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Souza Filho Talk IGARSS 2011 final version.pptx

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  • 1. LAPIG
    INDIRECT DETECTION OF LIQUID HYDROCARBON LEAKAGES ON CONTAMINED SOIL-VEGETATION SYSTEMS THROUGH REFLECTANCE AND IMAGING (PROSPECTIR-VS) SPECTROSCOPY: A POTENTIAL TOOL FOR EXTENSIVE PIPELINE MONITORING
    Carlos Roberto Souza Filho1, Lucíola Magalhães1; Giuliana Quitério1 Marcos Nopper1 , Teodoro Almeida2 ; Wilson Olveira3; Lis Rabaco3; Renato Rocha3
    University of Campinas, Campinas, Brazil 1
    University of São Paulo, São Paulo, Brazil2
    Petrobras S.A./CENPES, Rio de Janeiro, Brazil3
  • 2. INTRODUCTION & OBJECTIVES
    • Current methods of pipeline monitoring poses hindrances to the early detection of small hydrocarbon spills.
    • 3. Assuming that liquid fuels (gasoline and diesel) are potential vegetation stressors, this study investigates the spectral characteristics of agricultural crops subjected to daily contamination of liquid hydrocarbons, using reflectance spectroscopy (portable FieldSpec Hi-Res sensor, with 2150 bands in the VNIR-SWIR range) and imaging spectroscopy (airborne ProSpecTIR-VS hyperspectral sensor, with 357 bands in the VNIR-SWIR range).
    • 4. The study comprises both greenhouse and real scale experiments, where we will seek the probable impacts of hydrocarbon contamination through spectral changes in 5 plant species commonly present in the vicinity of pipelines, particularly in Brazil, with focus on Brachiaria brizantha (“grass”) and Neonotonia wightii (“soybean”)
  • THE EXPERIMENTS
  • 5. THE EXPERIMENT
    2 SCALES: small scale experiment (“lysimeter”) & macro scale (real, field scale).
    3 types of spectral analysis
    Leaf (lysimeter) (FieldSpec Hi-Res – ASD)
    Canopy (macro scale experiment) (FieldSpec Hi-Res – ASD)
    Canopy (airborne hyperspectral survey) (357 bands at 5nm resolution -ProSpecTIR-VS)
    • 2 contaminants: diesel (DSL) and gasoline (GSL)
  • THE EXPERIMENT Vegetation Species
    Resistance (i.e. plagues) and commonness
    Agronomic importance
    Extensive occurrence along pipelines
    Neonotonia wightii
    “soybean”
    Brachiaria brizantha
    “perenial grass”
  • 6. THE EXPERIMENTLysimeters
  • 7. THE EXPERIMENTLysimeters
  • 8. THE EXPERIMENTLysimeters
  • 9. LYSIMETERSBrachiaria brizantha (grass)
    Brachiaria brisantha
    • Experiments – timeframes of 5 months.
    • 10. Leakage – begininning with plantation
    • 11. Periodical spectral measuments
    • 12. Peridiocal leakages
    • 13. Controlled irrigation
    • 14. Samples collected for biochemical analysis by the end of the experiment
    Measument
    Time-days
    Vol-HC-ml
    SWHC*
    *Soil Water Holding Capacity
  • 15. LISYMETERNeonotonia wightii (soybean)
    Neonotonia wightii
    • Experiments: timeframes of 3 months
    • 16. Leakage – begininning 30 days after plantation
    • 17. Periodical spectral measuments
    • 18. Peridiocal leakages (once per 15 days)
    • 19. Controlled irrigation (automatically)
    • 20. Samples collected for biochemical analysis by the end of the experiment
    Measument
    Time-days
    Vol-HC-ml
    SWHC*
  • 21. THE EXPERIMENTMacro (real) scale
  • 22. THE EXPERIMENTMacro (real) scale
    HC resistant matle to avoid ground contatmination
    Hydrocarbon leakage system
  • 23. THE EXPERIMENTMacro (real) scale
    Five plant species: Brachiaria brizantha (BR) (“grass”), Neonotonia wightii (SJ) (“soybean”),Saccharum spp (CA) (“sugar cane”), Phaseolus vulgaris (FE) (“bean”), and Zea mays (MI) (“maize”) .
  • 24. THE EXPERIMENTMacro (real) scale
    • Experiments – timeframe of 2 (May-April/2010)
    • 25. Periodical spectral measuments of both leaf and canopy
    • 26. Peridiocal leakages – daily leakage of 200l of HCs
    • 27. Controlled irrigation
    • 28. Samples collected for biochemical analysis every week
    APRIL
    MAY
  • 29. THE EXPERIMENTCanopy Measuments from Specifically-designed Platform
    Platform for canopy spectral measuments
    • FieldSpec High Resolution (ASD);
    • 30. Sampled area: 30cm;
    • 31. 10 samples per parcel.
