Vegetation classification method with biochemical composition estimated from remote sensing data

References

• AIG . 2002 . ACORN 4.0 User's Guide , Boulder , CO : Analytical Imaging and Geophysics LLC .
   Google Scholar
• Ankerst , M. , Elsen , C. , Ester , M. and Kriegel , H. Visual classification: an interactive approach to decision tree construction . Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 15–18 August 1999 , San Diego , CA . pp. 392 – 396 . New York : Association for Computing Machinery .
   Google Scholar
• Beck , R. 2003 . EO-1 User Guide—Version 2.3 , Cincinnati , OH : University of Cincinnati .
   Google Scholar
• Binagli , E. , Gallo , I. , Boschetti , M. and Brivio , P.A. 2005 . “ A neural adaptive algorithm for feature selection and classification of high dimensionality data ” . In ICIAP 2005, LNCS 3617 , Edited by: Roli , F. and Vitulano , S. 753 – 760 . Berlin : Springer-Verlag .
   Google Scholar
• Boschetti , M. , Boschetti , L. , Oliveri , S. and Casati , L. 2007 . Tree species mapping with airborne hyper-spectral MIVIS data: the Ticino Park study case . International Journal of Remote Sensing , 28 : 1251 – 1261 .
   Web of Science ®Google Scholar
• Brodley , C.E. and Utgoff , P.E. 1995 . Multivariate decision trees . Machine Learning , 19 : 45 – 77 .
   Web of Science ®Google Scholar
• Congalton , R.G. 1991 . A review of assessing the accuracy of classifications of remotely sensed data . Remote Sensing of Environment , 37 : 35 – 46 .
   Web of Science ®Google Scholar
• Congalton , R.G. and Green , K. 1999 . Assessing the Accuracy of Remotely Sensed Data: Principles and Practices , Boca Raton, FL : Lewis . Publishers
   Google Scholar
• Coops , N.C. , Smith , M.L. , Martin , M.E. and Ollinger , S.V. 2003 . Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing , 41 : 1338 – 1346 .
   Web of Science ®Google Scholar
• Curran , P.J. 1989 . Remote sensing of foliar chemistry . Remote Sensing of Environment , 30 : 271 – 278 .
   Web of Science ®Google Scholar
• Curran , P.J. , Dungan , J.L. and Peterson , D.L. 2001 . Estimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologies . Remote Sensing of Environment , 76 : 349 – 359 .
   Web of Science ®Google Scholar
• Datt , B. 1998 . Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a + b, and total carotenoid content in eucalyptus leaves . Remote Sensing of Environment , 66 : 111 – 121 .
   Web of Science ®Google Scholar
• De Jong , S.M. and Riezebos , H.T. Use of a GIS database as . Proceedings of the Second European Conference on Geographical Information Systems . 2–5 April 1991 , Brussels , Belgium . “a priori” knowledge in multispectral land cover classification , pp. 503 – 508 . Utrecht : EGIS Foundation .
   Google Scholar
• Duda , R.O. and Hart , P.E. 1973 . Pattern Classification and Scene Analysis , New York : John Wiley & Sons .
   Google Scholar
• Foody , G.M. 2009 . Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority . Remote Sensing of Environment , 113 : 1658 – 1663 .
   Web of Science ®Google Scholar
• Foody , G.M. and Arora , M.K. 1997 . An evaluation of some factors affecting the accuracy of classification by an artificial neural network . International Journal of Remote Sensing , 18 : 799 – 810 .
   Web of Science ®Google Scholar
• Fourty , T. and Baret , F. 1998 . On spectral estimates of fresh leaf biochemistry . International Journal of Remote Sensing , 19 : 1283 – 1297 .
   Web of Science ®Google Scholar
• Friedl , M.A. and Brodley , C.E. 1997 . Decision tree classification of land cover from remotely sensed data . Remote Sensing of Environment , 61 : 399 – 409 .
   Web of Science ®Google Scholar
• Gitelson , A.A. , Keydan , G.P. and Merzlyak , M.N. 2006 . Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves . Geophysical Research Letters , 33 : L11402 doi: 10.1029/2006GL026457
   Web of Science ®Google Scholar
• Gitelson , A.A. , Zur , Y. , Chivkunova , O.B. and Merzlyak , M.N. 2002 . Assessing carotenoid content in plant leaves with reflectance spectroscopy . Photochemistry and Photobiology , 75 : 272 – 281 .
