Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 3 Spectral signatures of objects. Chairman: G. Guyot, Liaison: N. J. J. Bunnik

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: LANDSAT TM band combinations for crop discrimination. Sherry Chou Chen, Getulio Teixeira Batista & Antonio Tebaldi Tardin

Document type:: Multivolume work

Structure type:: Chapter

214 as the best to discriminate soybeans, com and sugarcane in the area. - Separability of J-M distance indicated that the statistical structure for crop discrimination was three-or four-dimensional. .Comparison of the classification matrices of the training areas using band combinations 2-4-5 and 2-4-5-7 showed similar results and a small difference in computer processing time. However, the smaller data storage volume of the three-band classification makes it the most appropriate for crop discrimination using digital analysis in this study area. REFERENCES Anuta, P.E., Bartolucci L.A., Dean, M.E., Lazano, D.F., Malaret E., McGillem C.D., 1984 Landsat-4 MSS and Thematic Mapper data quality and information content analysis. TREE Trans, on Geosci. and Remote Sens. GE-22(3): 222-235. Atkinson P., Cushnie J.L. and Townshend J.R.G. 1985. Inproving Thematic Mapper land cover classification using filtered data. Int. J. Remote Sens. 6(6): 955-961. Salcmonson, V.V., Smith P.L., Pink A.B., Webb, W.C., and Lynch T.J. 1980. An overview of progress in the design and implementation of Landsat D systems I.E.E.E. Trans. Geosci. Remote Sensing 18:137- 146. Sheffield C., 1985. Selecting band combinations frcm multispectral data. Photogram. Eng. Remote Sens. 51(6): 681-687. Swain, P.M., King, R.C. 1973. Two effective feature selection criterion for multispectral remote sensing. LARS information note 402673 West Lafayette, Purdue University. Townshend, J.R.G., Gayler, J.R., Hardy, J.R., Jackson M.J., and Baker, J.R. 1983. Preliminary analysis of Landsat-4 Thematic Mapper products. Int. J. Remote Sens. 4 (4): 817-828. Townshend J.R.G. 1984 Agricultural land-cover discrimination using thematic mapper spectral bands. Int. J. Remote Sens. 5(4): 681-698. Young, T.Y., Calvert, T.W. 1974. Classification estimation and pattern recognition. New York, Elsevier.

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