, Zn重金属含量光谱测量与分析[J]. 光谱学与光谱分析, 2014, 34(3): 812–817.. doi: 10.3964/j.issn.1000-0593(2014)03-0812-06

    SONG Lian, JIAN Ji, TAN Dejun, et al. Estimation of soil's heavy metal concentrations(As, Cd and Zn)in Wansheng mining area with geochemistry and field spectroscopy[J]. Spectroscopy and Spectral Analysis, 2014, 34(3): 812–817.. doi: 10.3964/j.issn.1000-0593(2014)03-0812-06
    [30] 徐明星, 吴绍华, 周生路, 等. 重金属含量的高光谱建模反演: 考古土壤中的应用[J]. 红外与毫米波学报, 2011, 30(2): 109–114. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201102004.htm

    XU Mingxing, WU Shaohua, ZHOU Shenglu, et al. Hyperspectral reflectance models for retrieving heavy metal content: Application in the archaeological soil[J]. Journal of Infrared and Millimeter Waves, 2011, 30(2): 109–114. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201102004.htm
    [31] FILZMOSER P, TODOROV V. Review of robust multivariate statistical methods in high dimension[J]. Analytica Chimica Acta, 2011, 705(1/2): 2–14. http://www.statistik.tuwien.ac.at/forschung/SM/SM-2011-1DB.pdf
    [32] ZHANG Xia, HUANG Changping, LIU Bo, et al. Inversion of soil Cu concentration based on band selection of hyperspetral data[C]//2010 IEEE International Geoscience and Remote Sensing Symposium. New York: IEEE, 2010: 3680–3683.
    [33] THISSEN U, PEPERS M, ÜSTÜN B, et al. Comparing support vector machines to PLS for spectral regression applications[J]. Chemometrics and Intelligent Laboratory Systems, 2004, 73(2): 169–179.. doi: 10.1016/j.chemolab.2004.01.002
    [34] 马伟波, 谭琨, 李海东, 等. 基于超限学习机的矿区土壤重金属高光谱反演[J]. 生态与农村环境学报, 2016, 32(2): 213–218. https://www.cnki.com.cn/Article/CJFDTOTAL-NCST201602008.htm

    MA Weibo, TAN Kun, LI Haidong, et al. Hyperspectral inversion of heavy metals in soil of a mining area using extreme learning machine[J]. Journal of Ecology and Rural Environment, 2016, 32(2): 213–218. https://www.cnki.com.cn/Article/CJFDTOTAL-NCST201602008.htm
    [35] WANG Fenghe, GAO Jay, ZHA Yong. Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 136: 73–84.. doi: 10.1016/j.isprsjprs.2017.12.003
    [36] MORTEZA S, SCOTT B J, WILLIAM D P. A linear physically-based model for remote sensing of soil moisture using short wave infrared bands[J]. Remote Sensing of Environment, 2015, 164: 66–76.. doi: 10.1016/j.rse.2015.04.007
    [37] 郑小坡, 孙越君, 秦其明, 等. 基于可见光–短波红外波谱反射率的裸土土壤含水量反演建模[J]. 光谱学与光谱分析, 2015, 35(8): 2113–2118.. doi: 10.3964/j.issn.1000-0593(2015)08-2113-06

    ZHENG Xiaopo, SUN Yuejun, QIN Qiming, et al. Bare soil moisture inversion model based on visible-shortwave infrared reflectance[J]. Spectroscopy and Spectral Analysis, 2015, 35(8): 2113–2118.. doi: 10.3964/j.issn.1000-0593(2015)08-2113-06
    [38] 郭辉, 卜小东, 黄可京, 等. 基于热红外遥感影像的玉米田间土壤水分反演研究[J]. 中国农机化学报, 2020, 41(10): 203–210.

    GUO Hui, BU Xiaodong, HUANG Kejing, et al. Inversion of soil moisture in corn field based on thermal infrared remote sensing image[J]. Journal of Chinese Agricultural Mechanization, 2020, 41(10): 203–210.
    [39] 李伯祥, 陈晓勇. 基于Sentinel多源遥感数据的河北省景县农田土壤水分协同反演[J]. 生态与农村环境学报, 2020, 36(6): 752–761.

