Multi-method ensemble selection of spectral bands related to leaf biochemistry

[1]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[2]  Luis Alonso,et al.  Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset. , 2014, Journal of photochemistry and photobiology. B, Biology.

[3]  Jean-Baptiste Féret,et al.  Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis , 2014 .

[4]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[5]  C. Ginzler,et al.  Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution , 2013 .

[6]  W. Verhoef,et al.  Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression , 2013 .

[7]  David Riaño,et al.  Estimating canopy water content from spectroscopy , 2012 .

[8]  Peijun Du,et al.  Hyperspectral remote sensing image classification based on the integration of support vector machine and random forest , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[9]  S. Schmidtlein,et al.  Mapping plant strategy types using remote sensing , 2012 .

[10]  Wei Zhang,et al.  Multiple Classifier System for Remote Sensing Image Classification: A Review , 2012, Sensors.

[11]  Roberta E. Martin,et al.  Spectroscopy of canopy chemicals in humid tropical forests , 2011 .

[12]  A. Skidmore,et al.  Mapping grassland leaf area index with airborne hyperspectral imagery : a comparison study of statistical approaches and inversion of radiative transfer models , 2011 .

[13]  L. Buydens,et al.  Opening the kernel of kernel partial least squares and support vector machines. , 2011, Analytica chimica acta.

[14]  K. Barry,et al.  Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling , 2011 .

[15]  Gregory P Asner,et al.  Canopy phylogenetic, chemical and spectral assembly in a lowland Amazonian forest. , 2011, The New phytologist.

[16]  J. Evans,et al.  Modeling Species Distribution and Change Using Random Forest , 2011 .

[17]  Roberta E. Martin,et al.  Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests. , 2011, Ecological applications : a publication of the Ecological Society of America.

[18]  S. Ollinger.  Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[19]  Zhang-hua Lou,et al.  A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands , 2011, Precision Agriculture.

[20]  Rasmus Bro,et al.  Variable selection in regression—a tutorial , 2010 .

[21]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

[22]  Roberta E. Martin,et al.  Brightness-normalized Partial Least Squares Regression for hyperspectral data , 2010 .

[23]  L. Buydens,et al.  Visualization and recovery of the (bio)chemical interesting variables in data analysis with support vector machine classification. , 2010, Analytical chemistry.

[24]  Michael E. Schaepman,et al.  Estimating canopy water content using hyperspectral remote sensing data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[25]  A. K. Skidmore,et al.  Nitrogen prediction in grasses: effect of bandwidth and plant material state on absorption feature selection , 2010 .

[26]  Clement Atzberger,et al.  Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[27]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[28]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[29]  Bjoern H. Menze,et al.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data , 2009, BMC Bioinformatics.

[30]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[31]  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.

[32]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[33]  George Alan Blackburn,et al.  Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. , 2008 .

[34]  Björn Waske,et al.  Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[35]  K. Soudani,et al.  Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .

[36]  Lin Li,et al.  Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression , 2008 .

[37]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[38]  Xueguang Shao,et al.  Removing uncertain variables based on ensemble partial least squares. , 2007, Analytica chimica acta.

[39]  L. Buydens,et al.  Visualisation and interpretation of Support Vector Regression models. , 2007, Analytica chimica acta.

[40]  Jon Atli Benediktsson,et al.  Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments , 2007, MCS.

[41]  Giles M. Foody,et al.  Mapping a specific class with an ensemble of classifiers , 2007 .

[42]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[43]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[44]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[45]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[46]  Jon Atli Benediktsson,et al.  Decision Fusion for the Classification of Urban Remote Sensing Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[47]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[48]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[49]  M. Fortin,et al.  Spatial Analysis: A Guide for Ecologists 1st edition , 2005 .

[50]  S. Lanteri,et al.  Selection of useful predictors in multivariate calibration , 2004, Analytical and bioanalytical chemistry.

[51]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[52]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[53]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[54]  A. Gitelson,et al.  Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit , 2003 .

[55]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[56]  Paul C. Smits,et al.  Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection , 2002, IEEE Trans. Geosci. Remote. Sens..

[57]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[58]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[59]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[60]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[61]  Lalit Kumar,et al.  Imaging Spectrometry and Vegetation Science , 2001 .

[62]  H. Martens,et al.  Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR) , 2000 .

[63]  Fuad Rahman,et al.  Serial Combination of Multiple Experts: A Unified Evaluation , 1999, Pattern Analysis & Applications.

[64]  J. Aber,et al.  Leaf Chemistry, 1992-1993 (ACCP) , 1999 .

[65]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[66]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[67]  B. Datt.  Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves , 1998 .

[68]  G. Asner.  Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[69]  G. A. Blackburn,et al.  Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .

[70]  L. Johnson,et al.  Spectrometric Estimation of Total Nitrogen Concentration in Douglas-Fir Foliage , 1996 .

[71]  S. Ustin,et al.  Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data , 1996 .

[72]  A. Gitelson,et al.  Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements , 1996 .

[73]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[74]  Gregory A. Carter,et al.  Responses of leaf spectral reflectance to plant stress. , 1993 .

[75]  F. M. Danson,et al.  High-spectral resolution data for determining leaf water content , 1992 .

[76]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[77]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[78]  D. H. Card,et al.  Remote sensing of forest canopy and leaf biochemical contents , 1988 .

[79]  R. R. Hocking.  The analysis and selection of variables in linear regression , 1976 .

[80]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .