Foliar dust information extraction and dust source identification in mining area based on Sentinel-2 data
- Vol. 27, Issue 9, Pages: 2165-2178(2023)
Published： 07 September 2023
DOI： 10.11834/jrs.20221051
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摘要
叶面降尘遥感监测是评估矿山粉尘污染状况的重要手段之一，与自然粉尘相比，重金属富集的矿山粉尘对人类健康和植被生长造成更严重的威胁。以往叶面降尘遥感监测大多针对降尘量进行反演和监测，而没有研究矿区粉尘与自然粉尘的差异。本文使用哨兵二号数据，以内蒙古自治区甲乌拉—查干铅锌银矿区为例，在分析叶面降尘的光谱响应特征基础上，基于特征向量主成分选择FPCS（Feature-oriented Principal Components Selection）方法，提取研究区叶面降尘范围和强度，在分析矿山尘源与自然尘源光谱特征差异基础上，建立了尘源光谱指数DSI（Dust-source Spectrum Index），用于区分矿山粉尘与自然粉尘，并分析了叶面降尘类型、强度与矿山地物分布的相关性，以及主要矿山尘源的扬尘扩散特征。结果表明叶面降尘导致植被可见光波段反射率升高、近红外波段反射率降低、植被红边“蓝移”，远离粉尘源方向，可见光波段反射率逐渐降低，红边位置逐渐向长波方向移动；矿山尘源与自然尘源光谱特征存在差异，矿山尘源叶面降尘像元在864.7 nm附近显示反射率吸收特征；FPCS成功提取了研究区叶面降尘的范围和强度，DSI能有效区分矿山降尘与自然降尘，提取的矿山叶面降尘像元与矿山地物空间相关性强；研究区主要矿山尘源为废石堆和矿山道路，其中废石堆扬尘扩散强度和距离大于矿山道路。该研究可为矿山开发粉尘污染状况快速评估提供一种技术思路。
Abstract
Remote sensing monitoring of foliar dust is one of the important methods to assess mine dust pollution. Compared with natural dust
mine dust
enriched with heavy metals
poses a more serious threat to human health and vegetation growth. Recently
most of foliar dust monitoring carried out inversion and monitoring for the amount of foliar dust
without distinguishing between mine dust and natural dust in mining areas. In this paper
methods were proposed to extract foliar dust information and to identify dust source
taking the Jiawula–Chagan Pb-Zn-Ag mining area in Inner Mongolia as an example.
The Feature-oriented Principal Component Selection (FPCS) method was applied to extract the distribution and intensity of foliar dust based on the analysis of the spectral response characteristics of foliar dust fall for Sentinel-2 data. Moreover
a Dust-source Spectrum Index (DSI) was proposed based on the reflectance differences between mine foliar dust and natural foliar dust
and DSI was used to distinguish the extracted foliar dust information between mine dust and natural dust. Then
the correlation between the type of dust source
the intensity of foliar dust
and the distribution of mine objects was analyzed
and the main mine dust sources and their dust diffusion characteristics were assessed. Finally
minimum noise fraction and pixel purity index methods were applied to analyze spectral characteristics difference and mineral composition difference between natural dust and mine dust.
Results showed foliar dust increased the vegetation reflectance in visible regions (B1—B4)
decreased the vegetation reflectance in near infrared regions (B7—B8A)
and shifted the red edge of the vegetation to shorter wavelength direction. The spectral reflectance gradually decreased with the increase of distances away from the mine dust sources
while the spectral reflectance gradually decreased in visible regions and increased in near infrared regions with the increase of distances away from dry lake dust sources. Mine dust sources and the affecting vegetation showed reflectance absorption characteristics near 864.7 nm (B8A)
attributed to pyrite oxidation. The distribution and intensity of foliar dust were successfully extracted by FPCS and compared with the reported experience model. DSI could distinguish mine dust fall from natural dust fall. The extracted mine foliar dust pixels had a strong spatial correlation with the mine objects. The main mine dust sources in the study area were mine dumps and mine roads
the dust diffusion intensity of mine dumps was greater
and the dust diffusion of mine dumps spread farther.
The sensitivity of Sentinel-2 reflectance to identify the intensity of foliar dust fall and to distinguish the type of dust source was verified. FPCS could be applied to extract the distribution and intensity of foliar dust
without ground sampling. The extracted mine foliar dust pixels were classified into mine dust fall and natural dust fall using the proposed DSI and achieved excellent results. This paper provided a method for the rapid assessment of dust pollution in mine areas.
关键词
遥感矿区叶面降尘尘源信息Sentinel-2FPCS尘源光谱指数（DSI）
Keywords
remote sensingmining areafoliar dustdust sourceSentinel-2Feature-oriented Principle Component Selection (FPCS)Dust Source Index (DSI)
references
Allen A G, Nemitz E, Shi J P, Harrison R M and Greenwood J C. 2001. Size distributions of trace metals in atmospheric aerosols in the United Kingdom. Atmospheric Environment, 35(27): 4581-4591 [DOI: 10.1016/S1352-2310(01)00190-Xhttp://dx.doi.org/10.1016/S1352-2310(01)00190-X]
Chudnovsky A and Ben-Dor E. 2008. Application of visible, near-infrared, and short-wave infrared (400-2500 nm) reflectance spectroscopy in quantitatively assessing settled dust in the indoor environment. Case study in dwellings and office environments. Science of the Total Environment, 393(2/3): 198-213 [DOI: 10.1016/j.scitotenv.2007.11.022http://dx.doi.org/10.1016/j.scitotenv.2007.11.022]
Crósta A P and Moore J M. 1989. Enhancement of landsat thematic mapper imagery for residual soil mapping in SW minais gerais state, Brazil: a prospecting case history in greenstone belt terrain//7th Thematic Conference on Remote Sensing for Exploration Geology. Calgary: [s.n.]: 1173-1187
Đorđević D, Vukmirović Z, Tošić I and Unkašević M. 2004. Contribution of dust transport and resuspension to particulate matter levels in the Mediterranean atmosphere. Atmospheric Environment, 38(22): 3637-3645 [DOI: 10.1016/j.atmosenv.2004.04.001http://dx.doi.org/10.1016/j.atmosenv.2004.04.001]
Etyemezian V. 2003. Vehicle-based road dust emission measurement (III): effect of speed, traffic volume, location, and season on PM10 road dust emissions in the Treasure Valley, ID. Atmospheric Environment, 37(32): 4583-4593 [DOI: 10.1016/s1352-2310(03)00530-2http://dx.doi.org/10.1016/s1352-2310(03)00530-2]
Fairlie T D, Jacob D J and Park R J. 2007. The impact of transpacific transport of mineral dust in the United States. Atmospheric Environment, 41(6): 1251-1266 [DOI: 10.1016/j.atmosenv.2006.09.048http://dx.doi.org/10.1016/j.atmosenv.2006.09.048]
Gao D R, Su T B, Lü K, Zhang J H and Zhao W B. 2016. Metallogenic geological features and prospecting of Jiawula-Chagan Pb-Zn-Ag ore deposit, Inner Mongolia. Mineral Exploration, 7(3): 391-398
高德荣, 苏庭宝, 吕奎, 张建华, 赵文斌. 2016. 内蒙古甲乌拉—查干铅锌银矿床成矿地质特征及找矿实践. 矿产勘查, 7(3): 391-398
Gao Y, Lan D M, Huang X Q, Xing J X and Shang J. 2016. Effects of tailings pond on vegetation in Baiyinnuoer Lead-Zinc mine. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 37(4): 60-65
高原, 蓝登明, 黄晓强, Xing J X, 尚洁. 2016. 白音诺尔铅锌矿尾矿库扬尘风积物对植被生长的影响. 内蒙古农业大学学报(自然科学版), 37(4): 60-65 [DOI: 10.16853/j.cnki.1009-3575.2016.04.010http://dx.doi.org/10.16853/j.cnki.1009-3575.2016.04.010]
Green A A, Berman M, Switzer P and Craig M D. 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26(1): 65-74 [DOI: 10.1109/36.3001http://dx.doi.org/10.1109/36.3001]
Hunt G R. 1977. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3): 501-513 [DOI: 10.1190/1.1440721http://dx.doi.org/10.1190/1.1440721]
Jain N, Singh R, Roy P, Martha T R, Kumar K V and Chauhan P. 2018. Mapping of hydrothermally altered zones in Aravalli Supergroup of rocks around Dungarpur and Udaipur, India, using Landsat-8 OLI and spectroscopy. Arabian Journal of Geosciences, 11(16): 455 [DOI: 10.1007/s12517-018-3799-5http://dx.doi.org/10.1007/s12517-018-3799-5]
Ji L, Xia W L, Xiang L and Wang S M. 1994. Mineral composition and sedimentation rate of surficial sediments in Hulun Lake, Inner Mongolia. Journal of Lake Sciences, 6(3): 227-232
