Artificial neural networks for mapping regional‐scale upland vegetation from high spatial resolution imagery

References

• Arora , M. K. and Foody , G. M. 1997 . Log‐linear modelling for the evaluation of the variables affecting the accuracy of probabilistic, fuzzy and neural network classifications. . International Journal of Remote Sensing , 18 : 785 – 798 .
   Web of Science ®Google Scholar
• Atkinson , P. M. and Curran , P. J. 1995 . Defining an optimal size of support for remote sensing investigations. . IEEE Transactions on Geosciences and Remote Sensing , 33 : 768 – 776 .
   Web of Science ®Google Scholar
• Atkinson , P. M. , Cutler , M. E. J. and Lewis , H. G. 1997 . Mapping sub‐pixel proportional land cover with AVHRR imagery. . International Journal of Remote Sensing , 18 : 917 – 935 .
   Web of Science ®Google Scholar
• Atkinson , P. M. and Tatnall , A. R. L. 1997 . Neural networks in remote sensing. . International Journal of Remote Sensing , 18 : 699 – 709 .
   Web of Science ®Google Scholar
• Backshall , J. , Manley , J. and Rebance , M. 2001 . The Upland Management Handbook, , 1 , Peterborough : English Nature .
   Google Scholar
• Bardgett , R. D. , Marsden , J. H. and Howard , D. C. 1995 . The extent and condition of heather on moorland in the uplands of England and Wales. . Biological Conservation , 71 : 155 – 161 .
   Web of Science ®Google Scholar
• Barrett , E. C. and Curtis , L. F. 1999 . Introduction to Environmental Remote Sensing, , 4 , Cheltenham : Stanley Thornes .
   Google Scholar
• Benediktsson , J. A. and Sveinsson , J. R. 1997 . “ Feature extraction for neural networks classifiers. ” . In Neurocomputation in Remote Sensing , Edited by: Kanellopoulos , I , Wilkinson , G. G , Roli , F and Austin , J . 97 – 104 . Berlin : Springer‐Verlag .
   Google Scholar
• Benediktsson , J. A. , Swain , P. H. and Ersoy , O. K. 1990 . Neural network approaches versus statistical methods in classification of multisource remote sensing data. . IEEE Transactions on Geosciences and Remote Sensing , 28 : 540 – 551 .
   Web of Science ®Google Scholar
• Benediktsson , J. A. , Swain , P. H. and Ersoy , O. K. 1993 . Conjugate‐gradient neural network in classification of multisource and very‐high‐dimensional remote sensing data. . International Journal of Remote Sensing , 14 : 1883 – 1903 .
   Google Scholar
• Bernard , A. C. 1998 . “ The identification of sub‐pixel components from remotely sensed data: an evaluation of an artificial neural network approach. ” . PhD thesis, University of Durham
   Google Scholar
• Bishop , C. M. 1995 . Neural Networks for Pattern Recognition, , 1 , Oxford : Clarendon Press .
   Google Scholar
• Boyd , D. S. , Phipps , P. C. and Foody , G. M. 2003 . “ Transferring remotely sensed predictors of drought events on Bornean rainforest. ” . In Scales and Dynamics in Observing the Environment Annual Conference of the Remote Sensing and Photogrammetry Society, 10–12 September 2003, Nottingham, UK
   Google Scholar
• Chavez , P. S. 1988 . An improved dark‐object subtraction technique for atmospheric scattering correction of multispectral data. . Remote Sensing of Environment , 24 : 459 – 479 .
   Web of Science ®Google Scholar
• Chavez , P. S. 1996 . Image‐based atmospheric corrections – revised and improved. . Photogrammetric Engineering and Remote Sensing , 62 : 1025 – 1036 .
   Web of Science ®Google Scholar
• Cherrill , A. J. and McClean , C. 1999 . The reliability of ‘phase1’ habitat mapping in the UK: the extent and types of observer bias. . Landscape and Urban Planning , 45 : 131 – 143 .
   Web of Science ®Google Scholar
• Cherrill , A. J. , McClean , C. , Lane , A. and Fuller , R. M. 1994 . A comparison of land cover types in an ecological field survey in northern England and a remotely sensed land cover map of Great Britain. . Biological Conservation , 71 : 313 – 323 .
