Please wait a minute...
 
自然资源遥感  2023, Vol. 35 Issue (3): 284-291    DOI: 10.6046/zrzyyg.2022196
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
德兴铜矿矿山重金属污染修复效果高光谱遥感评价
王嘉芃1,2(), 徐建国3, 沈家晓1,2, 张登荣1,2()
1.杭州师范大学遥感与地球科学研究院,杭州 311121
2.浙江省城市湿地与区域变化研究重点实验室,杭州 311121
3.宁波市宇科国土勘测规划设计有限公司,宁波 315000
Evaluating the remediation effect of heavy metal pollution in the Dexing copper mine based on hyperspectral remote sensing
WANG Jiapeng1,2(), XU Jianguo3, SHEN Jiaxiao1,2, ZHANG Dengrong1,2()
1. Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China
2. Zhejiang Provincial Key Laboratory of Urban Wetlands and Regional Change, Hangzhou 311121, China
3. Ningbo Yuke Land Survey, Planning and Design Co., Ltd., Ningbo 315000, China
全文: PDF(4549 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

合理快速评价矿山重金属污染修复的效果对于矿山生态恢复与重建治理工作具有重要意义。以德兴铜矿为例,根据野外实测植被光谱,分析矿区内主要植被的典型光谱特征; 根据实验室化验的植被叶片内重金属含量,分析其重金属含量与光谱特征参数红边位置的关系; 利用 2003年和2009年2景Hyperion高光谱卫星数据计算矿区植被的红边位置,推断矿区植被富集重金属的情况,进而评价矿山重金属污染修复的效果。研究结果表明,在典型复垦区1号、2号尾矿库四周重金属污染修复取得了较好的效果; 与2003年相比,2009年重金属污染修复整体上取得了一定的成效,大部分区域被修复,但仍有部分新增污染区,需采取修复措施。该方法可快速、合理、大范围地评价矿区重金属污染修复的效果。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
王嘉芃
徐建国
沈家晓
张登荣
关键词 光谱特征红边位置重金属污染修复高光谱德兴铜矿    
Abstract

Evaluating the remediation effect of heavy metal pollution in mines properly and rapidly holds considerable significance for ecological restoration and rehabilitation of mines. Based on the field-measured vegetation spectra, this study analyzed the typical spectral features of the main vegetation in the Dexing copper mining area. According to the heavy metal content in the leaves of vegetation tested in the laboratory, this study analyzed the relationship between heavy metal content and red edge position-a spectral characteristic parameter. This study calculated the red edge position of the vegetation in 2003 and 2009 using 2-scene Hyperion hyperspectral data, inferring the heavy metal enrichment in the vegetation of the mining area. Furthermore, this study evaluated the remediation effect of heavy metal pollution in the mining area. The results show that satisfactory results have been achieved from the remediation of heavy metal pollution around mine tailings nos. 1 and 2 in typical reclamation areas. Compared with 2003, 2009 witnessed generally satisfactory remediation effects of heavy metal pollution, with most areas being remedied and some newly polluted areas requiring remediation. The method proposed in this study can achieve a quick and reasonable evaluation of the remediation effect of large-scale heavy metal pollution in mining areas.

Key wordsspectral feature    red edge position    remediation of heavy metal pollution    hyperspectrum    Dexing copper mine
收稿日期: 2022-05-16      出版日期: 2023-09-19
ZTFLH:  TP79  
基金资助:国家重点研发计划项目“典型资源环境要素识别提取与定量遥感技术”(2016YFB0501404)
通讯作者: 张登荣(1966-),男,博士,教授,研究方向为地质遥感、GIS开发应用。Email: 13805747261@126.com
作者简介: 王嘉芃(1991-),女,硕士,工程师,研究方向为遥感生态监测、环境调查评估。Email: 361608279@qq.com
引用本文:   
王嘉芃, 徐建国, 沈家晓, 张登荣. 德兴铜矿矿山重金属污染修复效果高光谱遥感评价[J]. 自然资源遥感, 2023, 35(3): 284-291.
WANG Jiapeng, XU Jianguo, SHEN Jiaxiao, ZHANG Dengrong. Evaluating the remediation effect of heavy metal pollution in the Dexing copper mine based on hyperspectral remote sensing. Remote Sensing for Natural Resources, 2023, 35(3): 284-291.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022196      或      https://www.gtzyyg.com/CN/Y2023/V35/I3/284
Fig.1  研究区位置
采样点 Pb Cu Cd Mn Zn
1号尾矿库 4.4 19.0 0.15 46 21
2号尾矿库 3.5 12.0 0.10 100 36
背景值 3.1 6.9 0.08 232 25
Tab.1  不同尾矿库茅草叶片的重金属含量
Fig.2  Hyperion影像预处理后效果
Fig.3  总体技术路线
Fig.4  茅草和盐肤木反射波谱曲线
Fig.5  2003年和2009年德兴铜矿植被红边位置
Fig.6  2003年和2009年相同红边位置区间内像元个数对比
Fig.7  相比2003年,2009年已修复区和新增污染区分布
[1] De Simoni B S, Leite M G P. Assessment of rehabilitation projects results of a gold mine area using landscape function analysis[J]. Applied Geography, 2019, 108:22-29.
doi: 10.1016/j.apgeog.2019.05.005
[2] 李国政. 绿色发展视阈下矿山地质修复模式的升级与重塑[J]. 中国矿业大学学报(社会科学版), 2019(3):92-104.