    LI Boxiang, CHEN Xiaoyong. Synergic use of Sentinel-1 and Sentinel-2 images for soil moisture retrieval in vegetation covered agricultural areas of Jingxian County of Hebei Province[J]. Journal of Ecology and Rural Environment, 2020, 36(6): 752–761.
    [40] 夏军. 准东煤田土壤重金属污染高光谱遥感监测研究[D]. 乌鲁木齐: 新疆大学, 2014.

    XIA Jun. Study on the monitoring of soil heavy metal pollution with hyperspectral remote sensing in the Eastern Junggar Coalfield[D]. Urumqi: Xinjiang University, 2014.
    [41] HOFFMANN H, JENSEN R, THOMSEN A, et al. Crop water stress maps for entire growing seasons from visible and thermal UAV imagery[J]. Biogeosciences Discussions, 2016, 13(24): 316. http://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsRW5nTmV3UzIwMjEwMzAyEiAxODkwNmU1ZjA0YTE5NTMwOTlhMTkyYTJmZThiYWNmNRoIOHF5Nm11d3I%3D
    [42] 夏权, 夏萍, 陈黎卿, 等. 基于多光谱遥感的土壤含水量定量监测与分析[J]. 安徽农业大学学报, 2015, 42(3): 439–443. https://www.cnki.com.cn/Article/CJFDTOTAL-ANHU201503020.htm

    XIA Quan, XIA Ping, CHEN Liqing, et al. Quantitative analysis of the soil moisture content based on multispectral remote sensing data[J]. Journal of Anhui Agricultural University, 2015, 42(3): 439–443. https://www.cnki.com.cn/Article/CJFDTOTAL-ANHU201503020.htm
    [43] 冯珊珊, 梁雪映, 樊风雷, 等. 基于无人机多光谱数据的农田土壤水分遥感监测[J]. 华南师范大学学报(自然科学版), 2020, 52(6): 74–81. https://www.cnki.com.cn/Article/CJFDTOTAL-HNSF202006012.htm

    FENG Shanshan, LIANG Xueying, FAN Fenglei, et al. Monitoring of farmland soil moisture based on unmanned aerial vehicle multispectral data[J]. Journal of South China Normal University(Natural Science Edition), 2020, 52(6): 74–81. https://www.cnki.com.cn/Article/CJFDTOTAL-HNSF202006012.htm
    [44] 张智韬, 王海峰, 韩文霆, 等. 基于无人机多光谱遥感的土壤含水率反演研究[J]. 农业机械学报, 2018, 49(2): 173–181.

    ZHANG Zhitao, WANG Haifeng, HAN Wenting, et al. Inversion of soil moisture content based on multispectral remote sensing of UAVs[J]. Transactions of the Chinese Society of Agricultural Machinery, 2018, 49(2): 173–181.
    [45] 王海峰, 张智韬, 付秋萍, 等. 低空无人机多光谱遥感数据的土壤含水率反演[J]. 节水灌溉, 2018(1): 90–94.. doi: 10.3969/j.issn.1007-4929.2018.01.021

    WANG Haifeng, ZHANG Zhitao, FU Qiuping, et al. Inversion of soil moisture content based on multispectral remote sensing data of low altitude UAV[J]. Water Saving Irrigation, 2018(1): 90–94.. doi: 10.3969/j.issn.1007-4929.2018.01.021
    [46] 王启元, 赵艳玲, 房铄东, 等. 基于多光谱遥感的裸土土壤含水量反演研究[J]. 矿业科学学报, 2020, 5(6): 608–615. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX202006002.htm

    WANG Qiyuan, ZHAO Yanling, FANG Shuodong, et al. Inversion of soil moisture in bare soil based on multi-spectral remote sensing[J]. Journal of Mining Science and Technology, 2020, 5(6): 608–615. https://www.cnki.com.cn/Article/CJFDTOTAL-KYKX202006002.htm
    [47] 陈俊英, 王新涛, 张智韬, 等. 基于无人机–卫星遥感升尺度的土壤盐渍化监测方法[J]. 农业机械学报, 2019, 50(12): 161–169.. doi: 10.6041/j.issn.1000-1298.2019.12.018

