Detection and discrimination of various oil-contaminated soils using vegetation reflectance.
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Dominique Dubucq | Pierre Borderies | Guillaume Lassalle | Anthony Credoz | Rémy Hédacq | Georges Bertoni | Arnaud Elger | Sophie Fabre | Thierry Erudel | P. Borderies | S. Fabre | G. Lassalle | A. Credoz | D. Dubucq | A. Elger | R. Hédacq | Evelyne Buffan-Dubau | G. Bertoni | E. Buffan‐Dubau | Thierry Erudel
[1] N. Merkl,et al. Phytoremediation in the tropics--influence of heavy crude oil on root morphological characteristics of graminoids. , 2005, Environmental pollution.
[2] Carlos Roberto de Souza Filho,et al. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring , 2017 .
[3] Heiko Balzter,et al. Plant Family-Specific Impacts of Petroleum Pollution on Biodiversity and Leaf Chlorophyll Content in the Amazon Rainforest of Ecuador , 2017, PloS one.
[4] A. Adeniyi,et al. Determination of total petroleum hydrocarbons and heavy metals in soils within the vicinity of facilities handling refined petroleum products in Lagos metropolis. , 2002, Environment international.
[5] Roberta E. Martin,et al. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .
[6] George Alan Blackburn,et al. Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves , 2017 .
[7] Brent N. Holben,et al. Fraction images derived from NOAA AVHRR data for studying the deforestation in the Brazilian Amazon , 1994 .
[8] Susan L. Ustin,et al. Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Ju , 2005 .
[9] V. Geissen,et al. Growth of four tropical tree species in petroleum-contaminated soil and effects of crude oil contamination , 2016, Environmental Science and Pollution Research.
[10] Xinde Cao,et al. Accumulation of Pb, Cu, and Zn in native plants growing on a contaminated Florida site. , 2006, The Science of the total environment.
[11] Bogdan E. Popescu,et al. Gradient Directed Regularization for Linear Regression and Classi…cation , 2004 .
[12] D. Sims,et al. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .
[13] C. Field,et al. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .
[14] A. Elger,et al. Plant palatability can be inferred from a single-date feeding trial , 2004 .
[15] Travis E. Oliphant,et al. Python for Scientific Computing , 2007, Computing in Science & Engineering.
[16] Roy E. Welsch,et al. Detecting and Assessing Collinearity , 2005 .
[17] J. White,et al. Accumulation of weathered polycyclic aromatic hydrocarbons (PAHs) by plant and earthworm species. , 2006, Chemosphere.
[18] Yong-guan Zhu,et al. Uptake of selected PAHs from contaminated soils by rice seedlings (Oryza sativa) and influence of rhizosphere on PAH distribution. , 2008, Environmental pollution.
[19] G. A. Blackburn,et al. Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .
[20] A. Gitelson,et al. Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .
[21] S. Tarantola,et al. Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .
[22] Michel Barlaud,et al. Nonconvex Regularization in Remote Sensing , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[23] M M Nujkić,et al. Impact of metallurgical activities on the content of trace elements in the spatial soil and plant parts of Rubus fruticosus L. , 2016, Environmental science. Processes & impacts.
[24] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[25] Jay Gao,et al. Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges , 2018 .
[26] E. Hunt,et al. Estimating near-infrared leaf reflectance from leaf structural characteristics. , 2001, American journal of botany.
[27] M. Fingas,et al. Oil spill identification , 1999 .
[28] Kirk T. Semple,et al. Bioavailability of hydrophobic organic contaminants in soils: fundamental concepts and techniques for analysis , 2003 .
[29] K. Tansey,et al. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. , 2015, Environmental pollution.
[30] Zhongxin Chen,et al. Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance. , 2014, Marine pollution bulletin.
[31] H. Zou,et al. Addendum: Regularization and variable selection via the elastic net , 2005 .
[32] Harald van der Werff,et al. Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage , 2012, Ecol. Informatics.
[33] Moon S. Kim,et al. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .
[34] I. Jolliffe. Principal Component Analysis , 2002 .
[35] J. Flexas,et al. Photoprotection processes under water stress and recovery in Mediterranean plants with different growth forms and leaf habits , 2007 .
[36] M. Kendall,et al. The Problem of $m$ Rankings , 1939 .
[37] Skipper Seabold,et al. Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.
[38] Armando Apan,et al. Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .
[39] Dominique Dubucq,et al. Experimental study of hyperspectral responses of plants grown on mud pit soil , 2016, Remote Sensing.
[40] G. A. Blackburn,et al. Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .
[41] C. Poschenrieder,et al. Plant water relations as affected by heavy metal stress: A review , 1990 .
[42] Ivica Kisić,et al. The effect of drilling fluids and crude oil on some chemical characteristics of soil and crops , 2009 .
[43] S. Tao,et al. Polycyclic aromatic hydrocarbons (PAHs) in agricultural soil and vegetables from Tianjin. , 2004, The Science of the total environment.
[44] M. Konik,et al. Object-oriented approach to oil spill detection using ENVISAT ASAR images , 2016 .
[45] M. D. Steven,et al. Plant spectral responses to gas leaks and other stresses , 2005 .
[46] Bo Li,et al. The interactive effects of petroleum-hydrocarbon spillage and plant rhizosphere on concentrations and distribution of heavy metals in sediments in the Yellow River Delta, China. , 2010, Journal of hazardous materials.
[47] T. Jackson,et al. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery , 2008 .
[48] Mui Lay,et al. Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. , 2005, Environmental pollution.
[49] J. Hagemeyer. Ecophysiology of Plant Growth Under Heavy Metal Stress , 1999 .
[50] P. Legendre. Species associations: the Kendall coefficient of concordance revisited , 2005 .
[51] Dominique Dubucq,et al. Assessing Soil Contamination Due to Oil and Gas Production Using Vegetation Hyperspectral Reflectance. , 2018, Environmental science & technology.
[52] Carlos Cervantes,et al. Chromium toxicity in plants. , 2005, Environment international.
[53] G. Carter. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .
[54] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[55] A. Gitelson,et al. Remote estimation of chlorophyll content in higher plant leaves , 1997 .
[56] E. Dindar,et al. Variations of soil enzyme activities in petroleum-hydrocarbon contaminated soil , 2015 .
[57] John R. Miller,et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .
[58] G. Rondeaux,et al. Optimization of soil-adjusted vegetation indices , 1996 .
[59] Xavier Briottet,et al. Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements , 2017, Remote. Sens..
[60] B. Gimeno,et al. Interactive effects of ozone and drought stress on pigments and activities of antioxidative enzymes in Pinus halepensis , 2001 .
[61] W. Oechel,et al. Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species , 2002, Oecologia.
[62] M. Metwally,et al. Petroleum hydrocarbons and related heavy metals in the near-shore marine sediments of Kuwait , 1997 .
[63] Bin Li,et al. Rapid assessment of regional soil arsenic pollution risk via diffuse reflectance spectroscopy , 2017 .
[64] Damaris Zurell,et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .
[65] R. Congalton,et al. Accuracy assessment: a user's perspective , 1986 .
[66] Mashalah Khamehchiyan,et al. Effects of crude oil contamination on geotechnical properties of clayey and sandy soils , 2007 .
[67] Ieda Del'Arco Sanches,et al. Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy , 2013 .
[68] D. M. Moss,et al. Red edge spectral measurements from sugar maple leaves , 1993 .
[69] Harald van der Werff,et al. A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage , 2008, Sensors.
[70] S. Deka,et al. Effect of crude oil contamination on the chlorophyll content and morpho-anatomy of Cyperus brevifolius (Rottb.) Hassk , 2014, Environmental Science and Pollution Research.
[71] Carlos Roberto de Souza Filho,et al. A review on spectral processing methods for geological remote sensing , 2016, Int. J. Appl. Earth Obs. Geoinformation.
