Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties.
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Dominique Dubucq | Guillaume Lassalle | Anthony Credoz | Rémy Hédacq | Georges Bertoni | Arnaud Elger | Sophie Fabre | S. Fabre | G. Lassalle | A. Credoz | D. Dubucq | A. Elger | R. Hédacq | G. Bertoni
[1] P. Alam. ‘G’ , 2021, Composites Engineering: An A–Z Guide.
[2] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[3] P. Alam. ‘S’ , 2021, Composites Engineering: An A–Z Guide.
[4] 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.
[5] Wolfram Mauser,et al. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study , 2018, Remote. Sens..
[6] I. Auby,et al. Toxicity effects of an environmental realistic herbicide mixture on the seagrass Zostera noltei. , 2017, Environmental pollution.
[7] Ieda Del'Arco Sanches,et al. Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy , 2013 .
[8] Guofeng Wu,et al. Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants. , 2014, Environmental science & technology.
[9] Carlos Roberto de Souza Filho,et al. Spectroscopic characterization of red latosols contaminated by petroleum-hydrocarbon and empirical model to estimate pollutant content and type , 2016 .
[10] N. Merkl,et al. Phytoremediation in the Tropics—The Effect of Crude Oil on the Growth of Tropical Plants , 2004 .
[11] Dominique Dubucq,et al. Experimental study of hyperspectral responses of plants grown on mud pit soil , 2016, Remote Sensing.
[12] G. A. Blackburn,et al. Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .
[13] Benoit Rivard,et al. Characterization of mineral substrates impregnated with crude oils using proximal infrared hyperspectral imaging , 2016 .
[14] 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.
[15] Dominique Dubucq,et al. Hyperspectral signature analysis of three plant species to long-term hydrocarbon and heavy metal exposure , 2017, Remote Sensing.
[16] 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.
[17] Liangpei Zhang,et al. An Adaptive Differential Evolution Endmember Extraction Algorithm for Hyperspectral Remote Sensing Imagery , 2014, IEEE Geoscience and Remote Sensing Letters.
[18] Weimin Ju,et al. Improving the PROSPECT Model to Consider Anisotropic Scattering of Leaf Internal Materials and Its Use for Retrieving Leaf Biomass in Fresh Leaves , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[19] Susan L. Ustin,et al. Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact , 2018, Sensors.
[20] Guofeng Wu,et al. Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. , 2016, Journal of hazardous materials.
[21] Miss A.O. Penney. (b) , 1974, The New Yale Book of Quotations.
[22] J. Fletcher. Distribution , 2009, BMJ : British Medical Journal.
[23] 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 .
[24] Wei Gong,et al. Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval , 2018 .
[25] Y. Perrodin,et al. Ecological risk assessment of urban and industrial systems: a review. , 2011, The Science of the total environment.
[26] C Bona,et al. Development of Canavalia ensiformis in soil contaminated with diesel oil , 2016, Environmental Science and Pollution Research.
[27] Roberta E. Martin,et al. Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden , 2007, Oecologia.
[28] E. Hunt,et al. Estimating near-infrared leaf reflectance from leaf structural characteristics. , 2001, American journal of botany.
[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] Harald van der Werff,et al. Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage , 2012, Ecol. Informatics.
[32] E. Nemeth,et al. Physiological and molecular responses to heavy metal stresses suggest different detoxification mechanism of Populus deltoides and P. x canadensis. , 2016, Journal of plant physiology.
[33] Dawei Liu,et al. Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China , 2018, Remote. Sens..
[34] Margaret Kalacska,et al. Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments , 2009 .
[35] Xavier Briottet,et al. Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements , 2017, Remote. Sens..
[36] W. Oechel,et al. Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species , 2002, Oecologia.
[37] Njike Chigbu,et al. Comparative Analysis of Spectral Responses of Varied Plant Species to Oil Stress , 2013 .
[38] Hidetoshi Asai,et al. Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar , 2017, Remote. Sens..
[39] 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.
[40] George Alan Blackburn,et al. Early detection of oil-induced stress in crops using spectral and thermal responses , 2013 .
[41] Jason Levy,et al. Advances in Remote Sensing for Oil Spill Disaster Management: State-of-the-Art Sensors Technology for Oil Spill Surveillance , 2008, Sensors.
[42] A. Skidmore,et al. Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves , 2015 .
[43] L. A. Stone,et al. Computer Aided Design of Experiments , 1969 .
[44] K. Tansey,et al. Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest , 2017, Environmental Earth Sciences.
[45] Stéphane Jacquemoud,et al. PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .
[46] 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.
[47] A. Skidmore,et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .
[48] Hamad Karki,et al. Application of robotics in onshore oil and gas industry - A review Part I , 2016, Robotics Auton. Syst..
[49] F. Baret,et al. PROSPECT: A model of leaf optical properties spectra , 1990 .
[50] P. Harvey,et al. Scanning electron microscopic investigations of root structural modifications arising from growth in crude oil-contaminated sand , 2014, Environmental Science and Pollution Research.
[51] George Kvesitadze...,et al. Biochemical Mechanisms of Detoxification in Higher Plants: Basis of Phytoremediation , 2006 .
[52] Mui Lay,et al. Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. , 2005, Environmental pollution.
[53] Rei Sonobe,et al. Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments , 2018, Biosystems Engineering.
[54] Roberta E. Martin,et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .
[55] V. Ochoa-Herrera,et al. Distribution, contents and health risk assessment of metal(loid)s in small-scale farms in the Ecuadorian Amazon: An insight into impacts of oil activities. , 2018, The Science of the total environment.
[56] Su Zhang,et al. A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents , 2017, Remote. Sens..
[57] N. Goel,et al. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies , 2004 .
[58] David W. Lee,et al. Why leaves turn red in autumn. The role of anthocyanins in senescing leaves of red-osier dogwood. , 2001, Plant physiology.
[59] 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.
[60] Simcha Lev-Yadun,et al. Unravelling the evolution of autumn colours: an interdisciplinary approach. , 2009, Trends in ecology & evolution.
[61] F. M. Danson,et al. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .
[62] Ying Li,et al. Factors Influencing Leaf Chlorophyll Content in Natural Forests at the Biome Scale , 2018, Front. Ecol. Evol..
[63] D. Sims,et al. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .
[64] Dominique Dubucq,et al. Detection and discrimination of various oil-contaminated soils using vegetation reflectance. , 2019, The Science of the total environment.
[65] S. Khalid,et al. Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. , 2017, Journal of hazardous materials.
[66] Quan Wang,et al. Retrieval of Leaf Biochemical Parameters Using PROSPECT Inversion: A New Approach for Alleviating Ill-Posed Problems , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[67] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[68] Dominique Dubucq,et al. Assessing Soil Contamination Due to Oil and Gas Production Using Vegetation Hyperspectral Reflectance. , 2018, Environmental science & technology.
[69] A. Gitelson,et al. Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .
[70] Benoit Rivard,et al. Foliar spectral properties following leaf clipping and implications for handling techniques , 2006 .
[71] Ponnuthurai N. Suganthan,et al. Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..
[72] Xiao Zhang,et al. The effects of petroleum-contaminated soil on photosynthesis of Amorpha fruticosa seedlings , 2016, International Journal of Environmental Science and Technology.
[73] Jun-sheng Li,et al. Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China , 2013, PloS one.
[74] Rosa Elvira Correa Pabón,et al. Reflectance and imaging spectroscopy applied to detection of petroleum hydrocarbon pollution in bare soils. , 2019, The Science of the total environment.
[75] Valérie Demarez,et al. Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model , 1999 .
[76] Michael E. Schaepman,et al. Using spectral information from the NIR water absorption features for the retrieval of canopy water content , 2008, Int. J. Appl. Earth Obs. Geoinformation.
[77] Ming Xiao,et al. Plants' use of different nitrogen forms in response to crude oil contamination. , 2011, Environmental pollution.
[78] Frédéric Baret,et al. Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model , 2018, Plant Methods.
[79] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[80] M. MacKinnon,et al. Growth and Physiological Responses of Triticum aestivum and Deschampsia caespitosa Exposed to Petroleum Coke , 2011 .
[81] Ivica Kisić,et al. The effect of drilling fluids and crude oil on some chemical characteristics of soil and crops , 2009 .
[82] T. Jackson,et al. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery , 2008 .
[83] Ashish Ghosh,et al. Self-adaptive differential evolution for feature selection in hyperspectral image data , 2013, Appl. Soft Comput..
[84] G. Carter,et al. Variability in leaf optical properties among 26 species from a broad range of habitats. , 1998, American journal of botany.
[85] Jean-Yves Tourneret,et al. Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery , 2012, IEEE Transactions on Image Processing.
[86] Shantanu Datta,et al. A review on different pipeline fault detection methods , 2016 .
[87] Serge Rambal,et al. Exploring the relationships between reflectance and anatomical and biochemical properties in Quercus ilex leaves , 1999 .