Li G Z. Upgrading and reshaping of mine geological restoration mode from the perspective of green development[J]. Journal of China University of Mining and Technology(Social Sciences), 2019(3):92-104.
[3] 殷亚秋, 蒋存浩, 鞠星, 等. 海南岛2018年矿山地质环境遥感评价和生态修复对策[J]. 自然资源遥感, 2022, 34(2):194-202.doi:10.6046/zrzyyg.2021136.
doi: 10.6046/zrzyyg.2021136
Yin Y Q, Jiang C H, Ju X, et al. Remote sensing evaluation of mine geological environment of Hainan Island in 2018 and ecological restoration countermeasures[J]. Remote Sensing for Natural Resources, 2022, 34(2):194-202.doi:10.6046/zrzyyg.2021136.
doi: 10.6046/zrzyyg.2021136
[4] Vicenc C, Oriol O, Josep M A. RESTOQUARRY:Indicators for self-evaluation of ecological restoration in open-pit mines[J]. Ecological Indicators, 2019, 102:437-445.
doi: 10.1016/j.ecolind.2019.03.001
[5] Hou X Y, Liu S H, Zhao S, et al. Interaction mechanism between floristic quality and environmental factors during ecological restoration in a mine area based on structural equation modeling[J]. Ecological Engineering, 2018, 124:23-30.
doi: 10.1016/j.ecoleng.2018.09.021
[6] 杨灵玉, 高小红, 张威, 等. 基于Hyperion影像植被光谱的土壤重金属含量空间分布反演——以青海省玉树县为例[J]. 应用生态学报, 2016, 27(6):1775-1784.
doi: 10.13287/j.1001-9332.201606.030
Yang L Y, Gao X H, Zhang W, et al. The estimating heavy metal concentrations in topsoil from vegetation reflectance spectra of Hyperion images:A case study of Yushu County,Qinghai,China[J]. Chinese Journal of Applied Ecology, 2016, 27(6):1775-1784.
[7] 李盛阳, 刘志文, 刘康, 等. 航天高光谱遥感应用研究进展(特邀)[J]. 红外与激光工程, 2019, 48(3):0303001.
Li S Y, Liu Z W, Liu K, et al. Advances in application of space hyperspectral remote sensing(invited)[J]. Infrared and Laser Engineering, 2019, 48(3):0303001.
doi: 10.3788/IRLA
[8] 杨璐, 高永光, 胡振琪. 铜胁迫下植被光谱变化规律研究[J]. 矿业研究与开发, 2008, 28(4):74-76.
Yang L, Gao Y G, Hu Z Q. Study on spectral change of vegetation under Cu stress[J]. Mining Research and Development, 2008, 28(4):74-76.
[9] 李庆亭, 杨锋杰, 张兵, 等. 重金属污染胁迫下盐肤木的生化效应及波谱特征[J]. 遥感学报, 2008, 12(2):284-290.
Li Q T, Yang F J, Zhang B, et al. Biogeochemistry responses and spectral characteristics of rhus chinensis mill under heavy metal contamination stress[J]. Journal of Remote Sensing, 2008, 12(2):284-290.
[10] 任红艳, 庄大方, 潘剑君, 等. 重金属污染水稻的冠层反射光谱特征研究[J]. 光谱学与光谱分析, 2010, 30(2):430-434.
Ren H Y, Zhuang D F, Pan J J, et al. Study on canopy spectral characteristics of paddy polluted by heavy metals[J]. Spectroscopy and Spectral Analysis, 2010, 30(2):430-434.
[11] 赵汀, 王安建, 夏江周. 洎水河流域重金属污染区五节芒叶片光谱特征响应研究[J]. 国土资源遥感, 2010, 22(2):46-54.doi:10.6046/gtzyyg.2010.02.11.
doi: 10.6046/gtzyyg.2010.02.11
Zhao T, Wang A J, Xia J Z. The spectral response of typical vegetation leaves to heavy metal pollution in Jishui River basin[J]. Remote Sensing for Land and Resources, 2010, 22(2):46-54.doi:10.6046/gtzyyg.2010.02.11.
doi: 10.6046/gtzyyg.2010.02.11
[12] Liu M L, Liu X N, Wu L, et al. Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance[J]. Computers and Geosciences, 2011, 37:1254-1263.
doi: 10.1016/j.cageo.2010.11.019
[13] 陈圣波, 周超, 王晋年. 黑龙江多金属矿区植物胁迫光谱及其与金属元素含量关系研究[J]. 光谱学与光谱分析, 2012, 32(5):1310-1315.