   Google Scholar
• Curran , P. J. 1988 . The semivariogram in remote sensing: an introduction. . Remote Sensing of Environmen , 24 : 493 – 507 .
   Web of Science ®Google Scholar
• Duda , R. O. , Hart , P. E. and Stork , D. G. 2001 . Pattern Classification, , 2 , New York : John Wiley & Sons, Inc. .
   Google Scholar
• Dunne , R. A. and Campbell , N. A. 1994 . “ Some practical aspects of pruning multi‐layer perceptron models applied to remotely sensed data. ” . Technical Report 94/06, Murdoch University
   Google Scholar
• Egmont‐Petersen , M. , De Ridder , D. and Handels , H. 2002 . Image processing with neural networks – a review. . Pattern Recognition Letters , 35 : 2279 – 2301 .
   Google Scholar
• ERDP. 2000. England Rural Development Programme, North east ERDP chapter. Available online at: http://www.Maff.Gov.Uk/erdp/docs/nechapter/nesection12/ne121uplands.Htm (accessed 3 October 2000)
   Google Scholar
• Foody , G. M. 1992 . On the compensation for chance agreement in image classification accuracy assessment. . Photogrammetric Engineering and Remote Sensing , 58 : 1459 – 1460 .
   Web of Science ®Google Scholar
• Foody , G. M. 1995 . Cross‐entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. . ISPRS Journal of Photogrammetry & Remote Sensing , 50 : 2 – 12 .
   Web of Science ®Google Scholar
• Foody , G. M. 1999 . The significance of border training patterns in classification by a feedforward neural network using back propagation learning. . International Journal of Remote Sensing , 20 : 3549 – 3562 .
   Web of Science ®Google Scholar
• Foody , G. M. and Arora , M. K. 1996 . Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications. . Pattern Recognition Letters , 17 : 1389 – 1398 .
   Web of Science ®Google Scholar
• Foody , G. M. , Boyd , D. S. and Cutler , M. E. 2003 . Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. . Remote Sensing of Environment , 85 : 463 – 474 .
   Web of Science ®Google Scholar
• Foody , G. M. , Lucas , R. M. , Curran , P. J. and Honzak , M. 1997 . Non‐linear mixture modelling without end‐members using an artificial neural network. . International Journal of Remote Sensing , 18 : 937 – 953 .
   Web of Science ®Google Scholar
• Foody , G. M. , McCulloch , M. B. and Yates , W. B. 1995 . Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. . Photogrammetric Engineering and Remote Sensing , 61 : 391 – 401 .
   Web of Science ®Google Scholar
• Green , D. R. , Cummins , R. , Wright , R. and Miles , J. 1993 . “ A methodology for acquiring information on vegetation succession from remotely sensed imagery. ” . In Landscape Ecology and GIS , Edited by: Haines‐Young , R , Green , D. R and Cousins , S . 111 – 128 . London : Taylor & Francis .
   Google Scholar
• Gurney , K. 1997 . An Introduction to Neural Networks, , 1 , London, , UK : UCL Press Limited, Taylor & Francis Group .
   Google Scholar
• Hanley , H. B. and Fraser , C. S. 2001 . Geopositioning accuracy of Ikonos imagery: indications from 2d transformations. . The Photogrammetric Record , 17 : 317 – 329 .
   Web of Science ®Google Scholar
• Haykin , S. 1999 . Neural networks – a comprehensive foundation, , 2 , New Jersey : Prentice‐Hall, Inc. .
   Google Scholar
• Jones , A. R. and Wyatt , B. K. 1988 . “ Improved automated classification of upland environments utilizing high‐resolution satellite data. ” . In Ecological Change in the Uplands , Edited by: Usher , M. B and Thompson , D. B. A . 109 – 118 . Oxford : Special Publication Number 7 of the British Ecological Society .
   Google Scholar
• Kavzoglu , T. 2001 . “ An investigation of the design and use of feed forward artificial neural networks in the classification of remotely sensed images. ” . PhD thesis, The University of Nottingham
   Google Scholar
• Kavzoglu , T. and Mather , P. M. 1999 . Pruning artificial neural networks: an example using land cover classification of multi‐sensor images. . International Journal of Remote Sensing , 20 : 2787 – 2803 .
   Web of Science ®Google Scholar
• Klir , G. J. and Folger , T. A. 1988 . Fuzzy Sets, Uncertainty and Information, , 1 , State University of New York : Prentice‐Hall International, Inc. .
