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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 
  
framework was proposed for developing a procedure for the 
integration (Figure 1). 
  
GROUND TRUTH 
PRIMARY DATA ANCILLARY DATA RS Pancomatie 
LANDSAT bands Topographical parameters Land Es map 
— d Aerial Photo 
  
   
Field Observation 
geometric correction of 
minor data 
geometric correction of 
topographical map 
: I. 
i {oem of | 
;| Digital Terrain Mode! (DTM) || 
i |derivation of| derivation o | 
i ASPECT SLOPE | 
| 1 
   
  
geometric correction 
of band 1.2,3,4,5,7 
1st Level Classification 
Classes: Agriculture, Forest, Range-shrub, Range-herb 
Classification method: Maximum Likelihood 
selection of class 
spectral signatures 
Ti spectral 
PREPROCESSING 
  
  
BASIC 
DEFINITIONS 
  
Pi 
  
  
    
   
  
   
   
   
  
    
   
  
       
  
  
  
  
  
  
No 
«a correlation Correlation 
w 
a correlation 
_ 
el] e DTM and SLOPE remained 
«| c En 
E selection of class 
7. topographical signatures 
< Ti topographical 
» selection of T1 selection of T2 
| CLASSIFICATION | CLASSIFICATION | 
us ! Input bands: Input bands: | 
12,34,5,7, | 
a | DTM SLOPE | | 
| 
| 
- mea © 
Jy | 
zu 
Ol = 
zi test with 
SIG Ground truth data 
con 
à 
«x 
Figure 1: General Framework of the Study 
First phase: Procedures involved basically involves 
understanding class spectral characteristics. A certain time was 
devoted to understanding visual components of land cover classes 
in the study area making use of particular band combinations and 
other reference data. Training samples were selected for all classes 
overall the image, ensuring that they are good representatives of 
each information class. Selected training set was tested both for 
seperability and representativity, if not satisfied with the results; it 
was modified and tested again. This procedure continued since a 
balance between sample size and sample error was supplied. A 
Training Set Dendogram is used to obtain the results of a 
hierarchical analysis of the class signatures in graphic form 
(Figure 2). The spectral seperability of signatures were tested by 
"Transformed Divergence". 
Separability 
9.00 9.50 0.99 1.43 1.98 
[]11 (36.63x) 
2 2 (24,423) 
EN SET TES 
M 4 4 « 6.74%) 
Training Class 
  
  
  
Figure 2: Seperability of Initial Training set by means of Transverse 
Divergence measurement (1) Agriculture, (2) Range-shrub, (3) Range- 
herb, (4) Forest 
Class spectral signatures compose the initial training set for thc 
multispectral image data. However, this set is not used for training 
the classification procedure, rather it serves as prior information 
for the later redefinition of training data. 
Second phase: Quantification of the relationship between land 
cover classes and topographical parameters; elevation, slope and 
aspect is involved. Dependening on the significant relationships, 
ancillary topographical data that may contribute to improvement 
of classification accuracy is determined. 
Four land cover classes and the topographical parameters were 
tested for correlation. Land cover data involves training samples 
of land cover classes; agricultural land, range-shrub, range-herb., 
and forest. Those samples have been collected randomly from all 
over the study area and are spectrally good representatives of their 
associated classes, so, they formed an adequate test set. 
Topographical data merely involves the pixel values spatially 
corresponding to spectral training samples. 
Point Biserial Analysis is performed for quantifying the 
correlation between topographical parameters (interval scale) and 
land cover classes (dichotomous scale). The correlation 
coefficients obtained ranged between minimum of 0.02 to 
maximum positive of 0.65, and maximum negative of 0.41 (Table 
1); where 0 denotes there is no correlation, 1 is perfect correlation 
and -1 is perfect negative correlation. 
  
  
  
  
  
Topographic Land cover Correllation 
Parameter class Coefficient 
Elevation agriculture 0.62 
Slope agriculture 0.65 
Aspect agriculture 0.02 
Elevation range-shrub -0.34 
Slope range-shrub 0.48 
Aspect range-shrub -0.11 
Elevation range-herb. -0.41 
Slope range-herb. 0.08 
Aspect range-herb. 0.06 
Elevation forest 0.1 
Slope forest -0.5 
Aspect forest 0.24 
  
  
  
Table 1: Point Biserial Correlation coefficients for four land cover 
classes and topographical data 
The result of the point biserial correlation analysis indicated the 
relation between specific land cover classes and the topographic 
parameters. The significance test verified that correlation 
coefficients greater than approximately 0.30 are significant. 
Significance level, often called the p value is the probability that a 
statistical result as extreme as the one observed would occur if the 
null hypothesis were true. 
As a consequence of the point biserial correlation analysis; aspect 
parameter with very low correlation coefficient was incidentally 
excluded from the remaining part of the study. Elevation and slope 
data were quantified for use as ancillary input for classification. 
Third phase: Ancillary topographical data; elevation and slope 
were examined for topographical signatures selection. A 
procedure similar to that performed in the first phase was carried 
on. However, this time the aim 1s to define the representative sets