Neural networks, multitemporal landsat thematic mapper data and topographic data to classify forest damages in the Czech Republic

Canadian Journal of Remote Sensing

Volumn 23, Issue 3, 1997, Pages 217-229

  Ardö, Jonas ^a   Pilesjö, Petter ^a   Skidmore, Andrew ^b  

^a LUND UNIVERSITY   (Sweden)

^b ITC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; LEARNING SYSTEMS; REGRESSION ANALYSIS; REMOTE SENSING;

BAYESIAN CLASSIFIERS; FOREST DAMAGE; LANDSAT THEMATIC MAPPER; LOGISTIC REGRESSION;

NEURAL NETWORKS;

EID: 0031221032     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.1080/07038992.1997.10855204     Document Type: Article

References (53)

1. Aldrich, J. H. and Nelson, F. D., 1984. Linear probability, Logit, and probit models. Newbury Park, CA, Sage Publications.
2. Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, R. E., 1976. “A land use and land cover classification system for use with remote sensor data”, U. S. G. S, Washington U S Geological Service Professional Paper 964.
3. Ard S. J. 1994. “Coniferous forest damage assessment in the Krusne Hory, the Czech Republic using Landsat Thematic Mapper reflectance and logit regressions”, 7th Australasian Remote Sensing Conference. Proceedings'. Poster and Post-Deadline. Melbourne, March 1994. pp 171-178.
4. Ard S. J., Lambert, N., Henzlik, V. and Rock, B.N. 1997. “Satellite-based Estimations of coniferous forest cover changes: Krusne Hory, Czech Republic 1972 - 1989”, Ambio. Vol. 26, pp. 158-166.
5. Beale, R. and Jackson, T., 1991. Neural Computing: an introduction. Adam Hilger, Bristol, UK, 240 p.
6. 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 Geoscience and Remote Sensing, Vol 28, pp. 540-551
7. Benediktsson, J. A., Sveinsson, J. R. and Arnason, K., 1995. “Classification and Feature Extraction of AVRIS data", IEEE Transactions on Geoscience and Remote Sensing, Vol 33, pp. 1194-1205
8. Bischof, H., Schneider, W. and Pinz, A. J., 1992. “Multispectral classification of Landsat-images using neural networks”, IEEE Transactions on Geoscience and Remote Sensing Vol. 30, pp. 482-490.
9. Brockhaus, J. A., Khorram, S., Bruck, R. I., Campbell, M. V. and Stallings, C., 1992. “A comparison of Landsat TM and SPOT HRV data for use in the development of forest defoliation models”, International Journal of Remote Sensing, Vol 13, pp. 3235-3240.
10. Carter, F. W., 1985. “Pollution problems in post war Czechoslovakia”, Trans. Inst. Br. Geogr. N.S. 10, 17-44.
11. Civco, D. L., 1993. “Artificial neural networks for land cover classification and mapping”, International Journal of Geographical Information Systems Vol. 7, pp. 173-186.
12. Congalton, R. G., 1991. “A review of assessing the accuracy of classifications of remotely sensed data”, Remote Sensing of Environment, Vol. 37, pp. 36-46
13. Ekstrand, S., 1993 Assessment of forest damage using Landsat TM, elevation models and digital forest maps. Ph. D. thesis, Royal Institute of Technology, Stockholm, Sweden.
14. Ekstrand, S., 1994. “Assessment of forest damage with Landsat TM: Correction for varying forest stand Characteristics”; Remote Sensing of Environment, Vol. 47, pp. 291-302
15. Epema, G.F., 1990. “Determination of planetary reflectance for Landsat-5 Thematic-Mapper tapes processed by Earthnet (Italy)”, ESA Journal Vol. 14, pp. 101-108.
16. Fitzgerald, R. W. and Lees, B.G. 1992. “The application of neural networks to the floristic classification of remote sensing and GIS data in complex terrain”, In: Proceedings of the XVIIISPRS Congress in Washington, U.S. A., August 2 -14, 1992. American Society for Photogrammetry and Remote Sensing.
