Transcript Software and Tools Overview Dream Ocean Satellite Image

Software and Tools Overview
Dream Ocean Satellite Image Workshop
CH2M Hill Alumni Center, Corvallis, Oregon
August 18-19, 2011
Ichio ASANUMA
The Tokyo University of Information Sciences
Software and Tools Overview
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Tools and operating systems
Data levels and manipulation
Geophysical parameters
Spatial and temporal analysis
Models for spatial and temporal estimate
Models for decision making
Tools and operating systems
• Operating systems and PCs
– Significant improvements of PCs as workstations, desk-top
or notebook computers
• Changes of windows system
– Entertainments to business
– Sometimes slowing our use by version change
• Stable dissemination of linux system
– Commercial to freeware linux system
• Improvement of VMware player
– Realization of two operating system within one computer
VMware player
• Implementation of linux with Vmware player within
windows system
/ drive for
Linux VMware
LINUX operating system
with VMware player software
C drive for
Windows
Windows operating system
Large disk
space
Radiance
data
Geophysical
data
Regional
Instantaneous
Mission oriented
data processing
system
IPOPP, SeaDAS
Global
Time series
Value added
General
image
processing
software
IDL
ENVI
EARDAS
MATLAB
Geographic
Information
System
Sharing roles
in data levels and manipulation
• Mission oriented data processing system
– IPOPP
– SeaDAS
• General image processing software
with geophysical parameters
– IDL, ENVI, EARDAS, MATLAB, etc
• Geographic information system
– ArcGIS
Mission oriented
data processing system
• IPOPP
• SeaDAS
• Future requirements
– Continuity to the future missions
– Share of roles with other processing or GIS systems
General image processing software
• General image processing software works on
geophysical parameters and with geolocation data
– Inputs: GeoTiff data & others
– Processing:
• Spatial composite and/or time series analysis
• Modeling
– Outputs: GeoTiff data
– Future requirements:
• Cost
ExampleApplication
using general
with image
generalprocessing
image processing
software
software
• Asanuma, 2006, Depth and Time Resolved Primary
Productivity Model Examined for Optical Properties of
Water, Elsevier Oceanography Series 73, 91-109.
• Inputs to IDL under SeaDAS
– Chlorophyll-a
– Sea Surface Temperature
– Photo-synthetically Available Radiation
• Outputs from IDL
– Primary productivity
SST
Chl-a
PAR
MODTRAN PAR (June)
PPeu=∫t∫z Pb(z,PAR(z,noon),T) C(z)
PARM(0,t)/PARM(0,noon)dz dt
Primary productivity model
Asanuma 2002
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Vertical distribution of Chl-a and PAR
Vertical distribution of
chlorophyll-a concentration
for 5.0 mg/m3.
0.00
0
1.00
Asanuma 2001
V ertical distribution of C hl-a (EdP A R )
2.00
C hl-a (m g/m 3)
3.00
4.00
5.00
6.00
7.00
8.00
-20
Depth (m)
-40
-60
-80
-100
Vertical distribution of
EdPAR for chlorophyll-a
concentration for 5.0 mg/m3.
-120
-140
-160
0.01
0.10
1.00
E dP A R (% to 0m )
10.00
100.00
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0.05
0.1
0.2
0.3
0.8
1.5
7.5
10
0.4
0.5
0.6
0.7
0.8
1
1.5
2
5
1.5
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1
2
5
Validation of the model
Asanuma 2002
In-situ and satellite estimated primary productivity
Satellite estimated PP
(mgC.m-2.day-1)
2000
Sub-tropic
Sub-arctic
Equatorial Pacific
1500
1000
500
Asanuma model
0
0
500
1000
1500
2000
Version 2002.11
Correlation coefficient
= 0.768
r2 = 0.590
In-situ PP (mgC.m-2.day-1)
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ExampleApplication
using general
with image
generalprocessing
image processing
software
software
• Zainuddin et al., 2006, Using multi-sensor satellite
remote sensing and catch data to detect ocean hot
spots for albacore (Thunnus alalunga) in the
northwestern North Pacific, Deep-Sea Research Part
II 53, 419-431.
