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RESEARCH
SEMINAR:
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----The second research seminar at Tokio Marine & Nichido
Fire Insurance Co. Ltd on Oct 20th, 2006
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Presentation
by Dr. Seita EMORI from National
Institute for Environmental Studies
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Presentation by
Dr. Masaru INATSU from University of Tokyo
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Presentation by
Mr. Satoshi SUGIYAMA from NTT Energy and Environment Systems Laboratories
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Presentation by
Mr. Tomohiro UEZONO from Tokyo Marine & Nichido Fire Insurance Co., Ltd
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Presentation
by Mr. OKAZAKI from The Tokyo Marine Research Institute 
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----The first research seminar at ITOCHU Corporation on June
12th, 2006
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Presentation by
Dr. Yasuhiro YAMANAKA from Hokkaido University
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Presentation by
Dr. Masaru INATSU from University of Tokyo
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Presentation by
Mr. Satoshi SUGIYAMA from NTT Energy and Environment Systems Laboratories
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Presentation by
Mr. Tomohiro UEZONO from Tokyo Marine & Nichido Fire Insurance Co., Ltd
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RESEARCH
FOCUS:
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----Modelling climate-change impacts on Chinese agriculture by
EPIC model and MIROC data
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The impact of climate change in china is expected to be
considerable. The model for Interdisciplinary Research on
Climate (MIROC), developed by CCSR, was used to simulate China's
climate and to develop climate change scenarios for the country.
Regional crop models (GIS-based EPIC model) were driven by the
simulated climate data from MIROC to predict changes in yields
of key Chinese agricultural crops: rice, wheat, corn and cotton.
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----Influence of regional scale information
on the global circulation: a two-way nesting climate simulation
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----Linking
multi-temporal remotely sensed data, field observations and
GIS-based crop growth model for crop yield assessment
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Accurate and timely estimates or prediction of crop production
in regional scale is critical for many applications such as food
security warning system, agricultural lands management, food
trade policy and carbon cycle research. Remote sensing is the
only means to monitor the spatial and temporal variability of
crops at various scales. However, up to now only the primary
biophysical variables of the canopy or soil can be derived
directly from remotely sensed data, and cannot be used directly
for crop management decision making in many case. It has been
well recognized that the current satellite sensors have limited
direct applications because of the few spectral bands, coarse
spatial resolution, and inadequate repeat coverage.
Remote
sensed variables have to be integrated with ancillary data, such
as soil, weather, and the past management practices, to provide
higher level information that is pertinent to making all
strategic, tactic, and operational decisions through a crop
decision support system (DSS). Crop
growth models are the core of any crop DSS.
Assimilating remotely sensed data into crop growth model for
monitoring crop condition and forecasting crop yield in regional
level represents an important research direction in precision
farming and quantitative remote sensing.
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OTHERS:
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