吉磊, 夏威岚, 项亮, 王苏民. 1994. 内蒙古呼伦湖表层沉积物的矿物组成和沉积速率. 湖泊科学, 6(3): 227-232 [DOI: 10.18307/1994.0305http://dx.doi.org/10.18307/1994.0305]
Jing W L, Zhou X, Zhang C, Wang C Y and Jiang H. 2018. Machine learning for estimating leaf dust retention based on hyperspectral measurements. Journal of Sensors, 2018: 6026259 [DOI: 10.1155/2018/6026259http://dx.doi.org/10.1155/2018/6026259]
Kaufman Y J, Tanré D, Dubovik O, Karnieli A and Remer L A. 2001. Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing. Geophysical Research Letters, 28(8): 1479-1482 [DOI: 10.1029/2000gl012647http://dx.doi.org/10.1029/2000gl012647]
Kayet N, Pathak K, Chakrabarty A, Kumar S, Chowdary V M, Singh C P, Sahoo S and Basumatary S. 2019. Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas. Journal of Cleaner Production, 218: 993-1006 [DOI: 10.1016/j.jclepro.2019.01.305http://dx.doi.org/10.1016/j.jclepro.2019.01.305]
Liu L, Li Y, Zhou J, Han L and Xu X L. 2018. Gold-copper deposits in wushitala, southern tianshan, northwest China: application of ASTER data for mineral exploration. Geological Journal, 53(S2): 362-371 [DOI: 10.1002/gj.2989http://dx.doi.org/10.1002/gj.2989]
Liu L, Zhuang D F, Zhou J and Qiu D S. 2011. Alteration mineral mapping using masking and Crosta technique for mineral exploration in mid-vegetated areas: a case study in Areletuobie, Xinjiang (China). International Journal of Remote Sensing, 32(7): 1931-1944 [DOI: 10.1080/01431161003639678http://dx.doi.org/10.1080/01431161003639678]
Ma B D, Li X X, Jiang Z W, Pu R L, Liang A M and Che D F. 2020. Dust dispersion and its effect on vegetation spectra at canopy and pixel scales in an open-pit mining area. Remote Sensing, 12(22): 3759 [DOI: 10.3390/rs12223759http://dx.doi.org/10.3390/rs12223759]
Ma B D, Pu R L, Wu L X and Zhang S. 2017. Vegetation index differencing for estimating foliar dust in an ultra-low-grade magnetite mining area using landsat imagery. IEEE Access, 5: 8825-8834 [DOI: 10.1109/access.2017.2700474http://dx.doi.org/10.1109/access.2017.2700474]
Moradpour H, Paydar G R, Pour A B, Kamran K V, Feizizadeh B, Muslim A M and Hossain M S. 2022. Landsat-7 and ASTER remote sensing satellite imagery for identification of iron skarn mineralization in metamorphic regions. Geocarto International, 37(7): 1971-1998 [DOI: 10.1080/10106049.2020.1810327http://dx.doi.org/10.1080/10106049.2020.1810327]
Özyavaş A. 2016. Assessment of image processing techniques and ASTER SWIR data for the delineation of evaporates and carbonate outcrops along the Salt Lake Fault, Turkey. International Journal of Remote Sensing, 37(4): 770-781 [DOI: 10.1080/2150704x.2015.1130873http://dx.doi.org/10.1080/2150704x.2015.1130873]
Peng J, Wang J Q, Xiang H Y, Niu J L, Chi C M and Liu W Y. 2015. Effect of foliar dustfall content (FDC) on high spectral characteristics of pear leaves and remote sensing quantitative inversion of FDC. Spectroscopy and Spectral Analysis, 35(5): 1365-1369
彭杰, 王家强, 向红英, 牛建龙, 迟春明, 柳维扬. 2015. 叶面降尘对香梨叶片高光谱特征的影响及定量反演研究. 红外与毫米波学报, 35(5): 1365-1369 [DOI: 10.3964/j.issn.1000-0593(2015)05-1365-05http://dx.doi.org/10.3964/j.issn.1000-0593(2015)05-1365-05]
Peng J, Xiang H Y, Wang J Q, Ji W J, Liu W Y, Chi C M and Zuo T G. 2013. Quantitative model of foliar dustfall content using hyperspectral remote sensing. Journal of Infrared and Millimeter Waves, 32(4): 313-318, 343
彭杰, 向红英, 王家强, 纪文君, 柳维扬, 迟春明, 左天国. 2013. 叶面降尘的高光谱定量遥感模型. 红外与毫米波学报, 32(4): 313-318, 343 [DOI: 10.3724/SP.J.1010.2013.00313http://dx.doi.org/10.3724/SP.J.1010.2013.00313]
