Development of remote sensing methods for assessing and mapping soil pollution with heavy metals
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http://www.fao.org/globalsoilpartnership This presentation was made during the International Workshop "Soil Spectroscopy: the present and future of Soil Monitoring" that took place at FAO HQ Rome, Italy from 4-6 December 2013. This presentation was made by Asmaryan Sh. G., Muradyan V. S.Sahakyan L.V. & Saghatelyan A. K., and it has the objective to present Development of remote sensing methods for assessing and mapping soil pollution with heavy metals. ©FAO: http://www.fao.org
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Development of remote sensing methods for assessing and mapping soil pollution with heavy metals
- 1. Development of remote sensing methods for assessing and mapping soil pollution with heavy metals 4-6 December , 2013, FAO HQ, Rome, Italy, Asmaryan Sh. G., Muradyan V. S.Sahakyan L.V. Saghatelyan A. K. The Center for Ecological-Noosphere Studies of the National Academy of Sciences of the Republic of Armenia
- 2. Co Fe Pb Ti Zn Cu Ba Soil pollution One of dominant environmental pollutants are known to be the so-called heavy metals (HM). In this respect most problematic are mining centers and urban sites. Mn
- 3. Development of remote sensing methods for assessing and mapping soil pollution with heavy metals Terrestrial ecogeochemical investigations Spectrometry of soils (400-2500nm) Soil Transects - From Sample Bags 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 400 700 1000 1300 1600 1900 2200 2500 Wavelength (nm) Reflectancefactor 1b.000.sco 1b.001.sco 1b.002.sco 1b.003.sco 1d.000.sco 1d.001.sco 1d.002.sco 1d.003.sco 1f.000.sco 1f.001.sco 1f.002.sco 1f.003.sco 1h.000.sco 1h.001.sco 1h.002.sco 1h.003.sco 1j.000.sco 1j.001.sco 1j.002.sco 1j.003.sco Spectral analysis of satellite images Collation of data calibration-validation -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 400,000 444,000 488,000 532,000 576,000 620,000 664,000 708,000 752,000 796,000 840,000 884,000 928,000 972,000 1016,000 1060,000 1104,000 1148,000 1192,000 1236,000 1280,000 1324,000 1368,000 1412,000 1456,000 1500,000 1544,000 1588,000 1632,000 1676,000 1720,000 1764,000 1808,000 1852,000 1896,000 1940,000 1984,000 2028,000 2072,000 2116,000 2160,000 2204,000 2248,000 2292,000 2336,000 2380,000 2424,000 2468,000 Co Fe Pb Cr Ti Cu Mo Zn Sr V Zr Ba Mn Mapping soil pollution
- 5. SOFTWARE & DATA SUPPORT Satellite imagesGIS-softwares ENVI ArcGIS Ecogeochemical data of soils Spectrophotometry data of soils“Fieldspec3 ASD” WorldView 2 EO-1 Sensors
- 6. Correlation coefficients in different spectral ranges between HM in Kajaran soils and spectral irradiation values (reflectance) obtained with help of a spectrophotometer HM 2005 2011 Spectral wavl engt h (nm) Correlation coeffi cient (R) Spectral wavl engt h (nm) Correlation coeffici ent (R) Co 401 0,22 1033 -0,12 Fe 2476 0,29 2206 -0,14 Pb 2498 -0,61 1282 -0,21 Cr 2500 -0,28 2460 -0,34 Ti 2498 0,51 1639 0,27 Cu 1251 -0,27 402 0,31 Mo 685 -0,33 408 0,24 Zn 2498 -0,56 1720 -0,33 Sr 1199 -0,30 408 0,34 V 2498 0,38 408 -0,16 Zr 2498 0,21 408 -0,25 Ba 417 0,44 713 -0,10 Mn 2114 0,50 1654 0,37 Soil Transects - From Sample Bags 0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 400 700 1000 1300 1600 1900 2200 2500 Wavelength (nm) Reflectancefactor 1b.000.sco 1b.001.sco 1b.002.sco 1b.003.sco 1d.000.sco 1d.001.sco 1d.002.sco 1d.003.sco 1f.000.sco 1f.001.sco 1f.002.sco 1f.003.sco 1h.000.sco 1h.001.sco 1h.002.sco 1h.003.sco 1j.000.sco 1j.001.sco 1j.002.sco 1j.003.sco
- 7. HM Spectral wavelength (nm) Correlation coefficient (R) Co 770-895 -0,58 Fe 770-895 -0,59 Pb 400-450 0,59 Cr 450-510 0,74 Ti 585-625 -0,20 Cu 770-895 -0,64 Mo 630-690 -0,25 Zn 400-450 0,52 Sr 860-1040 0,39 V 585-625 -0,54 Zr 860-1040 0,38 Ba 585-625 -0,22 Mn 630-690 0,34 As 770-895 -0,50 W 630-690 -0,59 Correlation coefficients in different spectral ranges between HM in Kajaran soils and spectral irradiation values (reflectance) obtained with help of satellite image WorldView-2
- 8. Calculated possible distribution of heavy metals contents in Kajaran barren soils by satellite images 1. NDVI= (NIR- RED)/(NIR+RED), 2. Selected were pixels having 0,08-0,3 values that correspond to barren soils. Zn = 0,9552*( SIV) - 149,69 (1) Cr = 0,4048*( SIV)- 26,742 (2) Pb = 0,3458*( SIV)- 75,527 (3) 3. Employing a Raster Calculator instrument into ArcGIS. 4. Then, in the first approach a picture of possible distribution of Pb, Zn and Cr contents in Kajaran barren soils
- 9. Possible contents of Zn and Cr in Kajaran barren soils according to a hyperspectral satellite image EO-1 and a multi-spectral satellite image WorldView-2
- 10. 1. There exists a direct and inverse correlation between actual contents of HM vis Pb, Zn, Cr, Ti, Cu, Mn in the soils of city of Kajaran and spectral irradiation values obtained with help of a spectrophotometer and satellite images. However, the highest correlation is detected in respect to Pb, Zn и Cr. Conclusions 2. To verify the objectivity of the method one needs to collate between produced maps and geochemical maps of contents of the given elements, whereas further improvement of the methods will help implement direct mapping of heavy metal pollution of soils through treatment and classification of satellite images. 3. Collation of data of terrestrial ecogeochemical investigations, a spectrophotometric survey and remote observations may underlie creation of a remote system of soil monitoring.