  • THE EXPERIMENTProSpecTIR Airborne System
    • Data acquisition: MAy, 18th, 2010
    • 32. 14 days after HC leakage began
    • 33. 2600 L of HCs in the system = 130L per parcel
    • 34. ProsSpecTIR Spectral resolution: VIS/NIR:125 channel; SWIR:232 channel
    • 35. Spatial resolution: 60 cm
  • RESULTS
  • 36. LYSIMETERSBrachiaria brizantha
    DSL
    GSL
    CTR
    CTR
    M1 (45 days/200mL)
    Morphological alterations
    M4 (93 days/250mL)
    M7 (145 days/300mL)
  • 37. M1 = 35 days/0mL
    Morphological Alterations
    Canopy
    Roots
    M4 = 64 days/100mL
    GSL
    DSL
    CTR
    M7 = 99 days/200mL
    GSL CTR DSL
    Leaves
    LYSIMETERSNeonotonia wightii
  • 38. MACRO SCALE EXPERIMENT CSe RATIO (R694/R760 )
    GRASS
    SOYBEAN
    LYSIMETER
    - Leaves -
    Spectral Measument
    Spectral Measument
    PLATFORM
    - Dossel -
    Spectral Measument
    Spectral Measument
  • 39. MACRO SCALE EXPERIMENT
    Comparative photos of plant canopies when spectral alterations are perceived through CSe ratios
    GRASS
    GSL/M4
    CTR/M5
    DSL/M4
    SOYBEAN
    GSL/M5
    CTR/M5
    DSL/M4
  • 40. LYSIMETERSBrachiaria brizantha
    SHORTWAVE INFRARED (SWIR) REGION
    Three patterns are observed at:
    • 2477nm ; 2485 - 2495nm e 2440 - 2485nm
    Continuum-removed reflectance
    Wavelength (nm)
    Poly-Saccharides
  • 41. LYSIMETERSNeonotonia wightii
    SHORTWAVE INFRARED (SWIR) REGION
    Spectral pattern at 2062 nm >> association with leaf biochemical analysis (rise in monosaccharides (“sugar”) content)
    monosaccharides
    Normalised average (% in relation to CTR)
    Wavelength (nm)
  • 42. INFORMATION EXTRACTION OF HYPERSPECTRAL DATA (PROSPECTIR VS)
    Two step algorithm (Almeida & Souza Filho, 2004; 2008):
    1) Production of 15 spectral indices > enhancement of specific spectral signatures of vegetation properties
    2) Principal Component Analysis applied to three sub-stes of spectral indices
    Indices applied to vegetation analysis
    Group
    Index
    Spectral Formula
    ProSpecTIR channels
    -Caroten
    Antocianin
    a-Chlorophyll
    b-Chlorophyll
    Carotenoids
    SIPI
    CSe
    NDVI
    VOG1
    WBI
    MAC
    Leaf water
    Lignin
    Cellulose
    Nitrogen
  • 43. INFORMATION EXTRACTION OF HYPERSPECTRAL DATA (PROSPECTIR VS)
    GSL
    DSL
    CTR
    Degree of stress
    CSe Ratio
    Degree of stress based on the CSe (694nm/760nm) ratio
  • 44. INFORMATION EXTRACTION OF HYPERSPECTRAL DATA (PROSPECTIR VS)
    GSL
    DSL
    Degree of stress
    CTR
    PC1 - Group 1 (VNIR)
    a-Chlorophyll, b-Chlorophyll, Carotenoids
    Eigenvector matrix
    Eigenvector a-caroten antocian. a-Chlrop b-Chlorop Caroten. SIPI CSe
  • 45. INFORMATION EXTRACTION OF HYPERSPECTRAL DATA (PROSPECTIR VS)
    GSL
    Degree of stress
    DSL
    CTR
    PC1 - Group 2 (NIR)
    NDVI, VOG1 e WBI
    Eigenvector matrix
    Eigenvector
  • 46. INFORMATION EXTRACTION OF HYPERSPECTRAL DATA (PROSPECTIR VS)
    GSL
    Degree of stress
    DSL
    CTR
    PC2 – Group 3 (NIR/SWIR)
    Lignin
    EigenvectorLeaf Water Lignin Cellulose Nitrogen
  • 47. INFORMATION EXTRACTION OF HYPERSPECTRAL DATA (PROSPECTIR VS)
    PC1
    Group 1
    GSL
    DSL
    PC1
    Group 2
    PC2
    Group 3
    CTR
    RGB Colour Composition
    R: PC1 - Group 1 (VNIR)
    G: PC1 - Group 2 (NIR)
    B: PC2 - Group 3 (NIR/SWIR)
     
  • 48. DISCUSSIONS & CONCLUSIONS
  • 49. DISCUSSIONS & CONCLUSIONS
    • The proposed methodology showed a high correlation between canopy spectral measurements taken at close range with the FieldSpec Hi-Res sensor and from the airborne ProspecTIR-VS sensor.
    • 50. It was possible to characterize the reflectance of leaves grown in soils contaminated by low concentrations of gasoline and diesel and differentiate them from plants grown on soil without HCs.
    • 51. The use of selected vegetation indices showed a high correlation with the behavior of vegetation stressed by the presence of HCs in all three scales of observations.
    • 52. The spectral changes were similar among species but more prominent for gasoline (GSL) than diesel (DSL), occurring at different timeframes and under different doses of HCs
    • 53. The results confirm the higher toxicity of gasoline for all selected crops.
    • 54. The development of this work supports the possibility to preserve certain crops along pipelines that can be used as a bio-indicator of small leakages and the types of crops more susceptible to stress-induced leakage.
    • 55. It also makes a first step on the establishment of the initial timing (i.e., exposure time and volume of injected hydrocarbons) when the contamination effects are more perceptible remotely.
  • LAPIG
    Thank you !
    Geosciences Institute
    University of Campinas (UNICAMP)
    www.ige.unicamp.br
    www.ige.unicamp.br/sdm
    beto@ige.unicamp.br