17. Foody, G. M., McCulloch, M. B. and Yates, W. B., 1995. “The effect of training set size and composition on artificial neural network classification”, International Journal of Remote Sensing, Vol. 16, pp. 1707-1723.
18. Forsyth, R., 1984. Expert Systems: Principles and Case Studies. Chapman and Hall, London.
19. Greene, W. H., 1990. Econometric analysis. MacMillan: New York.
20. Gould, P., 1982. “The tyranny of taxonomy”, The Sciences, Vol. May/June 1982, p. 7-9.
21. Halounovä, L., 1994. “Comparison of neural network and maximum likelihood classification in an urban area”, Proceedings of the EARSeL 14th Symposium, Sensors and Environmental applications. Göteborg, Sweden, 6-8 June, pp. 463-468.
22. Hepner, G.F., Logan, T., Rittner, N. and Bryant, N., 1989. “Artificial neural network classification using a minimal training set: comparison to conventional supervised classification”, Photogrammetric Engineering And Remote Sensing, Vol. 56, pp. 469-473.
23. Hoffer, R.M., Fleming, M.D., Bartolucci, L. A., Davis, S.M., and Nelson, R.F., 1979, Digital processing of Landsat MSS and topographic data to improve capabilities for computerized mapping of forest cover types. Laboratory of Remote Sensing, Technical Note 011579, Purdue University, West Lafayette, Indiana 47907. 169 p.
24. Kanellopoulos, I., Varfis, G., Wilkinson, G. G. and Megeir, J., 1992. “Land-cover discrimination in SPOT HRV imagery using an artificial neural network - a 20 class experiment”, International Journal of Remote Sensing, Vol. 13, pp.917-924.
25. Klasterska, L, 1991. “Bohemian problem bared”, ACID NEWS March 1991, pp. 1-4.
26. Klimont, Z., Amann, M., Cofala, J., Gyarfas, Klaassen, G. and Schöpp, W, 1993. Emission of Air pollutants in the region of the central European Iniative-1988. IIASA, Laxenburg, Austria, SR-9303.
27. Kubelka, L., Karasek, A., Rybar, V, Baldalik, V, and Slodicak, M. 1993. Forest regeneration in the heavily polluted NE Kruänd Hory Mountains. Czech Ministry of Agriculture, Prague.
28. Kubikova, J., 1991. “Forest dieback in Czechoslovakia”, Vegetatio, Vol. 93, pp. 101-108.
29. Lambert, N. J., Ardö, J., Rock, B.N. and Vogelmann, J.E. 1995. “Spectral Characterization and regression based estimates of forest damage in Norway Spruce stands in the Czech Republic using Landsat Thematic Mapper data”, International Journal of Remote Sensing, Vol. 16, pp. 1261-1287
30. Lippman, R. R, 1987. “An introduction to Computing with Neural Nets”, IEEE ASSP Magazine April 1987, pp 4-22.
31. Lochman, V, 1986. “The contemporary state of forest soils in the Ore Mountains”, Prace Vulhm, VOL. 68, pp. 9-48. (In Czech, summary in English).
32. Matema, J., 1985. “Results of the research into air pollutants impact on forests in Czechoslovakia”. In Symposium on the effects of air pollution on forest and water ecosystems, pp. 127-138. The foundation for research of natural resources in Finland, Helsinki.
33. Parikh, J. A., DaPonte, J. S., Damodaran, M., Karageorgiou, A. and Podaras, P., 1991. “Comparison of backpropagation neural networks and statistical techniques for analysis of geological features in Landsat imagery”. SPIE - Application of neural network II 1469. pp. 526-538.
34. Pechala, F. and Böhme, W., (eds.) 1975. Podnebi a Pocasi V Krusnych Horach (Climate of Krusnd hory). Czech Hydrometeorological Institute, Prague, p. 106. (In Czech, Summary in English).
