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:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The use of SPOT simulation data in forestry mapping. S. J. Dury, W. G. Collins & P. D. Hedges

Document type:: Multivolume work

Structure type:: Chapter

Table 4 The Effect of correctly classifying all misclassified urban pixels. Class Correct (before smoothing) Total % Correct (after smoothing) Total % Oak 1845/1915 252 93 261 97 •I 1947/9 97 82 104 87 SW CH 1961 86 98 85 97 E L 1933 139 66 154 73 II 1949 53 54 54 55 EL/HL 1981 31 50 30 48 H L 1971 97 96 100 99 D F 1966 128 71 144 80 N S 1940 1 1 3 3 " 1971/2 145 83 159 91 NS/SP 1966 67 31 49 23 S P 1928 77 93 74 89 C P 1965/6 92 79 90 78 URBAN 149 96 151 97 AGRIC 390 85 425 93 One of the aims of this research is to build up a 'database' system to explore not only the relationships outlined above but also the influence of additonal variables such as slope, aspect and soil type. Jones (1972) demonstrated that production levels for forest plots were shown to correlate with aspect, average elevation and slope giving a multiple correlation value of r=0.957. One aspect to emerge so far from this study is the very high correlation between SPOT bands 1 and 2. One of these bands becomes virtually redundant for the purposes of spectral separation. Nelson et al (1984) found that when using TM Simulator data for forest cover-type mapping, the most useful waveband combinations used at least one band from the visible near infra-red and mid infra-red spectral regions. Thus the addition of a new mid infra-red sensor proposed for the SPOT 3 satellite (expected launch 1991) may greatly enhance spectral discrimination. There is also evidence to suggest that multitemporal classification techniques improve classification accuracy. 4 CONCLUSION The SPOT satellite will have the potential for forest-cover mapping at species level, and in some cases even different ages of the same species. In this study the major handicap to this was the presence of urban areas within the study area. The high spectral variance of the urban class resulted in gross misclassification errors. Applying a textural classifier or using a multi-layered classification approach may overcome this problem. There would appear to be a relationship between stocking and band 2 reflectance, and size and band 3 reflectance. It is intended to explore this further as well as identifying the influence of additonal variables - such as slope, aspect and soil types, by the merging of data sets to create a database system. Satellite remote sensing has greatest potential with regards to commercial forestry. The main limitation in mapping diversely specied, varied-aged natural forest is the pixel resolution; too many 'mixed' pixels would invariably result, rendering any subsequent classification meaningless. However the commercial forester's requirement is often restricted to straight, even-grained timber of selected species, resulting in even-aged stands of single species which are consequently easier to map. REFERENCES 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.Geol. Survey Professional Paper 964. Buchheim, M.P;Maclean, A.L and Lillesand, T.M 1984. Forest cover type mapping and Spruce Budworm defoliation detection using simulated Spot imagery. Spot Simulation Applications Handbook.Proceedings of the 1984 Spot Symposium. May 20-23 Scottsdale Arizona. Cross,A & Mason D.C.1985. Segmentation of remotely sensed images by a split and merge process. Dury,S.J; Collins,W.G.and Hedges,P.D.1986.The developement of a methodology for forestry management using Spot simulation data. Proceedings of the Int. Symp. on Photogrammetry and Remote Sensing for the developing countries,New Delhi,India. Grainger, A. 1980. The state of the world's tropical forests. The Ecologist Vol.10 Nos.1/2 p.6-54. Holmes, G.D 1980. The ecology of even-aged plantations : An introduction to forestry in upland Britain. Quarterly Journal of Forestry Vol 74 (2) 73-81. Isako, J. 1979. Texture analysis by space filter and application to forest type classification. Proceedings of the Fifth Int.Symp.on Machine Processing of Remotely Sensed Data.Purdue Univ., LARS, Lafayette, Indiana, p 392-393. Kachhwaha, T.S. 1983. Spectral signatures obtained from Landsat digital data for forest vegetation and land-use mapping in India. Photogrammetric Engr. and Remote Sensing 49 : 5 pp 685-689 Mayer, K.E and Fox III, L. 1981. Identificationof conifer species groupings from Landsat Digital Classifications. Photogrammetric Engr. and Remote Sensing 47 : 11 pp 1607 - 1614. Nelson, R.F; Latty, R.S and Mott, G. 1984. Classifying northern forests using Thematic Mapper simulation data. Photogrammetric Engr.and Remote Sensing 50 : 5 pp 607-617. Schary, R.W. 1972. Plants for Man. Prentice Hall,Inc. Eaglewood Cliffs. 428

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