Full text: Remote sensing for resources development and environmental management (Volume 1)

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 
6 classes = 44.29% 
classes =53.39% 
2 classes = 85.92% 
classes =79.60% 
6 classes = 43.70% 5 classes =62.85% 
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. 
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. 
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, 
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. 
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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