An iterative classification approach for mapping natural resources from satellite imagery

International Journal of Remote Sensing

Volumn 17, Issue 5, 1996, Pages 957-981

  San Miguel Ayanz, J ^a   Biging, G S ^b  

^a EUROPEAN SPACE AGENCY   (Netherlands)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAND SELECTION; CLASSIFICATION; GIS; NATURAL RESOURCE MAPPING; SATELLITE IMAGERY;

GEOGRAPHIC INFORMATION SYSTEMS; IMAGE ANALYSIS; IMAGE SEGMENTATION; IMAGING TECHNIQUES; ITERATIVE METHODS; NATURAL RESOURCES; PATTERN RECOGNITION; PROBABILITY;

SATELLITE IMAGERY; SPECTRAL PATTERN ANALYSIS; SPECTRAL SEPARABILITY INDICES;

REMOTE SENSING;

EID: 0029667618     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431169608949058     Document Type: Article

References (47)

1. Anderson, J. R., Hardy, E. E., Roach, J. S., and Witmer, R. D., 1976, A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, U.S. Government Printing Office, Washington, D.C.
2. Aronoff, S., 1982, Classification accuracy: a user approach. Photogrammetric Engineering and Remote Sensing, 48, 1299-1307.
3. Baker, J. R., Briggs, S. A., Gordon, V., Jones, A. R., Settle, J. J., Townshend, J. R. G., and Wyatt, B. K., 1991, Advances in classification for land cover mapping using SPOT HRV imagery. International Journal of Remote Sensing, 12, 1071-1085.
4. Biging, G. S., and Congalton, R. G., 1989, Development of Remote Sensing Technologies for Use in Forest Inventory. Proceedings of the International Conference and Workshop on Global Natural Resource Monitoring and Assessment: Preparing for the 21st Century, held in Venezia, Italy on 24-30 September 1989 (Bethesda, MD: American Society for Photogrammetry and Remote Sensing), pp. 1241-1249.
5. Bishop, Y. M., Fienberg, S. E., and Holland, P. W., 1975, Discrete Multivariate Analysis: Theory and Practice (Cambridge, Massachusetts: The MIT Press).
6. Bolstad, P. V., and Lillesand, T. M., 1991, Rapid Maximum Likelihood classification Photogrammetric Engineering and Remote Sensing, 57, 67-74.
7. Bradley, R. C., 1988, Using textural measures to distinguish spectrally similar vegetation. ASPRS/ACSM Annual Convention, held in St Louis, Missouri, 7-10 March 1988 (Bethesda, M D: American Society for Photogrammetry and Remote Sensing), 4, 40-49.
8. Burrough, P. A., 1986, Principles of Geographical Information Systems for Land Resources Assessment (Oxford: Oxford University Press).
9. Campbell, J. B., 1981, Spatial correlation effects upon accuracy of supervised classification of land cover. Photogrammetric Engineering and Remote Sensing, 47, 255-363.
10. Campbell, N. A., 1987, Accuracy assessment: another user’s perspective. Photogrammetric Engineering and Remote Sensing, 52, 423-428.
11. Card, D. H., 1982, Using known map category marginal frequencies to improve estimates of Thematic Map accuracy. Photogrammetric Engineering and Remote Sensing, 48, 431-439.
12. Chamignon, C., and Maniere, R., 1990,.Forest cover type mapping and damage assessment of Zeiraphira diniana by SPOT 1 HRV data in the Mercantour National Park. International Journal of Remote Sensing, II, 1439-1450.
13. Cohen, J., 1960, A coefficient of agreement of nominal scales. Educational and Psychological Measurement, 20, 37-46.
14. Congalton, R. G., 1984, A comparison of five sampling schemes used in assessing the accuracy of land cover/land use maps derived from remotely sensed data. PhD. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, U.S.A.
15. Congalton, R. G., 1991, A review of assessing the accuracy of classifications of remotely-sensed data. Remote Sensing of Environment, 37, 35-46.
16. Congalton, R. G., San Miguel-Ayanz, J., and Gallup, B. K., 1991, Remote sensing techniques for hardwood rangeland mapping. Final report to California Department of Forestry, Forest and Rangeland Resources Assessment Programm, Sacramento, California, U.S.A.
17. Crippen, R. E., 1990, Calculating the vegetation index faster. Remote Sensing of Environment, 34, 71-73.
18. Eaton, P. W., 1978, Yarnold’s criterion and minimum sample size. The American Statistician, 32, 102-103.
19. Eulers M., Greeniee D., Smith, T., and Star, J. L., 1991, Integration of remote sensing and GIS: data and data access. Photogrammetric Engineering and Remote Sensing, 57, 669-676.
20. ERDAS, 1990, ERDAS Field Guide, Version 7.5 (Atlanta: ERDAS Inc.).
21. Franklin, S. E., and Peddle, D. R., 1989, Spectral texture for improved class discrimination in complex terrain. International Journal of Remote Sensing, 10, 1437-1443.
22. Franklin, S. E., and Peddle, D. R., 1990, Classification of SPOT HRV imagery and texture features. International Journal of Remote Sensing, 11, 551-556.
23. Fung, T., and LeDrew, E., 1988, The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering and Remote Sensing, 54, 1449-1454.
