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UTILIZATION OF LINEAR MIXING MODEL APPLIED TO LANDSAT-TM 
DATA TO CHARACTERIZE BRAZILIAN AMAZON FOREST 
Sergio Bernardes 
Instituto Nacional de Pesquisas Espaciais - INPE 
Caixa Postal 515, CEP 12201-970 
Sao José dos Campos - SP, Brazil 
ISPRS Commission VII / Working Group 3 
ABSTRACT 
The necessity to provide periodical studies of the amazon region, characterizing its natural resources and 
anthropic alteration processes is a source of several Remote Sensing studies, many of them applying 
digital image processing techniques. Conventional methods of image classification underline, 
predominantly, in the spectral characteristics of the pixels, understanding them as composed by a single 
class of land cover. Usually, a digital number results from integration of the responses of many targets in 
the ground. In this way, the signal produced by the combination, in one pixel, of two or more classes of 
land cover will not representative of none of them, resulting in a misunderstood classification. Therefore 
the spectral mixture is a limiting factor in a automatic classification approach. The aim of this work is to 
evaluate the use of synthetic images, obtained by a Linear Mixing Model, to characterize Brazilian 
amazon vegetation. The study area consists of approximately 690 km? of the Brazilian amazon, situated 
in the forest/savana ("cerrado") contact region, between 11R00'S and 51R00'W to 52R30'W. For the 
methodology implementation, a visual interpretation of Landsat-TM data was performed, identifying 
classes of land cover (forest, second growth forest, savanna, bare soil, ...). A Linear Mixing Model was 
applied to generate three synthetic images ("vegetation", "soil" and "shade"). These images will be 
classified using a maximum likelihood algorithm. The product of this approach will be compared with 
the visual interpretation in a geographic information system, generating an error matrix. Kappa 
coefficient of agreement will be used to determine the classification accuracy obtained with the 
application of this methodology. In this way, this work intends to contribute to future space-time analysis 
of the large amazon region, estimating deforesting and monitoring land occupation. 
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