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For the last two decades, Landsat TM imagery has been used as an efficient tool for monitoring vegetation cover 
changes in tropical forest domains. Forest and clearings are well separated spectrally. Yet, several problems remain. 
First, the various stages of re-growth are not easily differentiated (Rignot et al., 1997). Second, cloud cover is a constant 
problem in the humid tropics, which limits the vegetation cover assessments in time and space (Rignot et al., 1997). 
Since 1991, imaging radar has been used to map natural resources from satellite platforms (Lillesand and Kiefer, 1994). 
Differing from the optical, the SAR (Synthetic Aperture Radar) sensor is active, it emits and receives electromagnetic 
radiation using an antenna. The energy received back (or backscatter) depends on the target's roughness, shape, 
orientation towards the antenna, and moisture content. As the wavelengths used are in the order of a few centimetres to 
nearly 30 centimetres, the radiation is not reflected or absorbed by clouds or haze, thereby allowing more frequent and 
systematic assessments of land cover changes and deforestation. On the other hand, SAR imagery is usually affected by 
terrain geometric distortions on its side-scanning geometry causing layover, foreshortening and shadow (Pohl, 1996). 
More recently, several studies have been conducted indicating the potential of merging optical and SAR data to assess 
better land cover classification results. In a study site located in the Brazilian amazon forest, Rignot er al.(1996) 
compared the classification results of two types of SAR sensors (SIR-C and JERS-1, with different wavelengths and 
incidence angles), two types of optical sensors (Landsat TM and Spot XS) and the combination of Landsat TM and 
SIR-C. They concluded the combined use of optical and radar imagery provides the most reliable form of land cover 
mapping, focusing on the discrimination of different stages of forest regrowth. Seven different classes of land cover 
including two levels of regrowth were mapped with 93% overall accuracy. 
This study aims to evaluate the fusion of Landsat TM imagery with SAR satellite imaging data (from two different 
sensors, with different polarizations, wavelengths, and incidence angles) to assess land cover classification in a 
representative area of the Brazilian Atlantic forest domain. 
2 STUDY AREA AND DATA SET 
The study area is located in south-eastern Brazil, in the south-east of Minas Gerais State, approximately 150 km from 
Rio de Janeiro. It extends from 21°30’ to 21°56” S and 43°40’ and 44°13” W, with an area of about 20.75 ha. The 
region is part of the Mantiqueira mountain range and it is characterised by undulated terrain with occurrence of 
escarpments in some areas. The elevation varies from 700 to 1784 (Lombada peak) metres above see level. 
Based on the mentioned latitude and altitude above sea level, Veloso et al. (1991) and Oliveira-Filho and Ratter (1995) 
would classify the primary vegetation in study area as tropical montane semideciduous forest, one of the major Atlantic 
forest formations. It is described by the authors as being ‘seasonal but moderately deciduous forest, occurring in the 
high-altitude (above 750m) hinterlands of southeastern and central Brazil, associated with soils of intermediate fertility’ 
Cattle grazing for milk production is the main land use type and therefore pasture is the dominant land cover type in the 
study area. Eucalyptus plantations occupy a secondary position in area but play an important hole in the region’s 
economy. Tourism has been increasing recently and is the main income for the population of Conceiçäo do Ibitipoca, a 
village of about 1,000 inhabitants nearby the Ibitipoca State Park. Inaugurated in 1973, the park is centrally located in 
the study area and has the status of conservation area. 
In data fusion, images have to be close to each other in time to avoid possible land cover changes. JERS-1 imagery was 
given a priority, since it is expected to give better forest cover classification results than the other available imaging 
SAR satellites due to its sensor characteristics (e.g. longer wavelength, medium incidence angle and HH polarization). 
The last JERS-1 image available for the region of interest is from December of 1995. The closest Landsat TM available 
with some clouds (not more than 1096) was chosen for data fusion purposes. An ERS-2 image was selected as a second 
source of SAR satellite data (with different sensor characteristics) for comparison purposes. A second Landsat TM 
image closer to the fieldwork date was selected to be classified for sampling design purposes. Finally, a cloud-free 
Ladsat TM image was used as source of information on the cloud gaps from the first Landsat TM image. Table 1 shows 
the images and their correspondent specifications. The study area boundaries were defined by overlaying images from 
the different sources and evaluating the suitability of all possible areas that contained the three imagery types required. 
Table 1: Satellite imagery selected, their main characteristics and purpose. 
  
  
  
  
  
  
  
Product (#) Date Bands Qi Frame Se Fee Purpose 
|_ Landsat-5 TM (1) 24/08/95 3,4,5 218 075 21°41°S, 44°37>W 30m data fusion 
Landsat-5 TM (2) 31/01/96 3,4,5 218 075 21°40’S, 44°42°W 30m | cloud cover gaps info. 
| Landsat-5 TM (3) 16/08/98 3.4.5 218 075 21°42’S, 44°37’W 30m sampling design 
JERS-1. SAR. BSQ (4) | 09/12/95 n.a. 377 337 21°83’S, 43°93’W 12.5m data fusion 
ERS-2. SAR. PRI (5) 17/04/96 n.a. 05188 4041 21°31°S, 43°477W 12.5m data fusion 
  
  
  
  
  
  
  
  
  
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part Bl. Amsterdam 2000. 97