[72] Sébastien Angélliaume,et al. Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea , 2017, Sensors.
[73] Philippe Lagacherie,et al. Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements , 2008 .
[74] John R. Miller,et al. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..
[75] J. Dash,et al. Evaluation of the MERIS terrestrial chlorophyll index , 2004 .
[76] Edward J. Milton,et al. Review Article Principles of field spectroscopy , 1987 .
[77] Benoit Rivard,et al. Characterization of mineral substrates impregnated with crude oils using proximal infrared hyperspectral imaging , 2016 .
[78] Dominique Dubucq,et al. Hyperspectral signature analysis of three plant species to long-term hydrocarbon and heavy metal exposure , 2017, Remote Sensing.
[79] G. A. Blackburn,et al. Detection and discrimination of oil and water deficit-induced stress in maize (Zea mays L.) using spectral and thermal responses , 2013 .
[80] R. Pini,et al. Modifications of the structural characteristics of new soil forming on industrial waste colonized by woody plants , 2009 .
[81] I. D. Sanches,et al. Unravelling remote sensing signatures of plants contaminated with gasoline and diesel: an approach using the red edge spectral feature. , 2013, Environmental pollution.
[82] Hankui K. Zhang,et al. An extended PROSPECT: Advance in the leaf optical properties model separating total chlorophylls into chlorophyll a and b , 2017, Scientific Reports.
[83] Spectral determination of concentrations of functionally diverse pigments in increasingly complex arctic tundra canopies , 2016, Oecologia.
[84] Bin Hu,et al. Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression , 2015, IEEE Transactions on NanoBioscience.
[85] A. Savitzky,et al. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .
[86] I. Auby,et al. Toxicity effects of an environmental realistic herbicide mixture on the seagrass Zostera noltei. , 2017, Environmental pollution.
[87] S. Gibb,et al. Improved resolution of mono- and divinyl chlorophylls a and b and zeaxanthin and lutein in phytoplankton extracts using reverse phase C-8 HPLC , 1997 .
[88] D. Percival,et al. Gas exchange, stem water potential and leaf orientation of Rubus idaeus L. are influenced by drought stress , 1998 .
[89] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.
[90] J. Pereira,et al. Metabolic responses to water deficit in two Eucalyptus globulus clones with contrasting drought sensitivity. , 2006, Tree physiology.
[91] P. C. Nagajyoti,et al. Heavy metals, occurrence and toxicity for plants: a review , 2010 .
[92] Cathleen E. Jones,et al. State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill , 2012 .
[93] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[94] W. Oechel,et al. Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem , 2006 .
[95] Christopher B. Field,et al. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .
[96] Ming Xiao,et al. Plants' use of different nitrogen forms in response to crude oil contamination. , 2011, Environmental pollution.
[97] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[98] N. Garg,et al. Arsenic toxicity in crop plants: physiological effects and tolerance mechanisms , 2011 .
[99] Francine Heisel,et al. Detection of vegetation stress via a new high resolution fluorescence imaging system , 1996 .
[100] Njike Chigbu,et al. Comparative Analysis of Spectral Responses of Varied Plant Species to Oil Stress , 2013 .
[101] G. Logan,et al. Australian offshore natural hydrocarbon seepage studies, a review and re-evaluation , 2010 .
[102] Josep Peñuelas,et al. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .
[103] Huili Gong,et al. Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[104] C Bona,et al. Development of Canavalia ensiformis in soil contaminated with diesel oil , 2016, Environmental Science and Pollution Research.
[105] John R. Miller,et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .
[106] G. Asner. Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .
[107] H. Athar,et al. Influence of sub-lethal crude oil concentration on growth, water relations and photosynthetic capacity of maize (Zea mays L.) plants , 2016, Environmental Science and Pollution Research.
[108] George Alan Blackburn,et al. Early detection of oil-induced stress in crops using spectral and thermal responses , 2013 .