joints. 
Data Analysis 
The data analysis, in the remote 
sensing data flow and Geographic 
Information Systems, involves the 
relationship between the data variables 
and the subsequent  interferences that 
can be developed. This error 
acumulation stage focus on the statistic 
techniques validation. Difficulties on 
the statistic analysis of space data 
source involves the typical adoption of 
the common linear model, composed by the 
Space autocorrelation effects. The data 
analysis is also subject to errors 
proceeding from the variability of the 
analysis specialists. This: variability 
involves the choice of pre-defined 
relevant variables or the synthesis of 
new multiple parameter variabies, 
correlated or not. 
The classification systems can be a 
Source of error in the remote sensing 
data integration in Geographic 
Information System. Some of the 
potential error sources induced by the 
classification systems are related to 
the inability of the systems to 
categorize mixed classes, transition 
Zones or ambigous classes definitions, 
the human subjectivity and the lack of 
compatibility between different 
classification systems used with remote 
sensing data and traditional data types. 
Thematic data levels created using 
general remote sensing data require the 
use of some kind of classification 
system to facilitate the data 
categorization for subsequent space data 
analysis by GIS. When we work with 
mixed pixels, or polygons and transition 
zones, or dynamic systems, inconsistent 
classification occur with the whole 
classification system. This inserts an 
error element which is particularly 
difficult to be quantified. 
The data generalization is usually 
made during the remote sensing analysis 
by two reasons: space resolution and/or 
thematics or spectral data resolution. 
The space generalization involves the 
analysis to produce the minimum unit of 
the map. The sampling for a space 
resolution better than the original 
results in a substantial error. 
Spectral generalization can be executed 
by filters that enhance certain 
characteristic like the frame or 
homegenize similar pixels. Some filters 
preserve the frames, while reduce the 
noise. Although, because some filters 
change the original value of the pixels, 
some errors, like the precise frame 
location or the loss of spectral 
similar, still the only resource, may 
happen. 
The data generalization after 
classification takes two patterns, 
spacial and thematics. The thematics 
generalization 1s the classes grouping 
to create significant homogeneus 
categories. Because of this, it is made 
with the analyst interference and the 
tendency errors can be introduced and 
information can be lost if the analyst 
352 
does not recognize a unique resource, 
Data Conversion 
By the growth of the Use of ‘the 
Geographic Information Systems and the 
necessity of incorporating digital 
remote sensing data as a quick and 
reliable information source, it was 
inevitable the necessity of converting 
matrix data into vectorial format data. 
Matrix format data are data arranged 
in a symmetrical spacing and same grid 
size. Satellite data are common 
examples of matrix data. These data are 
easily loaded into a computer, as a 
number matrix. 
Vectorial data keep the actual shape 
of a polygon, using series of vertexes 
connected by lines. Vectorial data are 
preferred for the representation of the 
most thematic maps in the GIS, due to 
the uniform lines for the frame viewing. 
Most part of the mapped products, 
including the result of photo- 
interpretation is usually represented in 
vectorial format. 
Unfortunatelly, there is sign- 
ificative error in the conversion from 
matrix to vectorial format and : from 
vectorial to the matrix format. The 
magnitude of these errors depends on the 
algorithm used in the conversion 
process,on the cell grid used and on the 
orientation used for the matrix 
representation. By no considering this 
error it is possible to insert 
considerable problems in the analysis. 
Error Avaliation 
The quantitative error analysis can 
be done during any stage of the data 
processing, including data acquisition. 
Theoretically, an error value must be 
determined after each stage of the 
analysis, but in remote sensing 
projects, the error value ratios are 
determined only after the complete data 
analysis is made and usually directed to 
the thematic and location accuracy. 
In determining the accuracy ratios an 
important point to be considered is the 
sample sizes. The high cost of each 
sample point carts the reduction of the 
sample size to its minimum, but keeping 
enough size to validate any statistic 
analysis. There are several 
recommendations and different equations 
to define the appropriate sample size. 
The sample scheme used is an 
important factor in the accuracy 
evaluation. The error matrix must be 
representative for the whole classified 
image, and the choice of an 
unappropriated scheme (poor) may result 
in a tendentious error matrix, causing 
over or  underestimation of the real 
accuracy. 
It is said that space autocorrelation 
occurs when the presence or the absence, 
or the degree of a certain 
characteristic afects the presence, 
absence, or degree of the same 
characteristic in the neighbor units. 
This condition is particularly important 
in the evaluation of the accuracy for 
positive or negative error influences in 
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