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Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
Tropical forest cover classification using Landsat data 
in north-eastern India 
Ashbindu Singh 
Indian Forest Service, Forest Department, Government of Manipur, Imphal 
ABSTRACT i Landsat MSS data were used for mapping tropical forest ctver classes in two site 
conditions of northeastern India. These are areas of rugged mountainous terrain and spec 
trally complex forest cover types. A supervised classification procedure based on a minimum 
distance to means algorithm was used in the analysis. Classified images were reclassified 
using a 3x3 majority filter. A quantitative evaluation of the results were carried out to 
determine the accuracy of computer-aided forest classification. 
The computer-aided analysis of Landsat MSS data has shown that 'forest* and 'non-forest* 
could be classified with a high degree of accuracy, but further break-up of the classes did 
not yield satisfactory classification accuracy. However, it does provide definitive infor 
mation about the location of 'closed forest'. The reclassification scheme, modestly improved 
the classification accuracy in areas of homogeneous cover types but the results were not 
encouraging for the aras of heterogeneous cover types. 
1 INTRODUCTION 
The tropical forest biome is, biologically 
and ecologically speaking, the most complex 
and diverse biome on earth (Richards 1973). 
However, the location and extent of the area 
under tropical forests are poorly known. 
The published statistics are presented with 
a spuriously high degree of precision but 
possess a very low level of accuracy and 
reliability (Persson 1977). Due to inacces 
sibility of such areas and ruggedness of 
the terrain in which tropical forests are 
located the application of satellite data 
offers only possibility of mapping forest 
cover types in such regions with any degree 
of regularity. Since Landsat MSS data are 
available at regular intervals they may be 
used to derive a data base for monitoring 
tropical forest resources. The objective of 
this study is to assess the suitability of 
digital Landsat MSS data for tropical forest 
cover classification and determine the le 
vels of accuracy of computer aided forest 
classification. 
2 THE STUDY METHODOLOGY 
2.1 The study area 
The study was conducted in the northeastern 
part of India. Two areas of 256 x 256 
Landsat pixels-each labelled here as Area-1 
and Area-2 were choosen for investigation. 
Floristic composition of forests and topo 
graphic features were different for both 
the areas. Area-1 has been undergoing rapid 
change due t© shifting cultivation, wereas, 
Area-2 is ©nly marginally affected by this 
practice. Area-1 is situated in the eastern 
hills of Himalayas; the terrain is extremely 
rugged. Dense subtropical evergreen forest 
occur on high hills and moist deciduous fo 
rest are found in the valleys and at lower 
altitudes. The majority of moderately steep 
to gentle slopes (10-30%) have been severely 
affected by practice of slash and bum agri 
culture. 
Area-2 is located in outer parts of the Hi 
malayas. This is an area with subdued relief. 
The forests can be classified into tropical 
semi-evergreen forest and secondary moist 
bamboo brakes. The typical scrub formation 
in this area is degraded stages ®f climax 
high forests resulting from excessive human 
interference (Champion and Seth 1968). 
2.2 Definition of cover classes 
A suitable definition of cover classes is 
required in order to assign the ground sur 
face conditions to specific classes. In thi^ 
study land cover rather than land use formed 
the basis ®f classification. Stand density 
and stand height were considered to be the 
most important parameters' for defining the 
classes. The major cover classes occuring 
in the study areas are given in table 1. 
2.3 Reference data set 
For Area-1 and Area-2 forest types maps pre 
pared with the help of black and white ae 
rial photographs after field checking at 
the scale ©f 1:50,000 and 1: 63,360, res 
pectively, were used for generation of sam 
ple sites. A 3 x 3 pixel observation was 
chosen as a sampling unit. All the pixels 
in the window were utilised for the sample. 
30 sample sites for each category were lo 
cated using random spatial coordinates. In 
the classification analysis half of these 
were used as a training set and the remai 
ning half as a testing set. 
2.4 Landsat data 
The Landsat-2 CCT of path 145 ROW 042 dated 
6-12-1981 collected by the National Remote 
Sensing Agency ef India at the receiving 
station in Secunderabad, India was used in 
the analysis.