Chen S B, Zhou C, Wang J N. Vegetation stress spectra and their relations with the contents of metal elements within the plant leaves in metal mines in Heilongjiang[J]. Spectroscopy and Spectral Analysis, 2012, 32(5):1310-1315.
[14] Deventer H V, Cho M A. Assessing leaf spectral properties of Phragmites australis impacted by acid mine drainage[J]. South African Journal of Science, 2014, 110:1-12.
[15] 朱叶青, 屈永华, 刘素红, 等. 重金属铜污染植被光谱响应特征研究[J]. 遥感学报, 2014, 18(2):335-352.
Zhu Y Q, Qu Y H, Liu S H, et al. Spectral response of wheat and lettuce to copper pollution[J]. Journal of Remote Sensing, 2014, 18(2):335-352.
[16] Song L, Jian J, Tan D J, et al. Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area,Chongqing,China[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 34:1-9.
doi: 10.1016/j.jag.2014.06.013
[17] Zheng T, Liu N, Wu L, et al. Estimation of chlorophyll content in potato leaves based on spectral red edge position[J]. IFAC-Papers OnLine, 2018, 51(17):602-606.
[18] Li D, Cheng T, Zhou K, et al. WREP:A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017(129):103-117.
[19] Prabir K D, Karun K C, Laxman B, et al. A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data[J]. International Journal of Remote Sensing, 2014, 35(4):1432-1449.
doi: 10.1080/01431161.2013.877616
[20] Pu R L, Gong P, Biging G S, et al. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003(41):916-921.
[21] 姚付启, 张振华, 杨润亚, 等. 基于红边参数的植被叶绿素含量高光谱估算模型[J]. 农业工程学报, 2009, 25(s2):123-129.
Yao F Q, Zhang Z H, Yang R Y, et al. Hyperspectral models for estimating vegetation chlorophyll content based on red edge parameter[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(s2):123-129.
[22] 佘宝, 黄敬峰, 石晶晶, 等. 基于红边位置变化特征的油菜种植区域提取[J]. 农业工程学报, 2013, 29(15):145-152.
She B, Huang J F, Shi J J, et al. Extracting oilseed rape growing regions based on variation characteristics of red edge position[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(15):145-152.
[23] Zhu L H, Chen Z X, Wang J J, et al. Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance[J]. Marine Pollution Bulletin, 2014(86):332-341.
[24] 张佳伟, 王仲林, 谭先明, 等. 利用不同红边位置算法估测玉米叶绿素含量[J]. 浙江大学学报(农业与生命科学版), 2021, 47(4):464-472.
Zhang J W, Wang Z L, Tan X M, et al. Estimation of corn chlorophyll content using different red edge position algorithms[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2021, 47(4):464-472.
[25] 甘甫平, 刘圣伟, 周强. 德兴铜矿矿山污染高光谱遥感直接识别研究[J]. 地球科学(中国地质大学学报), 2004, 29(1):119-126.
Gan F P, Liu S W, Zhou Q. Identification of mining pollution using Hyperion data at Dexing copper mine in Jiangxi Province,China[J]. Earth Science(Journal of China University of Geosciences), 2004, 29(1):119-126.
[26] 付卓, 肖如林, 申文明, 等. 典型矿区土壤重金属污染对植被影响遥感监测分析——以江西省德兴铜矿为例[J]. 环境与可持续发展, 2016, 41(6):66-68.
Fu Z, Xiao R L, Shen W M, et al. Monitoring and analysis of the impacts of soil heavy metal pollution on vegetation in typical mining areas using remote sensing imageries:A case study of Jiangxi Dexing copper mine[J]. Environment and Sustainable Development, 2016, 41(6):66-68.
[27] 薛利红, 杨林章. 采用不同红边位置提取技术估测蔬菜叶绿素含量的比较研究[J]. 农业工程学报, 2008, 24(9):165-169.
Xue L H, Yang L Z. Comparative study on estimation of chlorophyll content in spinach leaves using various red edge position extraction techniques[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(9):165-169.
[28] 张永贺, 郭啸川, 褚武道, 等. 基于红边位置的木荷叶片叶绿素含量估测模型研究[J]. 红外与激光工程, 2013, 42(3):798-804.
Zhang Y H, Guo X C, Chu W D, et al. Estimation model of schima superba leaf chlorophyll content based on red edge position[J]. Infrared and Laser Engineering, 2013, 42(3):798-804.