Rodríguez L, Ruiz E, Alonso-Azcárate J and Rincón J. 2009. Heavy metal distribution and chemical speciation in tailings and soils around a Pb-Zn mine in Spain. Journal of Environmental Management, 90(2): 1106-1116 [DOI: 10.1016/j.jenvman.2008.04.007http://dx.doi.org/10.1016/j.jenvman.2008.04.007]
Sims D A and Gamon J A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2/3): 337-354 [DOI: 10.1016/S0034-4257(02)00010-Xhttp://dx.doi.org/10.1016/S0034-4257(02)00010-X]
Song G C and Zhang Z. 2020. Remote sensing monitoring method for dust and wind accumulation in multi-metal mining area of Xin Barag Right Banner, Inner Mongolia. Remote Sensing for Land and Resources, 32(2): 46-53
宋国策, 张志. 2020. 内蒙古新巴尔虎右旗多金属矿区扬尘风积物遥感监测方法. 国土资源遥感, 32(2): 46-53 [DOI: 10.6046/gtzyyg.2020.02.07http://dx.doi.org/10.6046/gtzyyg.2020.02.07]
Stagakis S, Markos N, Sykioti O and Kyparissis A. 2010. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment, 114(5): 977-994 [DOI: 10.1016/j.rse.2009.12.006http://dx.doi.org/10.1016/j.rse.2009.12.006]
Su K, Yu Q, Hu Y H, Liu Z L, Wang P C, Zhang Q B, Zhu J Y, Niu T and Yue D P. 2019. Inversion and effect research on dust distribution of urban forests in Beijing. Forests, 10(5): 418 [DOI: 10.3390/f10050418http://dx.doi.org/10.3390/f10050418]
Tegen I, Werner M, Harrison S P and Kohfeld K E. 2004. Relative importance of climate and land use in determining present and future global soil dust emission. Geophysical Research Letters, 31(5): L05105 [DOI: 10.1029/2003gl019216http://dx.doi.org/10.1029/2003gl019216]
Vellak K, Liira J, Karofeld E, Galanina O, Noskova M and Paal J. 2014. Drastic turnover of bryophyte vegetation on bog microforms initiated by air pollution in Northeastern Estonia and Bordering Russia. Wetlands, 34(6): 1097-1108 [DOI: 10.1007/s13157-014-0569-3http://dx.doi.org/10.1007/s13157-014-0569-3]
Wambo J D T, Pour A B, Ganno S, Asimow P D, Zoheir B, dos Reis Salles R, Nzenti J P, Pradhan B and Muslim A M. 2020. Identifying high potential zones of gold mineralization in a sub-tropical region using Landsat-8 and ASTER remote sensing data: a case study of the Ngoura-Colomines goldfield, eastern Cameroon. Ore Geology Reviews, 122: 103530 [DOI: 10.1016/j.oregeorev.2020.103530http://dx.doi.org/10.1016/j.oregeorev.2020.103530]
Wang C, Chen J, Wu J, Tang Y H, Shi P J, Black T A and Zhu K. 2017. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sensing of Environment, 196: 1-12 [DOI: 10.1016/j.rse.2017.04.031http://dx.doi.org/10.1016/j.rse.2017.04.031]
Wang H F, Fang N, Yan X, Chen F T, Xiong Q L and Zhao W J. 2016. Retrieving dustfall distribution in Beijing City based on ground spectral data and remote sensing. Spectroscopy and Spectral Analysis, 36(9): 2911-2918
王皓飞, 房娜, 晏星, 陈凡涛, 熊秋林, 赵文吉. 2016. 基于光谱特征的北京城区植被滞尘分布反演. 光谱学与光谱分析, 36(9): 2911-2918 [DOI: 10.3964/j.issn.1000-0593(2016)09-2911-08http://dx.doi.org/10.3964/j.issn.1000-0593(2016)09-2911-08]
Wang T, Liu Y, Wu H Y and Zuo Y M. 2012. Influence of foliar dust on crop reflectance spectrum and nitrogen monitoring. Spectroscopy and Spectral Analysis, 32(7): 1895-1898
王涛, 刘洋, 吴海云, 左月明. 2012. 叶面尘对作物反射光谱及氮营养监测的影响. 光谱学与光谱分析, 32(7): 1895-1898 [DOI: 10.3964/j.issn.1000-0593(2012)07-1895-04http://dx.doi.org/10.3964/j.issn.1000-0593(2012)07-1895-04]
Wu Z C, Ye F W, Guo F S, Liu W H, Li H L and Yang Y. 2018. A review on application of techniques of principle component analysis on extracting alteration information of remote sensing. Journal of Geo-information Science, 20(11): 1644-1656