    CHEN Junying, WANG Xintao, ZHANG Zhitao, et al. Soil salinization monitoring method based on UAV-satellite remote sensing scale-up[J]. Transactions of the Chinese Society of Agricultural Machinery, 2019, 50(12): 161–169.. doi: 10.6041/j.issn.1000-1298.2019.12.018
    [48] 冯文哲, 王新涛, 韩佳, 等. 基于卫星和无人机遥感数据尺度转换的土壤盐渍化监测研究[J]. 节水灌溉, 2020(11): 87–93.. doi: 10.3969/j.issn.1007-4929.2020.11.018

    FENG Wenzhe, WANG Xintao, HAN Jia, et al. Research on soil salinization monitoring based on scale conversion of satellite and UAV remote sensing data[J]. Water Saving Irrigation, 2020(11): 87–93.. doi: 10.3969/j.issn.1007-4929.2020.11.018
    [49] 边玲玲. 基于多源数据的黄河三角洲土壤盐分提取方法研究[D]. 淄博: 山东理工大学, 2020.

    BIAN Lingling. Study on the methods of soil salt extraction in the Yellow River Delta based on multi-source data[D]. Zibo: Shandong University of Technology, 2020.
    [50] ZEWDU S, SURYABHAGAVAN K V, BALAKRISHNAN M. Geo-spatial approach for soil salinity mapping in Sego Irrigation Farm, South Ethiopia[J]. Journal of the Saudi Society of Agricultural Sciences, 2017, 16(1): 16–24.. doi: 10.1016/j.jssas.2014.12.003
    [51] 王飞, 丁建丽, 伍漫春. 基于NDVI–SI特征空间的土壤盐渍化遥感模型[J]. 农业工程学报, 2010, 26(8): 168–173.. doi: 10.3969/j.issn.1002-6819.2010.08.029

    WANG Fei, DING Jianli, WU Manchun. Remote sensing monitoring models of soil salinization based on NDVI-SI feature space[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(8): 168–173.. doi: 10.3969/j.issn.1002-6819.2010.08.029
    [52] LOBELL D B, LESCH S M, CORWIN D L, et al. Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI[J]. Journal of Environmental Quality, 2010, 39(1): 35–41.. doi: 10.2134/jeq2009.0140
    [53] AMAL A, LALIT K, YOUSEF Y A. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region[J]. Geoderma, 2014(230/231): 1–8. http://www.sciencedirect.com/science/article/pii/S0016706114001372
    [54] 魏广飞. 基于无人机多光谱遥感的土壤盐渍化监测模型研究[D]. 咸阳: 西北农林科技大学, 2020.

    WEI Guangfei. Research on monitoring model of soil salinization based on multispectral remote sensing of UAV[D]. Xianyang: Northwest A & F University, 2020.
    [55] SANTOS I C D L, SANTOS A D, OUMAR Z, et al. Remote sensing to detect nests of the leaf-cutting Ant Atta sexdens(Hymenoptera: Formicidae) in teak plantations[J]. Remote Sensing, 2019, 11(14): 1641.. doi: 10.3390/rs11141641
    [56] 王海峰. 基于高光谱遥感的土壤水盐监测模型研究: 以河套灌区沙壕渠灌域沙壤土为例[D]. 咸阳: 西北农林科技大学, 2019.

    WANG Haifeng. Hyperspectral remote sensing based models for soil moisture and salinity prediction: A case study from sandy loam soil in Shahaoqu district of Hetao irrigation area[D]. Xianyang: Northwest A & F University, 2019.
    [57] 张智韬, 魏广飞, 姚志华, 等. 基于无人机多光谱遥感的土壤含盐量反演模型研究[J]. 农业机械学报, 2019, 50(12): 151–160.. doi: 10.6041/j.issn.1000-1298.2019.12.017

    ZHANG Zhitao, WEI Guangfei, YAO Zhihua, et al. Soil salt inversion model based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Machinery, 2019, 50(12): 151–160.. doi: 10.6041/j.issn.1000-1298.2019.12.017
    [58] 张曦. 基于无人机摄影测量技术的矿区沉陷监测方法研究[D]. 邯郸: 河北工程大学, 2019.