[29] 马东辉, 柯长青. 南京冬季典型植被光谱特征分析[J]. 遥感技术与应用, 2016, 31(4):702-708.
Ma D H, Ke C Q. Research on spectral characteristics of winter typical vegetation in Nanjing[J]. Remote Sensing Technology and Application, 2016, 31(4):702-708.
[30] 吴继友, 杨旭东, 张福军, 等. 山东招远金矿区赤松针叶反射光谱红边的季节特征[J]. 遥感学报, 1997, 1(2):124-128.
Wu J Y, Yang X D, Zhang F J, et al. Seasonal characteristics of spectral reflectance of korean pine leaves in the gold mine area of Zhaoyuan City in Shandong Province[J]. Journal of Remote Sensing, 1997, 1(2):124-128.
[31] 张海星, 姚丽文, 熊报国, 等. 德兴铜矿1号尾矿库废弃土地生态恢复试验研究[J]. 环境与开发, 1999, 14(1):10-11.
Zhang H X, Yao L W, Xiong B G, et al. Study on the eco-recover test of waste land of 1# tailings bank in Dexing copper mine[J]. Environment and Exploitation, 1999, 14(1):10-11.
[32] 杨修, 高林. 德兴铜矿矿山废弃地植被恢复与重建研究[J]. 生态学报, 2001, 21(11):1932-1940.
Yang X, Gao L. A study on re-vegetation in mining wasteland of Dexing copper mine,China[J]. Acta Ecologica Sinica, 2001, 21(11):1932-1940.
[1] 郑宗生, 刘海霞, 王振华, 卢鹏, 沈绪坤, 唐鹏飞. 改进3D-CNN的高光谱图像地物分类方法[J]. 自然资源遥感, 2023, 35(2): 105-111.
[2] 张国建, 刘胜震, 孙英君, 俞凯杰, 刘丽娜. 基于弱监督鲁棒性自编码的高光谱异常检测[J]. 自然资源遥感, 2023, 35(2): 167-175.
[3] 李天驰, 王道儒, 赵亮, 凡仁福. 基于Landsat8遥感数据的西沙群岛永乐环礁底质分类与变化分析[J]. 自然资源遥感, 2023, 35(2): 70-79.
[4] 孔卓, 杨海涛, 郑逢杰, 李扬, 齐济, 朱沁雨, 杨忠霖. 高光谱遥感图像大气校正研究进展[J]. 自然资源遥感, 2022, 34(4): 1-10.
[5] 张鹏强, 高奎亮, 刘冰, 谭熊. 联合空谱信息的高光谱影像深度Transformer网络分类[J]. 自然资源遥感, 2022, 34(3): 27-32.
[6] 孙肖, 徐林林, 王晓阳, 田野, 王伟, 张中跃. 基于优化K-P-Means解混方法的高光谱图像矿物识别[J]. 自然资源遥感, 2022, 34(3): 43-49.
[7] 晏红波, 韦晚秋, 卢献健, 杨志高, 黎振宝. 基于高光谱特征的土壤含水量遥感反演方法综述[J]. 自然资源遥感, 2022, 34(2): 1-9.
[8] 孙肖, 彭军还, 赵锋, 王晓阳, 吕洁, 张登峰. 基于空间统计学的高光谱遥感影像主成分选择方法[J]. 自然资源遥感, 2022, 34(2): 37-46.
[9] 王茜, 任广利. 高光谱遥感异常信息在阿尔金索拉克地区铜金矿找矿工作中的应用[J]. 自然资源遥感, 2022, 34(1): 277-285.
[10] 曲海成, 王雅萱, 申磊. 多感受野特征与空谱注意力结合的高光谱图像超分辨率算法[J]. 自然资源遥感, 2022, 34(1): 43-52.
[11] 陈洁, 张立福, 张琳珊, 张红明, 童庆禧. 紫外-可见光水质参数在线监测技术研究进展[J]. 自然资源遥感, 2021, 33(4): 1-9.
[12] 高文龙, 张圣微, 林汐, 雒萌, 任照怡. 煤矿开采中SOM的遥感估算和时空动态分析[J]. 自然资源遥感, 2021, 33(4): 235-242.
[13] 刘咏梅, 范鸿建, 盖星华, 刘建红, 王雷. 基于无人机高光谱影像的NDVI估算植被盖度精度分析[J]. 自然资源遥感, 2021, 33(3): 11-17.
[14] 李双权, 马玉凤, 刘勋, 李长春, 杜军. 郑州邙山枣树沟黄土剖面常量元素含量的高光谱反演[J]. 自然资源遥感, 2021, 33(3): 121-129.
[15] 杜程, 李得林, 李根军, 杨雪松. 基于高原盐湖光谱特性下的溶解氧反演应用与探讨[J]. 自然资源遥感, 2021, 33(3): 246-252.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发