استخراج معلومات الترسيب على سطح الأوراق في منطقة المناجم بناءً على بيانات Sentinel-2 والتعرف على مصادر الغبار

  • 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: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: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

ملخص

تُعدُ مراقبة الترسيب على سطح الأوراق عبر الاستشعار عن بعد إحدى الوسائل المهمة لتقييم حالة تلوث الغبار في المناجم. مقارنة بالغبار الطبيعي، يشكل الغبار المعدني الغني بالمعادن الثقيلة في المناجم تهديدًا أكثر خطورة على صحة الإنسان ونمو النبات. في السابق، كانت مراقبة الترسيب على سطح الأوراق تركز في الغالب على الانعكاس الكمي للغبار ولم تبحث في الفروقات بين غبار المناجم والغبار الطبيعي. تستخدم هذه الدراسة بيانات Sentinel-2 كنموذج لمنطقة منجم يانغولا-تشاجان للرصاص والزنك والفضة في منطقة منغوليا الداخلية، حيث استُخدمت طريقة FPCS (الاختيار القائم على المتجهات الرئيسية الموجهة للميزات) لاستخراج مدى وشدة الترسيب على سطح الأوراق بناءً على تحليل خصائص الاستجابة الطيفية. وبناءً على اختلافات الخصائص الطيفية لمصادر الغبار المعدنية والطبيعية، تم إنشاء مؤشر الطيف مصدر الغبار (DSI) لتمييز غبار المناجم عن الغبار الطبيعي. كما تم تحليل العلاقة بين نوعية الترسيب على سطح الأوراق، شدته، وتوزيع المظاهر المعدني في المنطقة، بالإضافة إلى خصائص انتشار الغبار من مصادر الغبار الرئيسية. أظهرت النتائج أن الترسيب على سطح الأوراق يؤدي إلى زيادة الانعكاسية في نطاق الضوء المرئي، وانخفاضها في نطاق الأشعة تحت الحمراء القريبة، وانتقال الحافة الحمراء للنباتات إلى الأطوال الموجية الزرقاء. بعيدًا عن مصادر الغبار، تتناقص انعكاسية الضوء المرئي تدريجيًا، وتتحرك الحافة الحمراء نحو الأطوال الموجية الأطول. توجد اختلافات طيفية بين مصادر الغبار المعدنية والطبيعية، حيث يظهر بكسل الترسيب على سطح الأوراق قرب 864.7 نانومتر امتصاصًا في الانعكاسية. نجحت طريقة FPCS في استخراج مدى وشدة الترسيب، ويُظهر مؤشر DSI قدرة فعالة على التمييز بين الترسيب المعدني والطبيعي. هناك ارتباط مكاني قوي بين بكسلات الترسيب المستخرجة ومظاهر المنطقة المعدني. المصادر الرئيسية للغبار هي مكبات النفايات والطرق المعدنية، مع امتداد وانتشار الغبار من مكبات النفايات بشكل أكبر مقارنة بالطرق. توفر هذه الدراسة نهجًا تقنيًا سريعًا لتقييم حالة تلوث الغبار في تطوير المناجم.

مفهوم

الاستشعار عن بعد; منطقة المناجم; ترسيب على سطح الأوراق; معلومات مصدر الغبار; Sentinel-2; FPCS; مؤشر طيف مصدر الغبار (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

قراءة النص الكامل

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