The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beiiing 2008 
Figure 1. Location of study area 
2.2 Methods 
To verify accuracy of the results, a master landscape map was 
created based on field surveys and aerial photographs (Figure 4). 
Taking into consideration the local topography and land-use 
patterns, the following 11 landscape types were identified. 
1. Conifer Plantation 
2. Evergreen Broad-leaved Forest 
3. Deciduous Broad-leaved Forest 
4. Bamboo Grove 
5. Grassland 
6. Wetland Vegetation 
7. Paddy Field 
8. Bare Ground 
9. Rural Residential 
10. Urban Residential 
11. Open Water 
IKONOS data (Japan Space Imaging - multi-spectral 
resoulution 4 meter, panchromatic resolution 1 meter) acquired 
on 23 April 2001 was utilized (Figure 2). In addition, 
anticipating the role that topographic elements would play in 
the analyses, the local topography was surveyed in the field, 
and a topographic map was traced to form a GIS data base 
showing the boundary between the valley bottom lowlands and 
the slope (Figure 3). This division between the lowlands and 
the uplands (including the valley slopes), was incorporated in 
the initial process of segmentation and object-based 
classification. 
Figure 2. IKONOS true color image of study area 
Figure 3. Lowland boundary based on topographic data 
Red line: the boundary between valley bottom and slope 
HUla Conifer Plantation 
Mi Evergreen Broad-leaved Forest 
Deciduous Broad-leaved Forest 
Bamboo Grove 
I I Grassland 
|U&li Wetland Vegetation 
I 1 Paddy Field 
] Bare Ground 
I i Rural Residential 
lim Urban Residential 
HH Open Water 
Figure 4. Landscape map (Master map) 
Blue area shown in expanded view in Fig 8 
This classification employed the system developed by 
Kamagata et al (2006), using Definiens Ver.5 software 
(Definiens). Initial segmentation was a muli-resolution, 
bottom-up system based on the method of Baatz and Schape 
(2000). The panchromatic data was used only in the 
segmentation processing. In object-based classification, object 
size, shape and other parameters can be adjusted to fit the needs 
of the research. Texture and color of the image data were used 
to classify each unit, and integration of areas was accomplished 
by increasing the scale parameters. A scale parameter of SP=66 
was decided on. The study area was divided by segmentation 
processing, and each segment identified was considered to be 
one object. 
This research also integrated topographic data into the 
classification. The results of the above segmentation process 
were first classified into lowland and other terrain types based 
on the topographic data, using a higher scale parameter. Based 
on these classification results, landscape types were divided 
into three categories; those found only in the lowlands; those 
found only on the uplands, and those found on both. 
Classification criteria were established using the decision tree 
shown in Figure 5. Each landscape type was allocated into the 
hierarchy correlated with the proper terrain element. Landscape 
maps, field surveys and aerial photographs were used to 
establish the ground truth and set training data; and the 
classifications were implemented using the nearest neighbour 
method based on the mean value of each object.