• Inputs to IDL
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AVISO mean sea-level anomaly (MSLA)
Chlorophyll-a
Primary productivity
Sea surface temperature
• Output from IDL
– Probability of catch rate of albacore
Albacore CPUE
Albacore/boat-days in 1999-11
on TRMM/TMI SST
(contour on 20 deg-C)
Albacore/boat-days in 1999-11
on an environmental provability map
generated from SST and Chl.
Albacore/boat-days in 1999-11
on SeaWiFS chlorophyll-a
(contour on 0.3 mg m-3)
Geographic information system (GIS)
• GIS approach
– GIS controls layers and provides value added maps
– GIS could be useful in real world decision making.
• Simple approach by GIS
– Boolean logic with un-weighted layers
• Complex decision support by GIS
– Multiple criteria, multiple objectives weighted variable
layers
GIS integration
Primitive
use of GIS
• Global, time series and value addition
– ArcGIS
• Inputs: GeoTiff data/Layer components
• Processing:
– Insert additional information
– Projection etc.
• Outputs: GeoTiff data/Layers
• Future requirements:
>> Connecting effort between RS & GIS
GIS produces value
added maps with GIS
components of points,
lines, or polygons in
conjunction with raster
data and control
capability of layers.
GIS making
integration
Decision
with GIS
• Multi-criteria decision tools
– ArcGIS
• Inputs: GeoTiff data/Layer components
• Processing:
– Multi-criteria/Multi-objective decision making
– Value added data production
• Outputs: GeoTiff data/Layers
• Future requirements:
– Algorithm implementation to GIS software
GIS decision approach
Multi-criteria evaluation (MCE)
• Methodology
– Determination of criteria (factors)
– Normalization of factors / criterion scores
– Determination of weights for each factors
• Analytical hierarchy (AHP) process to calculate weights
– Evaluation using MCE algorithms
– Sensitivity analysis of results
• AHP & MCE are functions of ArcGIS
Application of
GIS multi-criteria evaluation
• Radiarta & Saitoh, 2009, Biophysical models for Japanese
scallop, Mizuhopecten yessoensis, aquaculture site
selection in Funka Bay, Hokkaido, Japan, using remotely
sensed data and geographic information system, Aquacult.
Int. 17:403-419.
• Inputs to ArcGIS
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Sea surface temperature
Chlorophyll-a
Suspended solid bathymetry
Scoring and weighting parameter
• Output from ArcGIS
– Aerial distribution of suitable aquaculture site
Why RS and GIS?
(from JPL-ESRI report, 02, 2011)
• Characterize and understand complex process
using measurements from multiple sources.
• GIS benefits:
– To visualize, analyze, and overlay geo-referenced data
– To access to the actual data values
– To access to a suite of robust analytic tools.
What are the problems that require connecting RS
and GIS?
(from JPL-ESRI report 02, 2011)
• Basic questions:
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Where is data?
How much does data cost?
What data is necessary?
What knowledge is necessary to have for it?
What HW/SW is necessary?
How can we share the way with clients/users?
• For data access:
– How can I gain access and how can I share?
• For analysis:
– How do I go from data to information?
Key gaps or barriers to the use of
RS data by GIS application
(from JPL-ESRI report 02, 2011)
• Many RS users do not need GIS to accomplish
their work.
• Difficulties in integrating raster and vector dataset
in GIS.
• Large volume of RS dataset for GIS.
• Difficulties in integrating GIS software with other
applications.
• Difficulties to deal multi-dimension dataset in GIS.
http://support.esri.com/en/downloads/datamodel/detail/21
Summary
• Hardware and operating system provide more
possibility in further application of satellite data.
• Mission oriented data processing system is the
important function to support geophysical data to
end user.
• General image processing software opens a
possibility to implement new approaches by end
users working on geophysical data.
• GIS provides capabilities to generate value added
map and decision making tools.