International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
2. STUDY AREA 
The study area is located on the northern fringe of the 
department of Guaviare in the Colombian Amazon. It extends 
from the capital of the department, San Jose del Guaviare, 30 
km south to El Retorno from latitude 2°35' to 2°20' north and 
from longitude 72°47' to 72?35' west (Bijker, 1997). 
ATLANTIC OCEAN 
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Figure 1. Location of study area 
2.1 Land cover 
Before the 1950s, tropical evergreen rain forests covered a 
larger part of the area. Due to the process of colonization, 
extensive parts have been deforested and replaced by crops, 
pastures or dense secondary vegetation. Besides the evergreen 
rain forest and the human influenced vegetation, savannahs also 
exist in the study area. 
2.2 Land use 
In the uplands pasture for cattle breeding is the dominant land 
use in cleared areas. The following process will mostly be 
applied to convert the forest into pastures: first the primary or 
secondary forest will be cut and burned, after that perennial 
crops (e.g. cacao, plantain) and annual crops (e.g. maize, rice) 
are planted. A smaller part of the area will be planted with more 
permanent crops like rubber, fruit trees, sugarcane and coca. 
The number of years a field is used for crops can vary, but after 
a view years of cultivation it will be left fallow or turned into 
pastures (Bijker, 1997). 
[n the alluvial plain of the Guaviare river agriculture is the main 
land use. The soils are more fertile than in the uplands, 
therefore the crop yields are higher in the alluvial plain. The 
main cultivated crops are bananas, cacao, maize, soybean, 
cotton, sesame, cassava and sugarcane (Bijker, 1997). 
3. DATA AND METHODOLOGY 
The input data in this research consist of one C-, L-, P-band 
polarimetric AIRSAR image of May 1993 with a spatial 
resolution of 6 m (Hoekman et al., 2000) and a time series of 
nine ERS-1 images from May 1992 until September 1994 with 
a spatial resolution of 12.5 m as well as the land cover maps 
derived from these images (Bijker, 1997). The land cover maps 
were based on all ERS-1 images available till that date. 
Common land cover classes are needed to facilitate the 
comparison between the classifications of the high spatial 
resolution image and the lower spatial resolution images. In this 
  
research four land cover classes were used: primary forest, 
secondary forest, pastures and recently cut areas. 
Furthermore, spatial scaling is needed. Spatial scaling takes 
information at one scale and uses it to derive processes at 
another scale (Jarvis, 1995). This can be either upscaling, where 
information at a higher spatial resolution is taken and 
transformed to the lower spatial resolution or downscaling, 
which works in opposite direction (Jarvis, 1995). In this case 
upscaling of the high spatial resolution AIRSAR land cover 
map was applied. 
Two processes will be applied to the high resolution AIRSAR 
data. First, the classification of these data, as made by Quifiones 
(1995; Hoekman et al., 2000) will be upscaled. The outcome is 
referred to as AIRSAR land cover map 1 later on. Secondly, the 
original AIRSAR image will be upscaled first and subsequently 
classified. This result is referred to as AIRSAR land cover map 
a 
Two different approaches for upscaling were evaluated in order 
to determine their effect on proportional area of land cover 
classes and to detect changes in the number and size of the 
patches of the AIRSAR land cover map during the upscaling 
process as well as to select the most eligible procedure to 
upscale the AIRSAR data to 12.5 m, the resolution of the ERS- 
1 land cover maps. Finally, the classified ERS-1 images of 
Bijker (1997) were compared with the upscaled AIRSAR land 
cover maps to assess their conformity. 
This research refers to pixels. According to Bian (1997), only 
objects that operate at a scale larger than the size of the pixel 
can be revealed during upscaling. But it also needs to be 
mentioned, that small objects, which have a high contrast with 
their surrounding area, may be detectable even if they are 
smaller than the pixel size. However, at a high spatial resolution 
pixel sizes are mostly smaller than objects of interest. 
Therefore, the neighbouring pixels are highly correlated and a 
low variance exists among them. With an increase in the pixel 
size, the similarity decreases and the variance increases 
(Rahman et al., 2003; Woodcock et al., 1987). 
4. RESULTS 
4.1 Evaluation of different upscaling approaches 
The first approach implies stepwise upscaling in half-meter 
steps, beginning with the AIRSAR land cover map 1 of 6 m 
resolution until the resolution of 12.5 m was obtained. The 
stepwise upscaling is based on each previous interim result. The 
second approach used direct upscaling to different levels of 
spatial resolution each based on the 6 m resolution AIRSAR 
image. The two approaches resulted both in 14 upscaled 
AIRSAR land cover maps of different spatial resolutions. 
In order to calculate the new output pixels, the nearest 
neighbour resampling method was applied, because it uses the 
nearest pixel without any interpolation to create the resampled 
image. The original pixels are simply relocated onto 2 
geometrically correct map grid. 
4.1.1 Changes in proportion of land cover classes 
Research regarding problems of upscaling high resolution 
remote sensing data showed, that it can be assumed that in 
general the proportion of land cover classes will decrease with 
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