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

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

4. City Kadjaran

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.