   PubMed Web of Science ®Google Scholar
• Green , A.A. , Berman , M. , Switzer , P. and Craig , M.D. 1988 . A transformation for ordering multispectral data in terms of image quality with implications for noise removal . IEEE Transactions on Geoscience and Remote Sensing , 26 : 65 – 74 .
   Web of Science ®Google Scholar
• Green , R.O. , Eastwood , M.L. , Sarture , C.M. , Chrien , T.G. , Aronsson , M. , Chippendale , B.J. , Faust , J.A. , Pavri , B.E. , Chovit , C.J. , Solis , M. , Olah , M.R. and Williams , O. 1998 . Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS) . Remote Sensing of Environment , 65 : 227 – 248 .
   Web of Science ®Google Scholar
• Haboudane , D. , Miller , J.R. , Tremblay , N. , Zarco-Tejada , P.J. and Dextraze , L. 2002 . Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture . Remote Sensing of Environment , 81 : 416 – 426 .
   Web of Science ®Google Scholar
• Han , T. , Goodenough , D.G. , Dyk , A. and Love , J. Detection and correction of abnormal pixels in Hyperion images . Proceedings of IEEE International Geoscience and Remote Sensing Symposium . 24–28 June 2002 , Toronto, ON , Canada. pp. 1327 – 1330 . New York : IEEE Press .
   Google Scholar
• Heerman , P.D. and Khazenie , N. 1992 . Classification of multispectral remote sensing data using a back-propagation neural network . IEEE Transactions on Geoscience and Remote Sensing , 20 : 81 – 88 .
   Web of Science ®Google Scholar
• Hoffbeck , J.P. 1995 . Classification of high dimensional multispectral data , IN , , USA : PhD thesis, Purdue University, West Lafayette .
   Google Scholar
• Huang , Z. , Turner , B.J. , Dury , S.J. , Wallis , I.R. and Foley , W.J. 2004 . Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis . Remote Sensing of Environment , 93 : 18 – 29 .
   Web of Science ®Google Scholar
• Kandrika , S. and Roy , P.S. 2008 . Land use land cover classification of Orissa using multi-temporal IRS-P6 AWIFS data: a decision tree approach . International Journal of Applied Earth Observation and Geoinformation , 10 : 186 – 193 .
   Web of Science ®Google Scholar
• Kavzoglu , T. and Mather , P.M. 2002 . The role of feature selection in artificial neural network applications . International Journal of Remote Sensing , 23 : 2919 – 2937 .
   Web of Science ®Google Scholar
• Kempeneers , P. , Zarco-Tejada , P.J. , North , P.R.J. , De , B.S. , Delalieux , S. , Sepulcre-Canto , G. , Morales , F. , Van Aardt , J.A.N. , Sagardoy , R. , Coppin , P. and Scheunders , P. 2008 . Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery . International Journal of Remote Sensing , 29 : 5093 – 5111 .
   Web of Science ®Google Scholar
• Kokaly , R.F. , Asner , G.P. , Ollinger , S.V. , Martin , M.E. and Wessman , C.A. 2009 . Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies . Remote Sensing of Environment , 113 : S78 – S91 .
   Web of Science ®Google Scholar
• Martin , M.E. , Plourde , L.C. , Ollinger , S.V. , Smith , M.-L. and Mcneil , B.E. 2008 . A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems . Remote Sensing of Environment , 112 : 3511 – 3519 .
   Web of Science ®Google Scholar
• Myneni , R.B. , Maggion , S. and Laquinta , J. 1995 . Optical remote sensing of vegetation: modeling, caveats, and algorithms . Remote Sensing of Environment , 51 : 169 – 188 .
   Web of Science ®Google Scholar
• Nilsson , J.N. 1965 . Learning Machines: Foundations of Trainable Pattern-Classifying Systems , New York : McGraw-Hill .
   Google Scholar
• Ollinger , S.V. and Smith , M.L. 2005 . Net primary production and canopy nitrogen in a temperate forest landscape: an analysis using imaging spectroscopy, modeling and field data . Ecosystems , 8 : 760 – 778 .
   Web of Science ®Google Scholar
• Pacifici , F. , Chini , M. and Emery , W.J. 2009 . A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification . Remote Sensing of Environment , 113 : 1276 – 1292 .
   Web of Science ®Google Scholar
• Rogan , J. , Franklin , J. , Stow , D. , Miller , J. , Woodcock , C. and Roberts , D. 2008 . Mapping land-cover modifications over large areas: a comparison of machine learning algorithms . Remote Sensing of Environment , 112 : 2272 – 2283 .
