International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 
In this study, a method based on logical channel approach is 
presented. Limitations introduced by logical channel method are 
relieved through adjustment of training set so as to provide 
additional sensitivity to ancillary data. Method is applied on a 
selected rural land to extract land cover information. 
Data and Study Area 
A region that shows variety both in morphology and land cover is 
selected near Ankara in central Anatolia. Morphological structure 
is uneven dominated by volcanic mountainous terrain with 
dissected stream valleys; besides there also exist flat regions. 
Superior land cover classes in the region belong to the typical 
continental environment of central Anatolia. Native vegetation is 
mainly rangelands (Anderson et al. 1976), they are composed of 
common steppe vegetation species in central Anatolian regions 
where typical continental climate is prevalent. The native shrubs 
and brushes of the study area are steppe species of maximum 2- 
meter height, distributed densely in the terrain. Herbaceous 
rangelands of the study area are poorly vegetated lands with 
herbaceous plants of maximum 20-30 cm height. In some areas, 
herbaceous rangelands are mixed with the native shrubs and 
brushes of the study area. Moving through north, particular areas 
are dominated with trees composed mainly of coniferous and 
partially of deciduous tree species. Apart from the natural land 
cover, particular land use classes are present in the study area; 
those are primarily agricultural, residential, industrial and 
transportational. 
A subscene of Landsat 7 ETM of May 2000 including bands 
1.2.3,4,5,7 is the primary source of the data analysis. Ancillary 
data is composed of Digital Terrain Model (DTM), slope and 
aspect of the study region. 
A group of data is set apart from the classification and used for 
obtaining ground truth information only. Those data are; IRS 
panchromatic image with 5 m resolution, forest map form General 
Directorate of Forest, digital land cover and land use map from 
General Directorate of Rural Affairs, aerial photograph stereo 
pairs and field observation data. 
Preliminary Data Processing 
1/25000-scaled topographical map served as a basis to 
georeference all available data. Remotely sensed data used in the 
study are free of systematic errors but they have unsystematic 
errors due to alterations in altitude and attitude. Geometric 
correction is made via Ground Control Points (GCPs) obtained 
from topographical map. This procedure is followed by image 
rectification. 
A 30x30 meter DTM was produced from 10-meter interval 
contours digitized from 1/25000-scaled topographical map. 
Consequently, derivatives of DTM; slope and aspect map with 
30x30 meter cell size were generated. 
Classes 
Classification level denotes the level of thematic detail for 
classification. Since the level of classification is dependent on the 
sensor system and image spatial resolution, the level of 
classification for the study was set taking the image's information 
capability into account. Primary data source for the study; Landsat 
7 ETM with 30x30 meters resolution is appropriate for performing 
a first level classification (Jensen, 1996). 
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The land cover and land use categories in the study area are 
composed of five Level I classes which are; 
e Urban and Built-up Land 
e Agricultural Land 
e Range Land 
e Forest 
e Water Bodies 
From the five Level I classes in the study area, two were excluded. 
These classes are Urban or built-up land and water bodies. 
Whereas land cover information can be directly interpreted by 
means of spectral characteristics of an image, additional 
information sources are needed to reinforce the image data in 
order to identify whether the area mentioned is an area associated 
with human activities (Lillesand and Kiefer, 1994). The data is 
usually a thematic map or information regarding the type of use of 
a specific area or construction and often becomes more critical 
than the spectral data. Since the remotely sensed imagery is the 
primary data source for this study, surpass of an ancillary data is 
unacceptable. Other reason for excluding built-up land class is 
related to artificial human effect. Human factor when exceeded a 
trade-off between required development area and present suitable 
area, is often challenging. Land use associated with human 
activities can be practiced anywhere even unusual, regardless of 
the topographical restrictions, but dependent on other parameters 
instead. The reason why water bodies were excluded from the 
analysis is; clear water bodies with distinct and unambiguous 
spectral signatures are the most easily extracted information class 
within a multispectral image, hence there is no need to support 
classification of such water bodies with additional information. 
As a consequence, land cover classes remained are; (1) 
Agriculture, (2) Range Land and (3) Forest. At this point, 
rangelands in the study area were reevaluated, because; rangelands 
of the area obviously consist of two contextually different 
categories, which are herbaceous rangeland and shrub rangeland. 
A subdivision for rangeland category is made although it may 
violate Level I of generally acknowledged classification schemes 
(Anderson et al., 1976; CORINE, 1993). As a consequence of this 
subdivision; ultimate list of land cover ended up with four classes; 
e Agriculture 
e Rangeland-shrub (Range-shrub) 
e Rangeland-herbaceous (Range-herb) 
e Forest 
METHOD 
Image classification for this study aims to convert spectral data 
into four land cover classes. A conventional supervised 
classification algorithm; maximum likelihood is selected. 
Maximum likelihood classifier clusters pixels into information 
classes by means of training data based on probability distribution 
models for the cases of interest. (Favela and Torres, 1998). 
Maximum Likelihood classifier is the most commonly used 
supervised method and is supposed to provide better results 
compared to the other supervised methods (Foody et al., 1992; 
Maselli et al., 1995). 
This study attempts to integrate topographical information into 
conventional supervised classification through particular 
adjustment on the training data. A five-phased methodological 
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PREPROCKESSINC 
BASIC 
ANAL YVYSES 
ACCURACY