   Google Scholar
• Lees , B. G. 1996 . Neural network application in geosciences: an introduction. . Computers & Geosciences , 22 : 955 – 957 .
   Google Scholar
• Lewis , H. G. and Brown , M. 2001 . A generalized confusion matrix for assessing area estimates from remotely sensed data. . International Journal of Remote Sensing , 22 : 3223 – 3235 .
   Web of Science ®Google Scholar
• Lippmann , R. P. 1987 . An introduction to computing with neural nets. . IEEE ASSP Magazine , : 4 – 22 . April
   Google Scholar
• Mather , P. M. 1999 . Computer Processing of Remotely‐sensed Images, , 2 , Chichester : John Wiley & Sons Ltd .
   Google Scholar
• Mehner , H. , Cutler , M. E. J. and Fairbairn , D. 2003 . “ Issues concerning the transferability of artificial neural networks. ” . In Scales and Dynamics in Observing the Environment Annual Conference of the Remote Sensing and Photogrammetry Society, 10–12 September 2003, Nottingham, UK
   Google Scholar
• Mehner , H. , Fairbairn , D. and Cutler , M. E. J. 2004a . “ Defining the optimal spatial resolution for mapping upland vegetation. ” . In Annual Meeting of the Remote Sensing and Photogrammetric Society (RSPSoc), UK Aberdeen
   Google Scholar
• Mehner , H. , Cutler , M. E. J. , Fairbairn , D. and Thompson , G. 2004b . Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors. . Global Ecology & Biogeography , 13 : 359 – 369 .
   Web of Science ®Google Scholar
• Miller , D. M. , Kaminsky , E. J. and Rana , S. 1995 . Neural network classification of remote‐sensing data. . Computers & Geosciences , 21 : 377 – 386 .
   Web of Science ®Google Scholar
• Openshaw , S. and Openshaw , C. 1997 . Artificial Intelligence in Geography, , 1 , Chichester, , UK : John Wiley & Sons .
   Google Scholar
• Paola , J. D. and Schowengerdt , R. A. 1995 . A detailed comparison of backpropagation neural network and maximum‐likelihood classifiers for urban land use classification. . IEEE Transactions on Geosciences and Remote Sensing , 33 : 981 – 996 .
   Web of Science ®Google Scholar
• Ray TW. 1994. A FAQ on vegetation in remote sensing. Available online at: http://www.Shef.Ac.Uk/ ∼bryant/6370p3/indices.Html (accessed 7 February 2001)
   Google Scholar
• RSPB. 2000. Royal Society for the Protection of Birds. Available online at: http://www.Rspb.Org.Uk/wildlife/default.Asp (accessed 19 February 2002)
   Google Scholar
• Slater , J. and Brown , R. 2000 . Changing landscapes: monitoring environmentally sensitive areas using satellite imagery. . International Journal of Remote Sensing , 21 : 2753 – 2767 .
   Web of Science ®Google Scholar
• Tallis , J. H. 1985 . “ Erosion of blanket peat in the southern Pennines: new light on an old problem. ” . In The Geomorphology of North‐West England , Edited by: Johnson , R. H . 313 – 336 . Manchester : Manchester University Press .
   Google Scholar
• Taylor , J. C. , Bird , A. C. and Keech , M. A. 1991 . “ Landscape changes in the national parks of England, application of satellite remote sensing. ” . Report 14, Silsoe College, Bedford, UK
   Google Scholar
• UKBiodiversity. 2002. UK Biodiversity Group. Available online at: http://www.Ukbap.Org.Uk (accessed 13 April 2002)
   Google Scholar
• Wilkinson , G. G. 1997 . “ Open question in neurocomputing for earth observation. ” . In Neurocomputation in Remote Sensing , Edited by: Kanellopoulos , I , Wilkinson , G. G , Roli , F and Austin , J . 3 – 13 . Berlin : Springer‐Verlag .
   Google Scholar
• Woodcock , C. E. , Macomber , S. A. , Pax‐Lenney , M. and Cohen , W. B. 2001 . Monitoring large areas for forest using Landsat: generalization across space, time and Landsat sensors. . Remote Sensing of Environment , 78 : 194 – 203 .
   Web of Science ®Google Scholar