KOBAYASHI, Shoko
G-COE Researcher
- Division of Humans and the Environment
- Environmental Science
- Ph. D. (Bioresources), Graduate School of Bioresources, Mie University,
2008
Current Research Interests
- Spatiotemporal Analysis of Remote Sensing Data using GIS
- Environmental Monitoring & Assessment
My major research topic is “environmental analysis using GIS (Geographical
Information System) and Satellite Remote Sensing”. Satellite data can provide
information about not only ground surface (reflectance, evaporation, land-use/land-cover),
but also about water surface (water quality, sea surface temperature) and
atmosphere (stratospheric ozone). This is strongly beneficial and essential
for monitoring, assessment or modelling the environmental ecosystems. The
analysis of satellite data on GIS platform constitutes the main part of
my research, because GIS enables to deal with various data including ground-based
observation, on-survey investigation data, air-photo and a broad range
of digital and statistical data. I will try to implement time-spatial and
statistical analyses of those data in a comprehensive manner, so as to
perform interdisciplinary investigation in figuring out the state of, and
changes in the environment, and in assessing the environmental impacts,
both at the local and the global scales.
Research Activities in 2008 Fiscal Year
Publication | Joint Research Project | Field Research |
Seminar/Symposium | Database | Academic Association |
Outside Activities | Award
- Publications
-
- Kobayashi S.; and Sanga-Ngoie K. (2008a) “The integrated radiometric correction of
optical remote sensing imageries”, International Journal of Remote Sensing
(in Press).
- Kobayashi S.; and Sanga-Ngoie K. (2008b) “A Comparative study of radiometric correction
methods for optical remote sensing imageries: the IRC vs other image-based
C-correction methods”, International Journal of Remote Sensing (in Press).
- Sanga-Ngoie K.; and Kobayashi S. (2008) “Assessment of the Integrated Radiometric Correction (IRC) method
by comparison with prior image-based methods for optical remote sensing
data”, Proceedings of the 44th Spring Conference of the Remote Sensing Society of Japan, (accepted).
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