Espectroscopia de Imageamento

A espectroscopia de imageamento (ou sensoriamento hiperespectral) consiste na obtenção de imagens em centenas de bandas espectrais (de largura nanométrica) e de forma contínua no espectro, como resposta à interação da energia eletromagnética com os alvos, seja ela refletida ou emitida (Goetz, 2009; Kruse, 2012).

Os sensores hiperespectrais proporcionam melhores resultados e produzem informações mais robustas, uma vez que fornecem um espectro de reflectância contínuo para cada pixel na cena. Imageadores hiperespectrais, frequentemente disponíveis em plataformas aéreas (e.g. ProSpecTIR – VS, HyMap, AVIRIS), fornecem imagens com maior resolução espacial e espectral em relação a sensores orbitais (Zomer, 2009), o que permite a detecção, mapeamento e caracterização direta de HCs ao invés da classificação de efeitos secundários de alteração. Em função da elevada resolução espectral destes sensores, os dados obtidos a partir das imagens podem ser comparados diretamente com dados de bibliotecas espectrais geradas em campo ou laboratório, viabilizando assim a utilização de algoritmos de desmistura espectral para a classificação das imagens.

Considerando o elevado potencial do imageamento hiperespectral, as imagens aqui obtidas com a estação de imageamento espectral automatizada (SisuROCK) no micro- experimento de impregnação, somadas às imagens adquiridas com o sensor hiperespectral aerotransportado (ProSpecTIR-VS) no macro-experimento, foram submetidas ao pré- processamento com wavelets e ao processamento digital de imagens com a utilização de algoritmos de classificação, respectivamente. Os endmembers utilizados para a classificação foram extraídos das bibliotecas espectrais de referência geradas nesses experimentos.

O objetivo do uso desta técnica foi o desenvolvimento de métodos de identificação e mapeamento remoto de regiões continentais afetadas por exsudações naturais ou vazamentos de petróleo, bem como a demonstração do potencial das bibliotecas espectrais geradas para a utilização em processamentos com algoritmos de classificação e desmistura espectral.

4. Referências

Abdi, H. (2007). “Partial least square regression”. In N.Salkind (Ed.), Encyclopedia of Measurement Statistics. Thousand Oaks, CA: SAGE Publications, INC. pp. 741-745

Aske, N., Kallevik, H. and Sjöblom, J. (2001). “Determination of saturate aromatic resin and asphaltenic (SARA) components in crude oils by means of Infrared and Near Infrared spectroscopy”. Energy & Fuels, 15, 1304-1312.

Balabin, R.M., Safieva, R.Z. and Lomakina, E.L. (2010). “Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques”. Analytica Chimica Acta, 671(1- 2), 27-35

Beebe, K.R. and Kowalski, B.R. (1987). “An introduction to multivariate calibration and analysis”, Analytica Chimica Acta., 57(17):1007A–1017A.

Ben-Dor, E. and Banin, A. (1990). “Near-infrared reflectance analysis of carbonate concentration in soils”. Applied Spectroscopy, 44, 1064–1069

Brekke, C. and Solberg, A. (2005). “Oil spill detection by satellite remote sensing”. Remote Sensing of Environment, 95, 1-13.

Chakraborty, S., Weindorf, D. C., Zhu, Y., Li, B., Morgan, C. L. S., Ge, Y. and Galbraith, J. (2012). “Spectral reflectance variability from soil physicochemical properties in oil contaminated soils”. Geoderma, 177-178, 80–89.

Chung, H., H. Choi. & M. Ku. (1999). “Rapid identification of petroleum products by near-infrared spectroscopy”. Bulletin of the Korean Chemical Society 20, 1021–1025.

Clark, R.N., Swayze, G.A., Leifer, I., Livo, K.E., Lundeen, S., Eastwood, M., Green, R.O., Kokaly, R., Hoefen, T., Sarture, C., McCubbin, I., Roberts, D., Steele, D., Ryan, T., Dominguez, R., Pearson, N. and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Team, (2010). “A method for qualitative mapping of oil spills using imaging spectroscopy”. U.S. Geological Survey Open-File Report 2010–1101.

de Jong, S. (1993), ‘‘SIMPLS:An alternative approach to partial least squares regression’’. Chemometrics and Intelligent Laboratory Systems, 18: 251-263

Donovan T.J., Forgey R.L., Roberts A.A. (1979). “Aeromagnetic Detection of Diagenetic Magnetite over Oil Field”. The AAPG Bulletin, 63, 245-248.