24. Gallup, B. K., 1991, Semi-automated training area selection and a nonparametric classifier compared to traditional digital satellite data classification, M.S. Thesis, University of California at Berkeley, Berkeley, California, U.S.A.
25. Hutchinson, C. F., 1982, Techniques for combining Landsat and ancillary data for digital classification improvement. Photogrammetric Engineering and Remote Sensing, 48, 123-130.
26. Jensen, J. R., 1986, Introductory Digital Image Processing. A Remote Sensing Perspective (Englewood Cliffs, NJ: Prentice-Hall).
27. Kettig, R. L., and Landgrebe, D. A., 1976, Classification of multispectral image data by extraction and classification of homogeneous objects. I.E.E.E. Transactions on Geoscience Electronics, 14, 19-25.
28. Labovitz, M. L., 1986, Issues arising from sampling designs and band selection in discriminating ground reference attributes using remotely-sensed data. Photogrammetric Engineering and Remote Sensing, 52, 201-211.
29. Mather, P. M., 1987, Computer Processing of Remotely Sensed Images: An Introduction (New York: John Wiley & Sons)
30. McGwire, K. C., 1992, Analyst variability in labelling unsupervised classifications. Photogrammetric Engineering and Remote Sensing, 58, 1673-1678.
31. McHarg, J., Radke, J., Berger, J., and Wallace, K., 1992, A Database Prototype for a National Ecological Inventory. Final report to the Environmental Protection Agency (EPA), Washington, D.C.
32. Peterson, D. L., and Running, S. W., 1990, Applications in forest science and management. In Theory and Applications of Optical Remote Sensing, edited by G. Asrar (New York: John Wiley & Sons), pp. 429-473.
33. Peucker, T. K., Fowler, R. J., Little, J.J., and Mark, D. M., 1978, The Triangulated Irregular Network. Proceedings of the D.T.M. Symposium. American Society of Photogrammetry-American Congress on Survey and Mapping, in St. Louis, Missouri, on 3-7 March 1978 (Bethesda, MD: American Society for Photogrammetry and Remote Sensing), pp. 24-31.
34. San Miguel-Ayanz, J., 1993, Forest type mapping and geographic information analysis of the Central Sierra of Spain from SPOT and Thematic Mapper imageries. PhD Dissertation, University of California at Berkeley, Berkeley, California, U.S.A.
35. San Miguel, A., Fernandez, A., and San Miguel, J., 1990, Tablas de peso para rebollos (iQuercus pyrenaica Wiild.) en monte bajo en la Sierra de Guadarrama. Investigacion Agraria, Sistemas y Recursos Forestales, 1, 173-179.
36. San Miguel, A., 1985, Variaciones producidas en un pastizal arbolado con rebollos (Quercus pyrenaica Willd.) pore claras de distinta intensidad. Anales INIA Serie Forestal, 9, 97-104.
37. Settle, J. J., and Briggs, S. A., 1987, Fast maximum likelihood classification of remotely sensed imagery. International Journal of Remote Sensing, 8, 723-734.
38. Shettigara, V. K., 1991, Robustness of Gaussian maximum likelihood and linear discriminant classifiers. Proceedings of the International Geoscience and Remote Sensing Symposium, IGARSS'91, Remote Sensing: Global Monitoring for Earth Management, held at the Helsinki University of Technology in Espoo, Finland, on 3-6 June 1991 (New York: I.E.E.E.), 3, 1830-1842.
39. Skidmore, A. K., and Turner, B. J., 1989, Assessing the accuracy of resource inventory maps. Proceedings International Conference and Workshop on Global Natural Resource Monitoring and Assessment: Preparing for the 21st Century, held in Venezia, Italy on 24-30 September 1989 (Bethesda, MD: American Society for Photogrammetry and Remote Sensing), 2, 524-535.
40. Skidmore, A. K., and Turner, B. J„ 1992, Map accuracy assessment using line intersect sampling. Photogrammetric Engineering and Remote Sensing, 58, 1453-1458.
41. Stenbeck, J. M., and Congalton, R. G., 1990, Using Thematic Mapper imagery to examine forest understory. Photogrammetric Engineering and Remote Sensing, 56, 1285-1290.
42. Story, M., and Congalton, R. G., 1986, Accuracy assessment: A user's perspective. Photogrammetric Engineering and Remote Sensing, 52, 397-399.
43. Swain, P. H., and Davis, S. M., 1978, Remote Sensing: The Quantitative Approach (London, New York: McGraw-Hill).
44. Townskend, J. K. G., Gayler, J. R., Hardy, J. R., Jackson, M. J., and Baker, J. R., 1983, Preliminary analysis of Landsat-4 Thematic Mapper products. International Journal of Remote Sensing, 4, 817-828
45. van Genderen, J. L., and Lock, B. F., 1977, Testing Land Use map accuracy. Photogrammetric Engineering and Remote Sensing, 43, 1135-1137.
46. Walsh, S. J., Cooper, J. W., Von Essen, J. E., and Gallager, K, B., 1990, Image enhancement of Landsat Thematic Mapper data and GIS data integration for evaluation of resource characteristics. Photogrammetric Engineering and Remote Sensing, 56, 1135-1141.
47. Yarnold, J.K., 1970, The minimum expectation in Chi-squared goodness of fit tests, and the accuracy of approximation for the null distribution. Journal of the American Statistical Association, 65, 864-886.