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:: The use of Thematic Mapper imagery for geomorphological mapping in arid and semi-arid environments. A. R. Jones

Document type:: Multivolume work

Structure type:: Chapter

279 lability of multiband remotely sensed data, and can lead to extraction of information not apparent on the original imagery. One such technique is principal component analysis (PCA), especially useful if the bands are highly correlated. New images are produced by calculating new, uncorrelated principal components using a series of linear weights, called eigenvectors, which are applied to the origional band values. These principal components represent more efficient ly the variance of the data. A study of the eigenvalues for the El Guettar scene indicates that the data are just about three dimensi onal in structure with 99 per cent of the variance being explained by the first three components (Table 4). A study of the eigenector weights attached to six principal component images, derived for the study area, using the six reflective TM bands. PCI is an overall summary of the albedo while PC2 is the diff erence between the visible/near IR and the mid-IR. This explains the brightness of the image, the extra ction of the areas of fan deposition, variation with in the playa and structure in the mountains. PC3 (Fig. 8a) has a high band 4 weight which produces an image similar to the 3/4 ratio displaying vegetation while PC4 (Fig. 8b) is the difference between the two mid-IR bands and picks out the structure in the solid geology, fans and variation in the playa very clearly. Too much noise in the remaining components prevent any useful analysis. Many remote sensing studies have used supervised classification whereby the image is classified by the user who defines training areas where the surface properties are known. This allows the computer to classify the image on the basis of the information held in the training areas. This works very well in vegetation studies (Townshend 1981) but in geomorph ology it is very'- difficult to obtain unique spectral responses for landforms. Therefore, supervised class ification is not very suitable and the potential of unsupervised classification has been tested. This applies a clustering algorithm to the data and requi res no previous knowledge of the area. In the algor ithm used, the data was clustered according to a max imum likelihood classification, and the only user in volvement was to stipulate the starting number of classes, the maximum and minimum percentage of the image contained in any one class and the number of iterations for clustering. The resulting image was colour-coded, density sliced, and class statistics were derived allowing the display of individual class es and the proportion of the image contained in each class. Figure 9. Example of one cluster derived from an un supervised classification of El Guettar using TM bands 3,4 and 5. This class relates to areas of solid geology. The test area was subjected to an unsupervised class ification on a three band image (3, 4 and 5) which produced 10 classes and 99 per cent of the image classified. The results are very engouraging. Some of the features which have been classified are areas of solid geology, variation within the fans, playa, alluvial plain, agriculture and the large ephemeral channel but a number of meaningless classes were also produced. The level of separation is visible in fig ure 9 which shows how one class corresponds to areas of solid lithology and oasis vegetation. Only rarely did a unique class occur, usually a number of featu res could be identified in the same cluster. This is due to similarity of pixel values of different units which leads to confusion within the feature space. However, the technique shows great potential for geo- morphological mapping especially if the user wishes to extract the main geomorphic units in a new area of interest. Further information reqarding the imaae nrocessina techniques discussed here can be found in Moik (1980) Schowengerdt (1983) and Gillespie (1980). 6 CONCLUSIONS It has been shown that although single band Thematic Mapper imagery and standard false-colour composites show excellent geomorphological detail, it is worth noting that when dealing with bare, unvegetated sur faces, typical of arid/semi-arid areas, the degree of correlation between the bands is so high that most of the bands are redundant. Much more information can be extracted using digital image processing. Effective techniques range from simple contrast stretching to more complex multivariate techniques such as unsuper vised classification and principal component analysis. However, no single image processing technique is suit able for all landforms. Best results are obtained when there exists a specific relationship between spectral response and the physical properties of the phenomena under investigation. This relationship can then be exploited by selecting the optimum image pro cessing technique. The resulting processed imagery are an invaluable asset to anyone contemplating geomorphological mapp ing especially in difficult terrain. Digitally enhan ced satellite Thematic Mapper imagery is a powerful tool for geomorphologists for studying processes and for mapping. Not only does it afford a new perspect ive from which to observe the Earth's surface but it allows the development of new ideas regarding geomorph ological processes, the origins and modifications of landforms. ACKNOWLEDGEMENTS I wish to thank Dr. J.R.G. Townshend for his construe tive comments during the preparation of this paper and to the cartographic and photographic units of the Geography Department, University of Reading. Arwyn Jones is a NERC postgraduate research student GT4/ 83/GS/87. REFERENCES Bailey, G.,J.Dwyer & Francica 1982. Evaluation of ima ge processing of Landsat data for geologic interpre tation of the Qaidam Basin, China. Second Thematic Conf., Remote Sensing for Exploration Geology, Fort Worth, Texas, p.555-577. Brunsden, D.,J.Doornkamp & D.Jones 1979. The Bahrain Surface Materials Resources Survey and its applicat ion to planning. Geogr. Jour. 145:1-35. Colwell, R. 1983. Manual of Remote Sensing, Vol I & II American Soc. of Photogram., Falls Church, USA. 2nd Edition. Doehring, D. 1980. Geomorphology in arid regions. All en & Unwin, London, p. 272.

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