    ZHANG Xi. Study on the method of subsidence monitoring in mining areas based on UAV photogrammetry technology[D]. Handan: Hebei University of Engineering, 2019.
    [59] 芦家欣. 基于机载LiDAR点云的榆神矿区采煤沉陷建模[D]. 西安: 西安科技大学, 2020.

    LU Jiaxin. Mining subsidence modeling based on airborne LIDAR point cloud in Yushen mining area[D]. Xi'an: Xi'an University of Science and Technology, 2020.
    [60] 杨秋丽, 魏建新, 郑江华, 等. 离散点云构建数字高程模型的插值方法研究[J]. 测绘科学, 2019, 44(7): 16–23.

    YANG Qiuli, WEI Jianxin, ZHENG Jianghua, et al. Comparison of interpolation methods of digital elevation model using discrete point cloud data[J]. Science of Surveying and Mapping, 2019, 44(7): 16–23.
    [61] MENG Xuelian, CURRIT N, ZHAO Kaiguang. Ground filtering algorithms for airborne LiDAR data: A review of critical issues[J]. Remote Sensing, 2010, 2(3): 833–860.. doi: 10.3390/rs2030833
    [62] YU Jinze, WEI Mingqiang, QIN Jing, et al. Feature-preserving mesh denoising via normal guided quadric error metrics[J]. Optics and Lasers in Engineering, 2014, 62: 57–68.. doi: 10.1016/j.optlaseng.2014.05.002
    [63] 胡达, 黎永索, 张可能, 等. Kriging点云滤波改进算法及监测试验研究[J/OL]. 土木与环境工程学报(中英文), 2021: 1–12 [2021-03-12]. https://kns.cnki.net/kcms/detail/50.1218.TU.20210312.0940.002.html

    HU Da, LI Yongsuo, ZHANG Keneng, et al. Improved Kriging point cloud filtering algorithm and monitoring experiment[J]. Journal of Civil and Environmental Engineering, 2021: 1–12[2021-03-12]. https://kns.cnki.net/kcms/detail/50.1218.TU.20210312.0940.002.html
    [64] 汪文琪, 李宗春, 付永健, 等. 一种多尺度自适应点云坡度滤波算法[J/OL]. 武汉大学学报(信息科学版), 2021: 1–8[1-04-09]. https://doi.org/10.13203/j.whugis20200016

    WANG Wenqi, LI Zongchun, FU Yongjian, et al. A multi-scale adaptive slope filtering algorithm of point cloud[J]. Geomatics and Information Science of Wuhan University, 2021: 1–8 [2021-04-09]. https://doi.org/10.13203/j.whugis20200016
    [65] 李炼. 机载LiDAR点云滤波及分类算法研究[D]. 成都: 成都理工大学, 2014.

    LI Lian. Researching the filtering and classification algorithms of airborne LiDAR point cloud[D]. Chengdu: Chengdu University of Technology, 2014.
    [66] 曲金博. 基于聚类算法的三维激光点云数据去噪方法研究[D]. 沈阳: 沈阳建筑大学, 2020.

    QU Jinbo. Study on 3D laser point cloud data denoising method based on clustering algorithm[D]. Shenyang: Shenyang Jianzhu University, 2020.
    [67] 汤伏全, 芦家欣, 韦书平, 等. 基于无人机LiDAR的榆神矿区采煤沉陷建模方法改进[J]. 煤炭学报, 2020, 45(7): 2655–2666. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202007034.htm

    TANG Fuquan, LU Jiaxin, WEI Shuping, et al. Improvement of mining subsidence modeling method based on UAV LiDAR in Yushen mining area[J]. Journal of China Coal Society, 2020, 45(7): 2655–2666. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202007034.htm
    [68] 戴士杰, 任永潮, 张慧博. 各向异性扩散滤波的三维散乱点云平滑去噪算法[J]. 计算机辅助设计与图形学学报, 2018, 30(10): 1843–1849. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201810008.htm

