Representation of ecological trends in remotely sensed data: Relating the probability of class membership to canopy composition and a vegetation ordination

Geocarto International

Volumn 11, Issue 1, 1996, Pages 3-11

  Foody, Giles M ^a   Trodd, Nigel M ^a  

^a UNIVERSITY OF SALFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CANOPY COMPOSITION; CLASS MEMBERSHIP; ECOLOGICAL TREND; HEATHLAND; HEATHLAND VEGETATION; IMAGE CLASSIFICATION; MAXIMUM LIKELIHOOD CLASSIFICATION; REMOTE SENSING; VEGETATION ORDINATION;

EID: 0030456229     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106049609354519     Document Type: Article

References (15)

1. Campbell, N A., 1984. “ Some aspects of allocation and discrimination ”. In Multivariate Statistical Methods in Physical Anthropology, Edited by:van Vark, G N, and Howells, W. 177–192. Reidel.
2. Foody, G M., 1990. “ Representation of inter-class gradients in heathland vegetation from ATM data ”. In Proceedings of the NERC Symposium on Airborne Remote Sensing 1990, 99–105. Nottingham:NERC-British Geological Survey.
3. Foody, G M., 1992. A fuzzy sets approach to the representation of vegetation continua from remotely sendsed data:an example from lowland heath. Photogrammetric Engineering and Remote Sensing, 58:221–225.
4. Foody, G M, Campbell, N A, Trodd, N M, and Wood, T F., 1992. Derivation and applications of probabilistic measures of class membership from the maximum likelihood classification. Photogrammetric Engineering and Remote Sensing, 58:1335–1341.
5. Hill, MO. 1979. DECORANA. A FORTRAN Program for Detrended Correspondence Analysis and Reciprocal Averaging, Cornell Ecology Program CEP-40 Ithaca, New York:Cornell University.
6. Key, J R, Maslanik, J A, and Barry, R G., 1989. Cloud classification from satellite data using a fuzzy sets algorithm:a polar example. International Journal of Remote Sensing, 10:1823–1842.
7. Klecka, W R., 1980. Discriminant Analysis, London:Sage.
8. Swain, P H., 1978. “ Fundamentals of pattern recognition in remote sensing ”. In Remote Sensing:the Quantitative Approach, Edited by:Swain, P H, and Davis, S M. 136–178. McGraw-Hill.
9. Thomas, IL, Benning, V M, and Ching, N P., 1987. Classification of Remotely Sensed Images, Bristol:Adam Hilger.
10. Tom, C H, and Miller, L D., 1984. An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms. Photogrammetric Engineering and Remote Sensing, 50:193–207.
11. Townshend, J R G., 1984. Agricultural land-cover discrimination using thematic mapper spectral bands. International Journal of Remote Sensing, 5:681–698.
12. Trodd, N M, Foody, G M, and Wood, T F., 1989. “ Maximum likelihood and maximum information:mapping heathland with the aid of probabilities derived from remotely sensed data ”. In Remote Sensing for Operational Applications, 421–426. Nottingham:Remote Sensing Society.
13. Wang, F., 1990. Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 28:194–201.
14. Weaves, R E., 1990. “ Upland vegetation survey:correspondence between ground and remotely sensed data ”. In Proceedings of the NERC Symposium on Airborne Remote Sensing 1990, 151–157. Nottingham:NERC-British Geological Survey.
15. Wood, T F, and Foody, G M., 1989. Analysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths. International Journal of Remote Sensing, 10:181–191.