Forina, M., Drava, G., Boggia, R., Lanteri, S., Conti, P., (1994). "Validation procedures in near-infrared spectrometry". Analytica Chimica Acta, 295(1):109-118

Ge, Y., Morgan, C. L. S., Thomasson, J. A. and Waiser, T. (2007). “A new perspective to near-infrared reflectance spectroscopy: a wavelet approach”, 50(1), 303–312.

Geladi, P. (2003). “Chemometrics in spectroscopy. Part 1. Classical Chemometrics”. Spectrochimica Acta Part B: Atomic Spectroscopy, 58 (5):767–782

Goetz, A. F. (2009). Three decades of hyperspectral remote sensing of the Earth: A personal view.

Remote Sensing of Environment, 113, S5-S16.

Hese, S. and Schmullius. C. (2009). “High spatial resolution images object classification for terrestrial oil spill contamination mapping in west Siberia”. International Journal of Applied Earth Observation and Geoinformation, 11, 130-141.

Hidajat, K and Chong, S.M. (2000). “Quality characterisation of crude oils by partial least square calibration of NIR spectra profiles”. Journal of Near Infrared Spectroscopy, 8, 53-59.

Hörig, B., Ku ¨hn, F., Oschu ¨ tz, F. and Lehmann, F., (2001). “HyMap hyperspectral remote sensing to detect hydrocarbons”. International Journal of Remote Sensing, 22 (8), 1413–1422.

Hunt, G.R. (1980). “Electromagnetic Radiation: The Communication Link in Remote Sensing”. In: Siegal, B.S.; Gillespie, A.R. Remote sensing in Geology. New York: J. Wiley & Sons. p. 5-45. Islam, K., Singh, B. and McBratney, A., (2003). “Simultaneous estimation of several soil properties by

ultra-violet, visible, and near infrared reflectance spectroscopy”. Australian Journal of Soil Research, 41, 1101–1114.

Jolliffe I.T. (2002). “Principal Component Analysis”, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 487 p.

Kokaly, R.F., Hoefen, T.M., Livo, E., Swayze, A.G., Leifer, I., McCubbin, I.B., Eastwood, M.L., Green, R.O., Lundeen, S.R., Sarture, C.M., Steele, D., Ryan, T., Bradley, E.S., Roberts, D.A. and the AVIRIS Team. (2010). “A rapid method for creating qualitative imagess indicative of thick oil emulsion on the ocean’s surface”. U. S. Geological Survey Open-File Report 2010-1107, 16p. Kruse, F. A. (2012). Mapping surface mineralogy using imaging spectrometry. Geomorphology,

137(1), 41-56.

Lammoglia, T. and Filho, C. R. de S. (2011). “Spectroscopic characterization of oils yielded from Brazilian offshore basins: Potential applications of remote sensing”. Remote Sensing of Environment, 115, 2525-2535.

Lyder, D. Feng, J., Rivard, B., Gallie, A. and Cloutis, E. (2010). “Remote bitumen content estimation of Athabasca oil sand from hyperspectral infrared reflectance spectra using Gaussian singlets and derivate of Gaussian wavelets”. FUEL, 89, 760-767.

Luz, E.R. (2003). “Predição de Propriedades de Gasolinas Usando Espectroscopia FTIR e Regressão por Mínimos Quadrados Parciais”. Dissertação (Mestrado)- PUC, Departamento de Química, Rio de Janeiro, 9-17.

Okparanma, R.N., Mouazen, A.M. (2013b). “Visible and near-infrared spectroscopy analysis of a polycyclic aromatic hydrocarbon in soils”. Scientific World Journal, 2013

Oliveira W,J. (1998). “Caracterização das Emanações Gasosas de Hidrocarbonetos na Região do Remanso do Fogo (MG), Através do Uso Integrado de Sensoriamento Remoto, Geoquímica, Geofísica, Geologia Estrutural e Espectrometria de Reflectância”. Dissertação de Doutorado, Instituto de Geociências, Universidade Estadual de Campinas-SP, 239 p.

Pearson, K. (1901). “On Lines and Planes of Closest Fit to Systems of Points in Space”. Philosophical Magazine, 2 (11):559–572.

Rivard, B., Lyder, D., Feng, J., Gallie, A., Cloutis, E., Dougan, P., Gonzalez, S., Cox, D. and Lipsett, M.G. (2010). “Bitumen content estimation of Athabasca oil sand from broad band infrared reflectance spectra”. The Canadian Journal of Chemical Engineering, 88, 830–838.

Salisbury J.W., Walter L.S., Vergo N. and D’Aria M. D. (1991). “Infrared (2.1-25um) spectra of minerals”. The John Hopkins University Press. Baltimore, Maryland, 267 pp.

Sanches, I.D., Souza Filho, C.R.; Magalhães, L.A.; Quitério, G.C.M., Alves, M.N.; Oliveira, W.J. . (2013a). “Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy”. Journal of Photogrammetry and Remote Sensing, 78, 85-101.

Sanches, I.D.; Souza Filho, C.R.; Magalhães, L.A.; Quitério, G.C.M.; Alves, M.N.; Oliveira, W.J. . (2013b). “Unravelling remote sensing signatures of plants contaminated with gasoline and diesel: An approach using the red edge spectral feature”. Environmental Pollution,174, 16-27.

Sanches, I. D. ;Souza Filho, C. R.; Kokaly, R. F. (2014) “Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680nm absorption feature with continuum removal”. Journal of Photogrammetry and Remote Sensing, 97, 111-122.

Saunders, D.F., Burson, K.R. & Thompson, C.K. (1999). “Model for Hydrocarbon Microseepage and Related Near-Surface Alterations”. AAPG Bulletin, 83(1), 170-185.

Schumacher, D. (2002). “Hydrocarbon-induced Alteration of Soils and Sediments” In D. Schumacher & M. A. Abrams (eds.) Hydrocarbon Migration and its Near Surface Expression. AAPG Memoir, 66, 71–89.

Schumacher, D. and Abrams, A. A., (1996). “Hydrocarbon Microseepage and its Near-Surface Expression”. AAPG Memoir, 66, ix.

van der Meer, F.D., van Dijk, P.M., Kroonenberg, S.B., Yang Hong and Lang, H. (2000). “Hyperspectral Hydrocarbon Microseepage Detection and Monitoring: Potentials and Limitations”. In Proceedings of the 2nd EARSEL workshop on imaging spectroscopy, Enschede, ITC, 9 p.

Vasques, G. M., Grunwald, S. and Sickman, J. O. (2009). “Modeling of soil organic carbon fractions using Visible–Near-Infrared spectroscopy”. Soil Science Society of America Journal, 73(1), 176 Viscarra Rossel, R.A. and Lark, R.M. (2009). “Improved analysis and modelling of soil VIS-NIR and

MIR-IR diffuse reflectance spectra using wavelets”. European Journal of Soil Science, 60, 453– 464.

Wetzel, D. L. (1983). “Near-infrared reflectance analysis”. Analytical Chemistry, 55(12), 1165A– 1176A.

Wold, H. (1966). “Estimation of principal components and related models by iterative least squares”. In P.R. Krishnaiaah (Ed.). Multivariate Analysis. New York: Academic Press, pp.391-420.

Zhaoxia, L. Na, J. and Li, L. (2011). “A two-stage registration algorithm for oil spill aerial image by invariants-based similarity and improved ICP”. International Journal of Remote Sensing, 32(13), 3649-3644.

Zwanziger, H. and Förste, H. (1998). “Near Infrared Spectroscopy of Fuel Contaminated Sand and Soil. I. Preliminary Results and Calibration Study”. Journal of Near Infrared Spectroscopy, 6:189-197.

Zomer, R. J., Trabucco, A. and Ustin, S. L. (2009). “Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing”. Journal of Environmental Management, 90(7), 2170–7.

Sensores e Técnicas de Sensoriamento Remoto para Identificação e