   Web of Science ®Google Scholar
• Schaepman , S.G. , Schaepman , M.E. , Painter , T.H. , Dangel , S. and Martonchik , J.V. 2006 . Reflectance quantities in optical remote sensing—definitions and case studies . Remote Sensing of Environment , 103 : 27 – 42 .
   Web of Science ®Google Scholar
• Shaanxi Normal University . 1980 . Analyses Method in Common Use of Agricultural Chemistry , Xi'an : Technology Press of Shaanxi . [in Chinese]
   Google Scholar
• Skidmore , A.K. 1989 . An expert system classifies eucalypt forest types using Thematic Mapper data and a digital terrain model . Photogrammetric Engineering and Remote Sensing , 55 : 1449 – 1464 .
   Web of Science ®Google Scholar
• Smith , M.L. , Martin , M.E. , Plourde , L. and Ollinger , S.V. 2003 . Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor . IEEE Transactions on Geoscience and Remote Sensing , 41 : 1332 – 1333 .
   Web of Science ®Google Scholar
• Soil Science Society of China . 2000 . Analyze Method of Soil and Agricultural Chemistry , Beijing : Chinese Agricultural Press . [in Chinese]
   Google Scholar
• Stehman , S.V. 1997 . Selecting and interpreting measures of thematic classification accuracy . Remote Sensing of Environment , 62 : 77 – 89 .
   Web of Science ®Google Scholar
• Thenkabail , P.S. , Smith , R.B. and De Pauw , E. 2000 . Hyperspectral vegetation indices and their relationships with agricultural crop characteristics . Remote Sensing of Environment , 71 : 158 – 182 .
   Web of Science ®Google Scholar
• Tooke , T.R. , Coopes , N.C. , Goodwin , N.R. and Voogt , J.A. 2009 . Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications . Remote Sensing of Environment , 113 : 398 – 407 .
   Web of Science ®Google Scholar
• Townsend , P.A. , Foster , J.R. , Chastain , R.A. and Currie , W.S. 2003 . Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the Central Appalachian Mountains using Hyperion and AVIRIS . IEEE Transactions on Geoscience and Remote Sensing , 41 : 1347 – 1354 .
   Web of Science ®Google Scholar
• Tso , B. and Mather , M.P. 2001 . Classification Methods for Remotely Sensed Data , London : Taylor & Francis .
   Google Scholar
• Ulfarsson , M.O. , Benediktsson , J.A. and Sveinsson , J.R. 2003 . Data fusion and feature extraction in the wavelet domain . International Journal of Remote Sensing , 24 : 3933 – 3945 .
   Web of Science ®Google Scholar
• Ustin , S.L. , Gitelson , A.A. , Jacquemoud , S. , Schaepman , M. , Asner , G.P. , Gamon , J.A. and Zarco-Tejada , P. 2009 . Retrieval of foliar information about plant pigment systems from high resolution spectroscopy . Remote Sensing of Environment , 113 : S67 – S77 .
   Web of Science ®Google Scholar
• Vaiphasa , C. , Skidmore , A.K. , Boer , W.F. and Vaiphasa , T. 2007 . A hyperspectral band selector for plant species discrimination . ISPRS Journal of Photogrammetry & Remote Sensing , 62 : 225 – 235 .
   Web of Science ®Google Scholar
• Verstraete , M.M. , Pinty , B. and Myneni , R.B. 1996 . Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing . Remote Sensing of Environment , 58 : 201 – 214 .
   Web of Science ®Google Scholar
• Wu , C.Y. , Niu , Z. , Tang , Q. and Huang , W.J. 2008 . Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation . Agricultural and Forest Meteorology , 148 : 1230 – 1241 .
   Web of Science ®Google Scholar
• Zarco-Tejada , P.J. and Miller , J.R. 1999 . Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery . Journal of Geophysical Research , 104 : 27 921 – 27 933 .
   Web of Science ®Google Scholar
• Zhang , Y.Q. , Chen , J.M. , Miller , J.R. and Noland , T.L. 2008 . Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery . Remote Sensing of Environment , 112 : 3234 – 3247 .
   Web of Science ®Google Scholar
• Zou , Y. 1995 . Experimental Guidance to Plant Physiology and Biochemistry , Beijing : Chinese Agricultural Press . [in Chinese]
   Google Scholar