    DAI Shijie, REN Yongchao, ZHANG Huibo. Study on smooth denoising of 3D scattered point clouds with anisotropic diffusion filtering[J]. Journal of Computer Aided Design and Computer Graphics, 2018, 30(10): 1843–1849. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201810008.htm
    [69] 李鹏飞, 吴海娥, 景军锋, 等. 点云模型的噪声分类去噪算法[J]. 计算机工程与应用, 2016, 52(20): 188–192.. doi: 10.3778/j.issn.1002-8331.1603-0354

    LI Pengfei, WU Hai'e, JING Junfeng, et al. Noise classification denoising algorithm for point cloud model[J]. Computer Engineering and Applications, 2016, 52(20): 188–192.. doi: 10.3778/j.issn.1002-8331.1603-0354
    [70] XU Xianlei, ZHAO Yanling, HU Zhenqi, et al. Boundary demarcation of the damaged cultivated land caused by coal mining subsidence[J]. Bulletin of Engineering Geology and the Environment, 2014, 73(2): 621–633.. doi: 10.1007/s10064-013-0495-2
    [71] 侯恩科, 首召贵, 徐友宁, 等. 无人机遥感技术在采煤地面塌陷监测中的应用[J]. 煤田地质与勘探, 2017, 45(6): 102–110.. doi: 10.3969/j.issn.1001-1986.2017.06.017

    HOU Enke, SHOU Zhaogui, XU Youning, et al. Application of UAV remote sensing technology in monitoring of coal mining-induced subsidence[J]. Coal Geology & Exploration, 2017, 45(6): 102–110.. doi: 10.3969/j.issn.1001-1986.2017.06.017
    [72] 高冠杰, 侯恩科, 谢晓深, 等. 基于四旋翼无人机的宁夏羊场湾煤矿采煤沉陷量监测[J]. 地质通报, 2018, 37(12): 2264–2269.

    GAO Guanjie, HOU Enke, XIE Xiaoshen, et al. The monitoring of ground surface subsidence related to coal seams mining in Yangchangwan coal mine by means of unmanned aerial vehicle with quad-rotors[J]. Geological Bulletin of China, 2018, 37(12): 2264–2269.
    [73] ZHOU Dawei, QI Lizhuang, ZHANG Demin, et al. Unmanned Aerial Vehicle(UAV) photogrammetry technology for dynamic mining subsidence monitoring and parameter inversion: A case study in China[J]. IEEE Access, 2020, 8: 16372–16386.. doi: 10.1109/ACCESS.2020.2967410
    [74] 胡振琪, 王新静, 贺安民. 风积沙区采煤沉陷地裂缝分布特征与发生发育规律[J]. 煤炭学报, 2014, 39(1): 11–18.

    HU Zhenqi, WANG Xinjing, HE Anmin. Distribution characteristic and development rules of ground fissures due to coal mining in windy and sandy region[J]. Journal of China Coal Society, 2014, 39(1): 11–18.
    [75] 胡青峰, 崔希民, 袁德宝, 等. 厚煤层开采地表裂缝形成机理与危害性分析[J]. 采矿与安全工程学报, 2012, 29(6): 864–869. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL201206019.htm

    HU Qingfeng, CUI Ximin, YUAN Debao, et al. Formation mechanism of surface cracks caused by thick seam mining and hazard analysis[J]. Journal of Mining & Safety Engineering, 2012, 29(6): 864–869. https://www.cnki.com.cn/Article/CJFDTOTAL-KSYL201206019.htm
    [76] 范立民, 张晓团, 向茂西, 等. 浅埋煤层高强度开采区地裂缝发育特征: 以陕西榆神府矿区为例[J]. 煤炭学报, 2015, 40(6): 1442–1447.

    FAN Limin, ZHANG Xiaotuan, XIANG Maoxi, et al. Characteristics of ground fissure development in high intensity mining area of shallow seam in Yushenfu coal field[J]. Journal of China Coal Society, 2015, 40(6): 1442–1447.
    [77] 施展宇. 地面三维激光扫描技术在开采沉陷应用研究[D]. 西安: 西安科技大学, 2014.

    SHI Zhanyu. Terrestrial 3D laser scanning technology application in mining subsidence[D]. Xi'an: Xi'an University of Science and Technology, 2014.
    [78] 刘文涛. 基于时序InSAR技术的矿区地面沉降监测与分析[D]. 西安: 西安科技大学, 2020.

    LIU Wentao. Monitoring and analysis of land subsidence in mining area based on time series InSAR technology[D]. Xi'an: Xi'an University of Science and Technology, 2020.
    [79] 谢晓深, 侯恩科, 高冠杰, 等. 宁夏羊场湾煤矿浅埋煤层开采地面塌陷发育规律及形成机理[J]. 地质通报, 2018, 37(12): 2233–2240. https://www.cnki.com.cn/Article/CJFDTOTAL-ZQYD201812014.htm

    XIE Xiaoshen, HOU Enke, GAO Guanjie, et al. A study of the development regularity and formation mechanism of ground subsidence in shallow coal seam mining of Yangchangwan coal mine, Ningxia[J]. Geological Bulletin of China, 2018, 37(12): 2233–2240. https://www.cnki.com.cn/Article/CJFDTOTAL-ZQYD201812014.htm
    [80] 侯恩科, 张杰, 谢晓深, 等. 无人机遥感与卫星遥感在采煤地表裂缝识别中的对比[J]. 地质通报, 2019, 38(2/3): 443–448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZQYD2019Z1026.htm

    HOU Enke, ZHANG Jie, XIE Xiaoshen, et al. Contrast application of unmanned aerial vehicle remote sensing and satellite remote sensing technology relating to ground surface cracks recognition in coal mining area[J]. Geological Bulletin of China, 2019, 38(2/3): 443–448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZQYD2019Z1026.htm
    [81] 侯恩科, 谢晓深, 徐友宁, 等. 羊场湾煤矿采动地裂缝发育特征及规律研究[J]. 采矿与岩层控制工程学报, 2020, 2(3): 037038.

    HOU Enke, XIE Xiaoshen, XU Youning, et al. Prediction of ground cracks induced by coal mining in Yangchangwan coal mine[J]. Journal of Mining and Strata Control Engineering, 2020, 2(3): 037038.
    [82] 毛崔磊. 黄土丘陵区采煤塌陷地裂缝分布特征研究[D]. 北京: 中国地质大学(北京), 2018.

    MAO Cuilei. Study on fracture distribution characteristics of coal mining collapse in loess hilly area[D]. Beijing: China University of Geosciences(Beijing), 2018.
    [83] 韦博文. 基于改进的MF–FDOG算法和无人机影像提取黄土地区地裂缝[D]. 成都: 西南交通大学, 2018.

    WEI Bowen. Extracting ground fissures in loess landform area using modified MF-FDOG algorithm and UAV images[D]. Chengdu: Southwest Jiaotong University, 2018.
    [84] JAIN R, KASTURI R, SCHUNCK B G. Machine vision[M]. New York: McGraw Hill, 1995.
    [85] 赵毅鑫, 许多, 孙波, 等. 基于无人机红外遥感和边缘检测技术的采动地裂缝辨识[J]. 煤炭学报, 2021, 46(2): 624–637.

