spectral responses of eucalyptus trees submitted to...
TRANSCRIPT
Spectral Responses of Eucalyptus Trees Submitted to Natural Hydrocarbon
Seepages: An Integrated Approach from Leaf- to Canopy- Scales
Lucíola Alves Magalhães Carlos Roberto de Souza Filho
Wilson José Oliveira
{luciola, [email protected]} {[email protected]}
State University of Campinas - UNICAMP
Institute of Geosciences (IG)
The São Francisco Basin displays several gaseous hydrocarbon seeps. Among the most intensively investigated in recent years are those located in the Remanso do Fogo area (Minas Gerais State) . The study area is situated between the São Francisco and Paracatu rivers and is covered by Eucalyptus spp. (Eucalyptus) plantations.
Objectives Data Processing Conclusions Introduction
Introduction
Objectives Data Processing Conclusions Introduction
Introduction
Seepages at Paracatu River
One of the first evidence of seeps in this region
Objectives
Introduction Data Processing Conclusions Objectives
This study aims to characterize spectral modifications on eucalyptus trees induced by hydrocarbons gases at leaf and canopy scale using:
ProSpecTIR image superimposed on a Landsat image
R(56)G(36)B(14)
Hyperspectral ProSpecTIR-VS images: collected in June 2010, 357 bands from 0.4 to 2.5 µm, 1 m spatial resolution,
Portable spectrometer FieldSpec® 3 Hi-Res: that formed a
database of foliar spectral measurements acquired in July 2011, > 2000 bands, 0.4 to 2.5 µm.
Data Processing The employment of the chlorophyll a index (R807/R638) allowed the identification of spectrally anomalous areas related to geobotanical anomalies
Spectir bands (b88/b53)
Introduction Objectives Conclusions Data Processing
Data Processing
Field checking of these anomalies shows that eucalyptus trees in these areas are poorly developed, with foliage loss or simply do not tree growing.
Introduction Data Sets Conclusions Data Processing
Foliage loss Not grow No stress
Data Processing
Introduction Data Sets Conclusions Data Processing
Additionally, three vegetation indices were tested: Moisture Stress Index (MSI): R1599/R819, SpecTIR bands (b221/b90) sensitive to increase leaf water content;
Structure insensitive pigment index (SIPI): ((R800-R445) / (R800-
R680)), SpecTIR bands (b87-b12)/(b87- b62) designed to maximize the sensitivity of the index to the ratio of carotenoids to chlorophyll, so an increase in SIPI indicate increased canopy stress;
CSE: indicator of plant stress. R694/R760, Spectir bands (b65/b78).
Data Processing
R(Chla) G(MSI) B(SIPI) R(Cse) G(MSI) B(SIPI)
Introduction Data Sets Conclusions Data Processing
A ternary composition of these indices demonstrated a lateral variation in photosynthetic pigments and leaf water content.
The study area is dominated by healthy vegetation in contrast to the western portion, which showed high concentrations of gas and stressed vegetation.
Data Processing
Introduction Data Sets Conclusions Data Processing
An average of 256 pixels in ten points provides spectral signatures used after for a comparison with the leaf's signatures collected in situ.
ROI
Introduction Data Sets Conclusions Data Processing
Data Processing Spectral signature – average of 256 pixels. Smothing at The Unscrambler® X. Red: points situated in western stressed vegetation Green: points situated in eastern non-stressed vegetation.
Introduction Data Sets Conclusions Data Processing
PC-1 (94%)-6000 -4000 -2000 0 2000 4000 6000 8000 10000 12000 14000
PC
-2 (6
%)
-1600
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
Scores
41
42
43
44
45
46
47
48
49
77
Data Processing
PCA
– Sc
ore
Plot
s PC
1 x
PC2
Introduction Data Sets Conclusions Data Processing
Data Processing
44 77
Healthy Stressed
Even so that spectral responses is quite different, field observations do no show these changes as we can see in these photos.
Introduction Data Sets Conclusions Data Processing
PC-1 (94%)-6000 -4000 -2000 0 2000 4000 6000 8000 10000 12000 14000
PC
-2 (6
%)
-1600
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
Scores
41
42
43
44
45
46
47
48
49
77
Data Processing
PCA
– Sc
ore
Plot
s PC
1 x
PC2
Introduction Data Sets Conclusions Data Processing
Data Processing
X-variables (PC-1) (94%)0,3985 0,4701 0,5436 0,6197 0,6958 0,7729 0,851 0,9192 1,0075 1,1091 1,2096 1,3104 1,4098 1,5101 1,6106 1,711 1,7987 1,8992 1,9999 2,1003 2,2 2,2507 2,3516 2,4518
0
0,1
Loadings
SWIR VIS NIR
1.894 µm
1.453 µm
672 µm
PC1 Loadings - SWIR and VIS are keys to distinguish between stressed and non-to less-stressed vegetation
Introduction Data Sets Conclusions Data Processing
Data Processing Leafs were analyzed in situ using the Leaf Clip: average of three leafs around each point.
44
42
45
49
Introduction Data Sets Conclusions Data Processing
Data Processing Spectra collected in image (ProSpecTIR data) Canopy scale.
Average of 256 pixels in each point.
44
42
45
49
Introduction Data Sets Conclusions Data Processing
Data Processing
Leaf
Canopy
Introduction Data Sets Conclusions Data Processing
Data Processing
Leaf Canopy
238% 112%
Leaf spectra recorded an increase two times higher than at canopy scale when
comparing the stressed vegetation with its healthy equivalent.
Introduction Data Sets Conclusions Data Processing
Data Processing
Leaf Canopy
At near infrared, leaf spectra register no significant variations, while at canopy scale, a mild increase of 5% is recorded at 1.17 microns.
Introduction Data Sets Conclusions Data Processing
Data Processing
Canopy
59% 172%
Leaf
19%
73% 5% 8%
3%
Throughout the SWIR, the increase in reflectance indicates a fall in leaf water content and biochemical compounds (lignin and cellulose) with chlorosis increasing. But changes in biochemical compounds could be observed just at leaf scale.
Introduction Data Sets Data Processing Conclusions
Conclusions The results derived at canopy scale were significantly similar to the spectral signatures at leaf scale, excluding the near infrared (NIR) and the end of the shortwave infrared (SWIR); The visible (VIS) region register the higher differences between stressed to non-stressed vegetation; Although the ATCOR radiometric calibration applies a non-linear interpolation at water absorption bands at canopy spectra, the results shows a high correlation with the reflectance values obtained in the field using a portable spectrometer. Changes in biochemical compounds could be registered just at leaf scale; These results support the efficient use of vegetation remote sensing in prospecting activities of hydrocarbon seepages.
Introduction Data Sets Data Processing Conclusions
Acknowledgements
Conrad Wright, Mark Landers, Chris Joyce, Rob Cushing
Guilherme Brechbuhler de Pinho
Financial support
Lis Maria, Renato Rocha, Pedro Altoé