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

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Chapter

Title:: Tropical forest cover classification using Landsat data in north-eastern India. Ashbindu Singh

Document type:: Multivolume work

Structure type:: Chapter

516 array set out in rows and columms which ex presses the number of observations assigned ta a particular land caver type (Landsat) relative ta the actual land caver (reference data). The values alang the diaganal repre sent the percentage af carrectly classified pixels fer each class and aff diaganal values represent errars af cammissians and amissians. Mapping accuracy M af class I (Kalensky and Wightman 1976) «1 '*>' N^7 x 10054 Where - mapping accuracy af class I N ■ number af carrectly classified 1 pixels in class I E_ = number af erraneaus pixels in 1 class I averall Landsat classification accuracy: Tatal carrect pixels Tatal pixels The tables 2-5 summarize the result af the classification far bath areas and the aver all summary af the results abtained by app lying twa classification schemes is given in table 6. Table 6. Summary af the accuracy af results far the twa classification schemes Area-1 Area-2 6 classes = 44.29% 5 classes =53.39% Class!- fication 2 classes = 85.92% 2 classes =79.60% 6 classes = 43.70% 5 classes =62.85% Recla ssifi cation 2 classes = 84.29% 2 classes =85.73% Landsat MSS system has its limitations. How ever, it does provide definitive information about the location af ‘closed forest*. It is expected that finer resolution satellite data such as that from the Thematic Mapper and SPOT would provide mare specific information. ACKNOWLEDGEMENTS This w©rk was carried out during postgraduate studies in the Department af Geography Uni versity af Reading, England; the author is grateful ta Dr.J.R.G.Tawnshend, Director, NERC unit far Thematic Information Systems at University of Reading for his valuable suggestions. The author is also thankful to the Commonwealth Scholarship Commission in the U.K and the Forest Department, Government af Manipur, India, respectively, for their financial support and sponsorship. REFERENCES Champion, H.G. & S.K.Seth 1968. Revised Survey ©f Forest Types af India, New Delhi, Govt.of India Press. Justice, C. & J.R.G.Tawnshend 1982. A compa rison af unsupervised classification pro cedures of Landsat MSS data for an area af complex surface conditions in Basilicata, Southern Italy, Remote Sensing af Environ, 12:407-420. Kalensky, Z. & J.M.Wightman 1976. Automatic forest mapping using remotely sensed data. Prec. 16th IUFRO world congress, Division 6, Norway: 115-135. Perssan, R. 1977. Scape and approach to world forest resource appraisals. Res. Rep. No. 23, Royal College of Forestry, Stockholm. Richards, P.W. 1973. The tropical rain forest. Scientific American 229; 58-68. Schawengerdt, R.A. 1983. Techniques for image processing and classification in remote sensing. New York, Academic press. 5, RESULTS AND DISCUSSION The application af computer aided analysis af Landsat MSS indicated that use af super vised technique did not yield acceptable accuracy as far as tropical forest caver cla ssification is concerned. However, closed forests which are among the mast productive in terms of primary production and source af ‘gene pool' can be identified with a high degree af accuracy (upta 90%) even in areas af rugged mountainous terrain and spectrally complex forest caver tapes. This capability itself is very useful to forest planners con cerned with the large area inventories in a region with a large inaccessible forests. These are the areas about which very little information is available. Contextual consi derations i.e. reclassification scheme, mo destly improved (6% to 9%) the classifica tion accuracy in areas of homogeneous cover types (Area-2) but the results were not en couraging for the areas of heterogeneous cover types (Area-1)• Also in mountainous terrain, shadow is one of the major sources of misclassification as in Area-1 roughly 15% of the pixels were affected by shadow. In Area-2 due to subdued relief only 2% of the pixels were affected by shadow. In con clusion, in areas affected by shifting cul tivation due to complex spatial distribu tion and intermixing of cover types, the

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