ul 2004 
—M 
dat set: 
1ational 
2004. 
od with 
ind use 
(9), pp. 
MONITORING THE AMAZON WITH DIFFERENT SPATIAL AND TEMPORAL 
RESOLUTION 
G. Zimmermann, W. Bijker 
ITC, P.O. Box 6, 7500 AA Enschede, The Netherlands - (Zimmermann, bijker)@itc.nl 
KEY WORDS: Forestry, Monitoring, Combination, Radar, Resolution, Spatial, Temporal 
ABSTRACT: 
When monitoring deforestation, frequent images with an optimal spatial resolution are required. But in reality either images with 
low spatial resolution and a high revisit frequency or high spatial resolution images with a low revisit time are available. Combining 
different spatial and temporal resolutions could solve this problem. The study site in the Colombian Amazon contains small fields 
within the forest. The input data consisted of one high spatial resolution AIRSAR image and a time series of nine ERS-1 images of 
medium spatial resolution, and their supervised classifications. The AIRSAR image was upscaled with stepwise upscaling based on 
interim results and by direct upscaling from the same basis to different levels of spatial resolution. The comparison showed that the 
proportion of land cover classes did not change substantially in either of the two upscaling approaches, while the number and size of 
the patches showed a clear decrease with continuing upscaling. The direct upscaling approach provided best results. Furthermore, 
the conformity of the upscaled AIRSAR land cover map and the ERS-1 land cover maps was determined. For the study area with its 
particular land cover pattern, the effect of the spatial resolution on classification was not as important as expected. The fact that 
AIRSAR has three fully polarimetric bands, while ERS-1 has only one band and one polarization was a more important cause for 
differences between the land cover maps than the differences in spatial resolution. 
1. INTRODUCTION 
The Amazon forest is the largest tropical forest in the world. 
The Greater Amazon region in South America has to cope with 
large amounts of damage from deforestation affecting the 
region itself, as well as global ecosystems through its influence 
on climate and hydrology. Many governmental and non- 
governmental organizations are therefore interested in regular 
updates of information on the forest. Monitoring based on 
remotely sensed imagery is a logical choice, because the area is 
vast and inaccessible. Previous research on tropical 
deforestation used images with low spatial resolution (e.g. 
Cross et al., 1991; Malingreau et al., 1989; Mayaux et al., 1995) 
as well as medium spatial resolution imagery (e.g. Skole et al., 
1993). 
When monitoring the earth's surface with remote sensing, 
problems like high costs for high spatial resolution imagery and 
image processing as well as spatial and temporal resolutions 
that are sub-optimal for the process to be monitored are 
encountered. If monitoring a process over a specific time, one 
will need frequent images with an optimal spatial resolution. 
But in reality either images with low spatial resolution and a 
high revisit frequency or high spatial resolution images with a 
low revisit frequency are available. A combination of imagery 
with different spatial and temporal resolutions may be 
considered to overcome these problems, c.g. to reduce costs and 
üme of image acquisition and processing while maintaining 
required spatial and temporal detail. 
For a number of change processes, both the process itself and 
its speed are known or can be predicted across the study area. 
Deforestation occurs mainly and most rapidly along the fringes 
of the forest and close to roads and rivers. 
Much research has already been carried out on integration of 
data of different spatial resolution and the generalization of 
data. However, the temporal dimension, how this works out in a 
957 
monitoring system has not received so much attention yet. Thus 
it is of particular relevance to focus the research on combining 
the spatial and the temporal aspect. 
1.1 Research objective 
The objective of the research is to assess whether a combination 
of low spatial resolution and high spatial resolution imagery 
gives better results than only using frequent low spatial 
resolution or only infrequent high spatial resolution. 
1.2 Research questions 
Does a combination of low spatial resolution and high spatial 
resolution imagery give better results, in terms of higher 
accuracy, more thematic detail, than only using frequent low 
spatial resolution or only infrequent high spatial resolution? 
* Did the upscaling method affect the results of the 
classification of the high spatial resolution data? And 
if so, how? 
= Did parts of the data or information get lost or could 
new information be gained during upscaling? 
« What kind of pattern change occurs with a change in 
the resolution? 
* What is the degree of conformity between low and 
high spatial resolution data? 
= Can the better spatial detail of the high spatial 
resolution images be interpolated over time while 
using low spatial resolution images?