吴志春, 叶发旺, 郭福生, 刘文恒, 李华亮, 杨羿. 2018. 主成分分析技术在遥感蚀变信息提取中的应用研究综述. 地球信息科学学报, 20(11): 1644-1656 [DOI: 10.12082/dqxxkx.2018.180195http://dx.doi.org/10.12082/dqxxkx.2018.180195]
Xu J H and Yu J T. 2013. Air dustfall impact on spectrum of ficus Microcarpa’s leaf. Advanced Materials Research, 655-657: 813-815 [DOI: 10.4028/www.scientific.net/AMR.655-657.813http://dx.doi.org/10.4028/www.scientific.net/AMR.655-657.813]
Yan X, Shi W Z, Zhao W J and Luo N N. 2015. Mapping dustfall distribution in urban areas using remote sensing and ground spectral data. Science of the Total Environment, 506-507: 604-612 [DOI: 10.1016/j.scitotenv.2014.11.036http://dx.doi.org/10.1016/j.scitotenv.2014.11.036]
Zhang P F, Guli J, Yin J Q, Bao A M, Yao F and Liu J P. 2014. Using hyperspectral indices to measure the effect of mine dust on the growth of three typical desert plants. Spectroscopy and Spectral Analysis, 34(8): 2162-2168 [DOI: 10.3964/j.issn.1000-0593(2014)08-2162-07http://dx.doi.org/10.3964/j.issn.1000-0593(2014)08-2162-07]
Zhang Y J, Yang J M and Chen W. 2002. A study of the method for extraction of alteration anomalies from the ETM+ (TM) data and its application: geologic basis and spectral precondition. Remote Sensing for Land and Resources, 14(4): 30-36
张玉君, 杨建民, 陈薇. 2002. ETM+(TM)蚀变遥感异常提取方法研究与应用——地质依据和波谱前提. 国土资源遥感, 14(4): 30-36 [DOI: 10.6046/gtzyyg.2002.04.07http://dx.doi.org/10.6046/gtzyyg.2002.04.07]
Zhang Y J, Zeng Z M and Chen W. 2003. The methods for extraction of alteration anomalies from the ETM+ (TM) data and their application: method selection and technological flow chart. Remote Sensing for Land and Resources, 15(2): 44-49
张玉君, 曾朝铭, 陈薇. 2003. ETM+(TM)蚀变遥感异常提取方法研究与应用—方法选择和技术流程. 国土资源遥感, 15(2): 44-49 [DOI: 10.6046/gtzyyg.2003.02.11http://dx.doi.org/10.6046/gtzyyg.2003.02.11]
Zhao H Y. 2007. Recent 45 years climate climate change and its effects on ecological environment on Hulunbeier sandy land. Chinese Journal of Ecology, 26(11): 1817-1821
赵慧颖. 2007. 呼伦贝尔沙地45年来气候变化及其对生态环境的影响. 生态学杂志, 26(11): 1817-1821
Zhao Y B, Lei S G, Yang X C, Gong C G, Wang C J, Cheng W, Li H and She C C. 2020. Study on spectral response and estimation of grassland plants dust retention based on hyperspectral data. Remote Sensing, 12(12): 2019 [DOI: 10.3390/rs12122019http://dx.doi.org/10.3390/rs12122019]
Zhu J Y, Yu Q, Zhu H, He W J, Xu C Y, Liao J Y, Zhu Q Y and Su K. 2019. Response of dust particle pollution and construction of a leaf dust deposition prediction model based on leaf reflection spectrum characteristics. Environmental Science and Pollution Research, 26(36): 36764-36775 [DOI: 10.1007/s11356-019-06635-4http://dx.doi.org/10.1007/s11356-019-06635-4]
Zhu J Y, Zhang X N, He W J, Yan X M, Yu Q, Xu C Y, Jiang Q, Huang H G and Wang R R. 2020. Response of plant reflectance spectrum to simulated dust deposition and its estimation model. Scientific Reports, 10(1): 15803 [DOI: 10.1038/s41598-020-73006-2http://dx.doi.org/10.1038/s41598-020-73006-2]
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Institute of Geologic Survey, China University of Geosciences
Institute of Geophysics & Geomatics, China University of Geoscience
School of Earth Science and Resources, Chang'an University
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences
School of Land Engineering, Chang'an University
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