    ZHAO Yixin, XU Duo, SUN Bo, et al. Investigation on ground fissure identification using UAV infrared remote sensing and edge detection technology[J]. Journal of China Coal Society, 2021, 46(2): 624–637.
    [86] SHRUTHI R B V, KERLE N, JETTEN V. Object-based gully feature extraction using high spatial resolution imagery[J]. Geomorphology, 2011, 134(3/4): 260–268. http://meetingorganizer.copernicus.org/EGU2011/EGU2011-7386.pdf
    [87] FENG Quanlong, LIU Jiantao, GONG Jianhua. UAV remote sensing for urban vegetation mapping using random forest and texture analysis[J]. Remote Sensing, 2015, 7(1): 1074–1094.. doi: 10.3390/rs70101074
    [88] HARALICK R M, STERNBERG S R, ZHUANG Xinhua. Image analysis using mathematical morphology[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(4): 532–550. http://www.onacademic.com/detail/journal_1000035550417210_52b8.html
    [89] ANDRÉ S, JEAN-PHILIPPE M, NORMAN K, et al. Image-based mapping of surface fissures for the investigation of landslide dynamics[J]. Geomorphology, 2013, 186: 12–27.. doi: 10.1016/j.geomorph.2012.12.010
    [90] BRUCE G B. Machine Vision Handbook[M]. London: Springer, 2012.
    [91] FRANCIONI M, SALVINI R, STEAD D, et al. An integrated remote sensing-GIS approach for the analysis of an open pit in the Carrara marble district, Italy: Slope stability assessment through kinematic and numerical methods[J]. Computers and Geotechnics, 2015, 67: 46–63.. doi: 10.1016/j.compgeo.2015.02.009
    [92] MA Guotao, HU Xiewen, YIN Yueping, et al. Failure mechanisms and development of catastrophic rockslides triggered by precipitation and open-pit mining in Emei, Sichuan, China[J]. Recent Landslides, 2018, 15(7): 1401–1414.. doi: 10.1007/s10346-018-0981-5
    [93] LI Qing, MIN Gaochen, CHEN Peng, et al. Computer vision-based techniques and path planning strategy in a slope monitoring system using unmanned aerial vehicle[J]. International Journal of Advanced Robotic Systems, 2020, 17(2): 172988142090430.. doi: 10.1177/1729881420904303
    [94] 宋诚. 数字摄影测量法在边坡监测中的应用研究[D]. 广州: 广州大学, 2014.

    SONG Cheng. The application research of digital photogrammetry method in slope monitoring[D]. Guangzhou: Guangzhou University, 2014.
    [95] 吕爱钟, 贾晓阳. 边坡危险滑动面及稳定安全系数的力学解析方法[J/OL]. 工程地质学报, 2021: 1–7[1-05-13]. https://doi.org/10.13544/j.cnki.jeg.2020-568

    LYU Aizhong, JIA Xiaoyang. Mechanical analysis method for dangerous sliding surface and factor of safety[J]. Journal of Engineering Geology, 2021: 1–7[2021-05-13]. https://doi.org/10.13544/j.cnki.jeg.2020-568
    [96] 靳远成, 赵鹏辉, 薄雾, 等. 基于无人机影像的边坡精细化建模及稳定性分析[J]. 水利与建筑工程学报, 2020, 18(6): 178–183.. doi: 10.3969/j.issn.1672-1144.2020.06.030

    JIN Yuancheng, ZHAO Penghui, BO Wu, et al. Refined modeling and stability analysis of slope based on UAV images[J]. Journal of Water Resources and Architectural Engineering, 2020, 18(6): 178–183.. doi: 10.3969/j.issn.1672-1144.2020.06.030
    [97] 岳西蒙, 伍法权, 沙鹏, 等. 基于三维点云建模的矿山边坡稳定性分析[J]. 中国矿业, 2021, 30(4): 89–95.. doi: 10.3969/j.issn.1009-105x.2021.04.008

    YUE Ximeng, WU Faquan, SHA Peng, et al. Stability analysis of mine slope based on 3D point cloud modeling[J]. China Mining Magazine, 2021, 30(4): 89–95.. doi: 10.3969/j.issn.1009-105x.2021.04.008
    [98] 金爱兵, 陈帅军, 赵安宇, 等. 基于无人机摄影测量的露天矿边坡数值模拟[J]. 岩土力学, 2021, 42(1): 255–264. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX202101028.htm

    JIN Aibing, CHEN Shuaijun, ZHAO Anyu, et al. Numerical simulation of open-pit mine slope based on unmanned aerial vehicle photogrammetry[J]. Rock and Soil Mechanics, 2021, 42(1): 255–264. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX202101028.htm
  • 加载中
表(2)
计量
  • 文章访问数:  204
  • HTML全文浏览量:  29
  • PDF下载量:  45
  • 被引次数: 0
出版历程
  • 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

目录

    /

    返回文章
    返回