International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
database was filled during defining the label-point inside the
boundary. An additional area-layer, a so-called 3d-layer, was
added to take area objects into account, which cover other area
objects. These are namely big bridges and highway tunnels.
Temporal detections have been undertaken by down-dating the
land-use-classes of the 3 layers by going back-wards to 1988,
1965 and 1945. Finally 4 line layers, 4 area layers and 4 3d
area-layers have been developed. Using database-analyses and
GIS-intersections the changes can be detected and quantified.
Fist the entire line layer had to be down-dated by the older
images. Additional line-objects have been added and others
been erased. The database had to be checked if the object
attributes still fit to the legendary. Then the down-dated lines
and the lines of the newer area layer have been imported into a
new empty layer. To import the database just for information,
only the label points with attached area database have been
copied. Then also here the lines have been corrected as
boundary lines. As far as the area and the use was the same, the
area had been created automatically and the attributes taken
from the point database.
Ancillary data processing was as far as possible done in GIS or
with combination of other database software like. A big number
of spatial data have been mapped. These are the topographic
maps 1:25k, city-plans, map of the public transport, DTM,
administrative borders, geological maps and others. A big
number of socio-economic statistics have been pre-processed.
For the validation and pre-calculation of statistical data, MS-
Excel with dBase Ill-output enables geo-coding and
combination with GIS. But we have to be aware that the
combination of spatial data of a specific time, non-spatial,
temporal data, non-spatial regional data and GIS-Data are very
often difficult to do. Some information is just qualitative and
not quantified. Modelling and parameterisation has to be done.
The result of this processing solves questions like: how many
people live in which kind of residential area. It can help to
understand the spreading of the population in Istanbul. Some
initialisation of urban grows are indicated by single objects (like
the Bosporus-bridge) or by changes in the law (legalisation of
some Gecekondu-areas). To point out such facts, is a detective’s
work where the collected data and the land-use change database
helps very much.
4 INTERPRETATION
Key objectives in MOLAND are to quantify the changes in land
use patterns, to explain the trends of growth for the selected
urban areas, and to help in identifying strategies for sustainable
urban and regional development. The extensive data set created
within MOLAND allows handling a series of unique land-
referenced data. Those data are used to build and, particularly,
to test specific spatially referenced indicators. Such indicators
serve several purposes:
« Provide a better understanding of complex territorial
problems
» Provide a sufficiently complete basis for the approaches to
urban and regional spatial planning (particularly regarding
sustainable land use management)
Help city managers and decision-makers in defining local
policies
Provide regional/national authorities and international
institutions with detailed territorial-referenced information at
local and European levels.
100%
90% Bl Water bodies
8094 ;
: El Natural areas (forest,
07.
70% wetlands, etc.)
60% Ll Agricultural areas
50% :
40% Green urban areas
30% El Industrial, commercial &
20% transport services
10% Urban fabric
0% =
1945- 1968 1988 2000
Figure 2:The land-use change by groups of MOLAND Classes
We will give a glance of the statistical operations done on the
land-use data of different years. The graph shows the main
changes in different groups. Grow of residential area (urban
fabric) is strong, mainly between 68 and 88. The same can be
detected for business areas. In same time agricultural area lost
space.
1200 3 —4— Urban fabric
&
1000 - %
L4 :
: P Agricultural areas
800 - 2
E x Industrial, commercial. |
600 & transport services
400 a —# Natural areas (forests, |
E & wetlands, etc.)
200 - X . Green urban areas
| Tq
ER $ 9
= T T > T T X xX T
Water bodies (witheut-
1945: :1955 1965 1975 1985 1995 2005 sea)
Figure 3: Grow of the different groups by linear time-scale and
trend-graphs
Figure 3 shows the change from agricultural to urban land-use.
The time-scale was made linear to enable trend-analyses with
polynomial function of second degree. The trend might be over-
sized but even an effective visualisation of the future. To
combine this data with demographic ones, gives other
indications.
20 - : E — 600
B i—e-mbmm u "7 m
18 = |
| —48-—- residential surface 500
16 N | residential surrace
ul & | :
i5 5 i — 400
= *
iplc "t 300
8.
; + -.200
6. +
+
: 100
2 da e +
0 + * + : 7 0
S S A S S S S S S
Figure 4: Growth of population and residential surface
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