35. Peddle, D. R., Foody, G. M., Zhang, A., Franklin, S. and Ellsworth, F. L., 1994. “Multi-source image classification II: an empirical comparison of evidential reasoning and neural network approaches”, Canadian Journal of Remote Sensing, Vol. 20, pp. 396-407.
36. Pokomy, R, 1984. “To the problem of forest soils acidification in mountain regions of the CSR (Czech Socialist Republic)”. In: Proceedings: Air pollution and stability of coniferous forest ecosystems. Klimo, E. and Saly, R. (eds.). University of Agriculture, Bmo, p. 81-92.
37. Prusa, E. 1990. Natural forest of the Czech Republic. Czech Ministry of Agriculture, Prague. (In Czech with English summary). 243 p.
38. Ritter, H., Martinetz, T. and Schulten, K, 1991. Neural computing and SelfOrganizing Maps. Addison-Wesley: Reading Massachusetts.
39. Rock, B. N., Hoshizaki, T. and Miller, J. R., 1988. “Comparison of In Situ and Airborne Spectral Measurements of the Blue Shift Associated with Forest decline”, Remote Sensing of Environment, Vol. 24, pp. 109-127.
40. Rumelhart D. E. and McClelland, J. L.,1986. Parallel Distributed Processing, Volume 1. MIT Bradford Press.
41. Schlyter, P. 1993. Operational aerial forest damage surveys. Environmental and energy systems studies, Licenciate thesis, Lund University, Lund.
42. Shapiro, S. and Wtlk, M. B., 1965. “An analysis of variance test for normality”, Biometrika, Vol. 52, pp. 591-611.
43. Skidmore, A. K., W. Brinkhof and J. Delaney, 1994. “Using neural networks to analyse spatial data”. 7th Australasian Remote Sensing Conference Proceedings'. Poster and Post-Deadline, Melbourne, March 1994, pp. 235-246.
44. Skidmore, A.K., Forbes, G.W., Carpenter, D. J., 1988. “Non-parametric test of overlap in multispectral classification”, International Journal of GIS, Vol. 9, pp. 777-785.
45. Skidmore, A.K., 1989. “An expert system classifies eucalypt forest types using Landsat Thematic Mapper data and a digital terrain model”, Photogrammetric Engineering and Remote Sensing Vol. 55, pp. 1449-1464.
46. Skidmore, A.K., B.J. Turner, W. Brinkhof and E. Knowles. 1997. “Performance of neural networks: Mapping forests using GIS and remotely sensed data”, Photogrammetric Engineering and Remote Sensing, Vol. LXIII, pp. 501-514.
47. Srinivasan, A. and Richards, J. A., 1990. “Knowledge-based techniques for multi-source classification”, International Journal of Remote Sensing, Vol. 11, pp. 505-525.
48. Strahler, A. H., Estes, J. E., Maynard, P. F., Mertz, F. C., and Stow, D. A., 1980. “Incorporating collateral data in Landsat classification and modelling procedures”. Proceedings of the Nth International Symposium on Remote Sensing of Environment, San Jose, Costa Rica, Vol. 2, pp. 1009-1026.
49. Sui, D. Z., 1994. “Recent applications of neural networks for spatial data handling”, Canadian Journal of Remote Sensing, Vol. 20, pp. 368-380.
50. Svoboda, J., (ed.) 1966. Regional Geology of Czechoslovakia. Czech Geological Survey, Publishing House of the Czechoslovak Academy of Sciences, Prague, 668 p.
51. Vogelmann, J. E., 1988, “Detection of forest change in the Green mountains of Vermont using Multispectral Scanner data”, International Journal of Remote Sensing, Vol. 9, pp. 1187-1200.
52. Williams, R.B.G. 1984. Introduction to statistics for Geographers and Earth scientists. MACMILLAN LTD, London.
53. Yoshida, T. And Omatu, S., 1994. “Neural Network Approach to Land Cover Mapping”, IEEE TYansactions on Geoscience and Remote Rensing, Vol. 32, pp. 1103-1109.