Research Information System
ACRS 2015: The 36 th Asian Conference on Remote Sensing “Fostering Resilient Growth in Asia”, Metro Manila, Philippines, 19 - 23 October 2015, pp.1-2, (Full Text)
In the last three decades, the technologies and methods belonging to satellite sensor data have evolved to include a
wide range of imaging scales with potential interest and importance to many disciplines. Satellite sensor imagery for
land cover/use and its changes is a key to various applications such as urban changes, environment, forestry,
agriculture, hydrology, geomorphology and geology, disaster management etc. Natural resource management,
planning and monitoring applications depend on accurate information about the land cover/use for large or small
region. Satellite images enable the analysis of static attributes (such as type and amount) and dynamic attributes
(such as types and rates land cover/use changes) of land use / cover. Moreover, comparison of the recent date
satellite images with up to date images can provide land cover / use change detection with varying spatial resolution.
Satellite sensor images with moderate to various resolutions have facilitated scientific research activities at
landscape and regional scales. Availability of new generation satellite images provide high spatial resolution needs
for analysis of urban growth and transportation development for assessment and monitoring. Moreover,
multispectral bands of remotely sensed data increases the spectral resolution that is beneficial and efficient for
analysis and classification of the environment condition, land cover/use and change detection and urban growth
impacts on these conditions.
In this research, SPOT 4 panchromatic image belonging to 1992 and Landsat 5 TM multispectral image belonging to
2011 were analyzed to determine the land cover /use changes in a part of Istanbul metropolitan area. Data fusion
was applied to images for taking the advantage of high spatial resolution property of SPOT 4 panchromatic image
combined with high spectral and radiometric resolution of Landsat 5 TM multispectral image. Gram – Schmidt
spectral sharpening algorithm was used for data fusion process which uses all bands of the multispectral image as
information in merging. Then, fused image was classified to determine the changes in land cover /use rapidly.
Results showed that fusion of the recent date and up to date images provided reliable data for change detection.
Especially for this study, land cover /use changes in metropolitan area was detected accurately.