이전|다음

Sentinel-2 데이터를 기반으로 한 광산 지역 잎 표면 먼지 퇴적 정보 추출 및 먼지원 식별

  • role： First author第一作者
  • Affiliation：

    Institute of Geologic Survey, China University of Geosciences (Wuhan), Wuhan 430074, China

    Hubei Institute of Land Surveying and Mapping, Wuhan 430010, China

  • Email：21844ss@cug.edu.cn
  • Introduction：帅爽，研究方向为遥感地质、地学遥感应用。E-mail： 21844ss@cug.edu.cn
SHUAI Shuang13,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Geophysics & Geomatics, China University of Geoscience (Wuhan), Wuhan 430074, China

  • Email：3slab@cug.edu.cn
  • Introduction：张志，研究方向为遥感地质、地学遥感应用。E-mail： 3slab@cug.edu.cn
ZHANG Zhi2*,  
  • Affiliation：

    Institute of Geologic Survey, China University of Geosciences (Wuhan), Wuhan 430074, China

LYU Xinbiao1,  
  • Affiliation：

    Hubei Institute of Land Surveying and Mapping, Wuhan 430010, China

CHEN Si3,  
  • Affiliation：

    Hubei Institute of Land Surveying and Mapping, Wuhan 430010, China

MA Zicheng3,  
  • Affiliation：

    Hubei Institute of Land Surveying and Mapping, Wuhan 430010, China

XIE Cuirong3

추상적인

잎 표면의 먼지 퇴적 원격 감지는 광산 먼지 오염 상태를 평가하는 중요한 수단 중 하나입니다. 자연 먼지와 비교할 때, 중금속이 농축된 광산 먼지는 인간 건강과 식생 성장에 더 심각한 위협을 초래합니다. 과거 잎 표면 먼지 퇴적 원격 감지는 주로 먼지 퇴적량의 반전 및 모니터링에 집중하였으며, 광산 먼지와 자연 먼지의 차이에 대한 연구는 부족했습니다. 본 논문은 Sentinel-2 데이터를 사용하여 내몽골자치구 자오울라-차간 납-아연-은 광산 지역을 사례로, 잎 표면 먼지 퇴적의 분광 반응 특성 분석을 바탕으로 특징 벡터 주성분 선택법인 FPCS (Feature-oriented Principal Components Selection) 방법을 기반으로 연구 지역의 잎 표면 먼지 퇴적 범위와 강도를 추출하였습니다. 광산 먼지원과 자연 먼지원의 분광 특성 차이를 분석하여 먼지원 분광 지수인 DSI (Dust-source Spectrum Index)를 구축하여 광산 먼지와 자연 먼지를 구분하였고, 잎 표면 먼지 퇴적 유형, 강도와 광산 지물 분포 간의 상관성 및 주요 광산 먼지원의 비산 먼지 확산 특성을 분석하였습니다. 결과는 잎 표면 먼지 퇴적으로 인해 식생 가시광선 반사율은 증가하고 근적외선 반사율은 감소하며, 식생 적변(紅邊)이 ‘블루 이동’하며, 먼지원에서 멀어질수록 가시광선 반사율은 점차 감소하고 적변 위치는 더 긴 파장 방향으로 이동함을 나타냅니다. 광산 먼지원과 자연 먼지원은 분광 특성에 차이가 있으며, 광산 먼지원 잎 표면 먼지 퇴적 화소는 864.7 nm 부근에서 반사율 흡수 특성을 보입니다. FPCS는 연구 지역 잎 표면 먼지 퇴적 범위와 강도를 성공적으로 추출하였고, DSI는 광산 먼지와 자연 먼지를 효과적으로 구분하였으며, 추출된 광산 잎 표면 먼지 퇴적 화소는 광산 지물과 공간적으로 높은 상관성을 보였습니다. 연구 지역 주요 광산 먼지원은 폐석 더미와 광산 도로이며, 폐석 더미의 비산 먼지 확산 강도와 거리가 광산 도로보다 큽니다. 본 연구는 광산 개발 먼지 오염 상태의 신속한 평가를 위한 기술적 방안을 제공합니다.

키워드

원격 감지; 광산 지역; 잎 표면 먼지 퇴적; 먼지원 정보; Sentinel-2; FPCS; 먼지원 분광 지수(DSI)

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Hunt G R. 1977. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3): 501-513
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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

더 읽기

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website