Download I-1: PROPOSAL TITLE

PSU Research Proposal
Title: Satellite Data
Mining: Mining
Environmental Satellite
Data- An Application on
Locating Potential Water
Resources
Department:
Computer Science
PI Name: Ahmed
Sameh
Duration: 2 years
Budget Est: SR 79,000
Date: 12/20/2010
0
I - PROPOSAL
I-1: PROPOSAL TITLE (Provide a short descriptive title, give prominence to keywords)
Satellite Data Mining: Mining Environmental Satellite Data- An Application on Locating
Potential Water Resources
I-2: COMMERCIAL POTENTIAL
Yes
Could this project have commercial potential? (Select one)
No
 If yes, briefly elaborate on the commercial potential
The Saudi Geological Survey in Jeddah as well as the Saudi Military Geological Survey in Riyadh
might be interested in acquiring the developed system. The methodology proposed in this project
is new; we could apply for a patent after proving its success.
I-3: CHECK-LIST
Have you checked to ensure all questions in the application form have been answered?
Have you checked to ensure you have included the correct costs in your budget?
The principal investigator and all co-principal investigators should sign.
I-4: PERSONNEL AND AUTHORIZATION
PRINCIPAL INVESTIGATOR [PI]
Academic Rank: Professor
College:
Telephone:
Full Name: Ahmed Sameh
Department: Computer Science
CIS
494-8524
Ext: X8524
Mobile: 05044299846
E-Mail: [email protected]
Signature:
CO- INVESTIGATOR(S) [CIs]
1)
Date:
(non-PSU CIs permitted)
Full Name: Mohamed Sultan (Western Michigan University)
Academic Rank: Professor
College:
Telephone:
E-Mail:
Department:
Mobile:
1
12/20/2010
Signature:
2)
Date:
Full Name:
/
Date:
/
/
Date:
/
/
Date:
/
/
Full Name: Fakhry Khellah
College:
E-Mail:
Department: Computer Science
CIS
Telephone:
Mobile:
Signature:
Date:
Full Name:
Khalid Al-Mustafa
Academic Rank: Assistant Professor
College:
CIS
Telephone:
E-Mail:
Department: Information System
Mobile:
Signature:
Full Name:
Academic Rank:
College:
Telephone:
Ayman Kassem (King Fahd University)
E-Mail:
Department:
Mobile:
Signature:
6)
/
Date:
Academic Rank: Assistant Professor
5)
/
Mobile:
Signature:
4)
/
E-Mail:
Department: Computer Science
CIS
Telephone:
3)
/
Rashid Zantout
Academic Rank: Associate Professor
College:
/
Full Name:
Academic Rank:
College:
Telephone:
E-Mail:
Department:
Mobile:
Signature:
2
II - DESCRIPTION
II-1: ABSTRACT (Provide a statement of the project - maximum 200 words)
This project explores the area of “Multi-Media Data Mining”. In particular “Satellite Images” are usually the
target of traditional “Remote Sensing” research. Satellite data mining will expand on traditional image
remote sensing operations to reveal hidden knowledge embedded in such rich collection of satellite images
utilizing text background knowledge databases. The research will target Saudi Arabia’s needs from mining its
satellite images. It will serve some important necessities such as: military boarder surveillance, monitoring
‘hot spots’, identifying exposed artifacts by international rooming spay satellites, Oil and mineral resource
exploration, monitoring oil spills, classification of vegetation regions, discovering both above and
underground waterpockets, monitor Atmospheric conditions (e.g. water vapor, wind patterns, air
temperature), predict climate changes, and explore phenomena such as “Tashahor”, “Flooding”, and Cities
Urban planning. The project will deliver an integrated system developed on top of standard platform of fast
Oracle databases of Saudi satellite images composed of its different regions at different resolutions supported
by clever images’ indexing techniques. It will also build a powerful library of algorithms on top of standard
Matlab image processing toolbox using GIS data. The integrated system will be unique in targeting the above
Saudi’s needs in such comprehensive and cross-correlated manner mixing both images databases and text
background knowledge databases. Previous research in remote sensing applies specific algorithms on images
for specific purpose. The proposed system correlates different needs and discover inter-relations that never
been investigated before through data mining at different fronts (both inter- and intra-). For example,
correlating “Tashahor” with wind patterns and soil moisture prediction will make use of three types of images
databases (Tashahor, winds, and soil) along with a text background knowledge database that stores previous
knowledge about these issues specific for Saudi Arabia. Such unique feature of the proposed system is close
to integrating “Remote Sensing”, “Geographical Information System” with “Data Mining”. To our
knowledge such federation of databases and techniques never been investigated before. The project will focus
its scope to one area at a time. As a starter we will explore the rich area of Saudi geological data, background
knowledge, and specific hydrologic investigation queries. Domain-specific experts and historical knowledge
will be the main source of background text data. The Saudi Geological Survey (SGS) agency will be the main
source of images and text data. One of the investigators is working in a research project investigating Land
Slides near Jeddah. He also has been working on the Empty quarter in a previous research project. Other
image data will be collected and/or purchased from commercial Internet satellite agencies. At this point in
time, the SGS is providing us with data about Al Robh El-Kaly (Empty Quarter). Data about annual
precipitation, field region gauges, and aquifer characteristics of rocks at this area are provided. In summary,
this project will deliver both a new methodology (with an implemented prototype), as well as new results in
the area of application of the methodology.
3
II-2: PROJECT GOALS AND OBJECTIVES
The main objectives of this project is to build a Saudi Multi-media data mining architecture that is open, light
weight, and distributed relying on standard databases and data mining techniques. The end product should be
able to answer both Intra-Correlation queries and Inter-Correlation queries. In answering these queries, we
target the following goals and objectives:
1- Better understanding of the interplay of mixing remote sensing operations with background knowledge
2- Effective decoupling of the data mining algorithms, background knowledge, and the image databases
3- Efficient acceleration of the federation of databases for fast data accesses
4- Flexible scripting of the queries within the architecture to implement an open system
5- Distributed architecture (Client/Server) with web support
6- Use of standard databases and remote sensing operations
7- Building solid and reliable supporting infrastructure centered around users’ needs
8- Maintaining security and privacy issues
9- Monitoring System requirement changes
10- Ensuring scalability and abstraction of functionality
11- Assure effective responses to both Intra- and Inter- Correlation queries
12- Ensure efficient deployment (minimal client installation and on-demand dynamic loading of components)
12- Provide appropriate visual results interpretation
13- Provide several scenario based validations (openness, completeness, and efficiency)
14- Ability of future porting of the client side to other platforms (e.g. Mobile)
15- Support for future advanced data mining algorithms that can for example combine semantic
interpretations and retrieve/correlate satellite image patches
16- Deliver military queries such as observe deployment of military forces, weapons development,
assessment of damage caused by bombs and also provide intelligence on enemy capabilities, nuclear
compliance and missile launches
17- Support more complex queries that involve automatic semantic categorization of images (higher-order
semantic that focus on both spectral and spatial dimensions)
Through out this research we will be targeting two categories of characteristics through which we can define
various measures of success. The two categories of characteristics are: measurable functionality
characteristics and measurable performance characteristics. The implemented architecture should provide the
following characteristics (measurable functionalities):
1- Easy entry of collected data; both images and background knowledge
2- Transparency of correlation details from the web client point of view
2- Flexible formulation of both Intra- and Inter- Correlation queries
3- Effective and efficient graphical user interface
4- Practical visualization display of results
5- Capability of low level investigation of low and intermediate operations
6- Exposing faced difficulties en route of answering queries
As for the performance non-functional expectations:
1- Easy single point of service deployment
2- Openness and scalability through supporting addition of new data mining methods and/or databases to the
system
3- Better utilization of bandwidth and speeds of data transfer over the network
4- Security and privacy issues
5- Light weight system implementation
6- Flexibility to handle diverse applications (e.g. military, exploration)
4
III - INTRODUCTION
III-1: REVIEW AND ANALYSIS OF RELATED WORK
Multi-media data mining is more general than traditional data and text mining. It deals with a combination of
text, audio, still images, animation, video and interactivity contents. In this project we limit our scope to only
text, and still satellite images. Data mining of such contents is building on top of remote sensing capabilities
that same way that computer vision research builds on top of low level image processing capabilities. Remote
sensing is defined as the science and technology by which the characteristics of objects of interest can be
identified, measured or analyzed without direct contact. Remote sensing replaces costly and slow data
collection on the ground, ensuring in the process that areas or objects are not disturbed. The most established
markets in remote sensing are in the areas of natural resources such as exploration of oil, gas,
nd minerals; military spying, classification of vegetation regions, discovering underground waterpockets,
monitor Atmospheric conditions (e.g. water vapor, wind patterns, air temperature), and predict climate
changes. There are three main problems with remote sensing which are: (1) the enormously growing volume
of imagery (e.g. Multi/hyper spectral of different formats) with lots of time consuming preprocessing; (2)
they allow only for simple queries of sensory data; and (3) they lack efficient retrieval of useful information
since there is lots of complex spatial/tem-oral associations. The proposed project will resolve these traditional
problems and will expand on image remote sensing operations to reveal hidden knowledge embedded in such
rich collection of satellite images of Saudi Arabia utilizing text background knowledge databases. Text
background knowledge databases are databases that store relevant background knowledge specific to Saudi
Arabia with regards to the specific investigation area. For example, previously known knowledge about
“Flooding” regions in the Kingdom and the previous experience gain from such historical events are
collected and coded into such text background databases. As another example, oil spills occur both in the
open sees and along the coasts, so background information about major shipping routes and anchorage areas
will help in locating potentially vulnerable areas. Similar information about gas, and minerals; military
boarder previous events, information about ‘hot spots’ such as holy places, knowledge about classification of
vegetation regions in the Kingdom, information about both above and underground known waterpockets,
background information about previous Atmospheric conditions (e.g. water vapor, wind patterns, air
temperature) in certain regions at certain times, and background knowledge about “Tashahor” areas are all
collected and coded in the text background databases.
The proposed system will be unique in targeting the above Saudi’s queries in such comprehensive and crosscorrelated manner mixing both images databases and text background knowledge databases. Previous
research in remote sensing applies specific image processing algorithms such as image registration, and
object recognition on images for specific purpose. The proposed system will correlate different queries and
discover inter-relations between image and text data that never been investigated before through data mining
at different fronts (both intra- and inter-). Intra-Correlations means cross-correlating images databases and
text background databases for a specific type of queries. For example in exploring new underground
waterpockets in “Aseer” area, the proposed system will mine collection of “Aseer” satellite images for
waterpocket detections (certain resolutions with specific filters), and text background knowledge databases
related to this issue. Inter-Correlation means cross-correlating heterogeneous images databases and text
background databases to answer mixed-type queries. For example, answering queries about the relationship
of “Tashahor” with wind patterns and soil moisture in “Aseer” will make use of three types of images
databases (Tashahor, winds, and soil) along with a text background knowledge database that stores previous
knowledge about these issues specific for “Aseer”. Such unique feature of the proposed system is close to
integrating “Remote Sensing”, “Geographical Information System” with “Data Mining”. For example, how
pre-classification image segmentation will interplay with lower resolution background knowledge? To our
knowledge such federation of databases and techniques never been investigated before.
Existing research prototypes that are similar to the proposed systems are: (1) The ITSC Algorithm
development and Mining (Adam) System [5]; (2) NASA JPL Diamond Eye System [6]; (3) DLR Intelligent
Satellite Information Mining System [7]; and (4) The Insightful VisiMine System [8]
ADaM was developed at the University of Alabama in response to the need to mine large scientific data sets
for geophysical phenomena detection and feature extraction. It provides knowledge discovery and data
mining capabilities for data values, as well as for metadata, and catalogs the information discovered. It
contains algorithms for detecting a variety of geophysical phenomena to address the needs of the Earth
Science community.
5
Diamond Eye , developed at Jet Propulsion Lab, California Institute of Technology, is an image mining
system that enables scientists to locate and catalog objects of interest in large image collections. This system
provides a platform-independent interface to novel image mining algorithms, as well as to computational and
a database resources that allow scientists to browse, annotate and search through images and analyze the
resulting object catalogs.
The German Aerospace Center DLR has created an intelligent satellite information mining system as a next
generation architecture to help users to gather rapidly information during courses of actions. This is a tool to
add value and to manage the huge amount of historical and newly acquired satellite data-sets by giving to
experts access to relevant information in an understandable and directly usable form and to provide friendly
interfaces for information query and browsing.
VisiMine is an interactive mining system for image databases. It is developed by the Insightful corporation in
Seattle. VisiMine is a system for data mining and statistical analysis of large collection of remotely sensed
images. It provides an environment for high-interaction graphical analysis of multivariate data, modern
statistical methods, data clustering and classification, and mathematical computing.
None of the above research prototypes supports the proposed integrated Intra- Inter- correlation queries or
makes use of the background knowledge. The proposed project use the methodology of “Data Fusing” to
bring together multiple sources of data and apply data mining algorithms in order to reveal hidden and
inferred knowledge.
III-2: SIGNIFICANCE OF WORK
Findings: This project takes the field of remote sensing to higher grounds by applying knowledge discovery
techniques of data mining utilizing a set of background knowledge. Intra- and Inter- Correlation queries and
the federation of both images and text databases are new concepts in the field of remote sensing. It is our
hope that applying such new concepts will lead to interesting finding at the various areas of Saudi’s needs.
The project will deliver a prototype implementation of the proposed architecture. The prototype will have all
the functional and non-functional specifications listed in this proposal. A number of Intra- and InterCorrelation queries will be supplies within the prototype in the areas of military boarder sullivaliance,
monitoring ‘hot spots’, identifying exposed artifacts by international rooming spay satellites, Oil and mineral
resource exploration, monitoring oil spills, classification of vegetation regions, discovering both above and
underground waterpockets, monitor Atmospheric conditions (e.g. water vapor, wind patterns, air
temperature), predict climate changes, and explore phenomena such as “Tashahor”, “Flooding”, and Cities
Urban planning.
The Saudi Geological Survey in Jeddah as well as the Saudi Military Geological Survey in Riyadh might be
interested in acquiring the developed system. The methodology proposed in this project in new, we could
apply for a patent after proving its success.
Publications:
A number of research publications are expected out of this research: A journal paper plus at least two
conference papers. Also a number of research presentations will be produced.
This research introduces data mining and knowledge-based guidance in remote sensing of images. It presents
an integrated remote sensing image information mining framework and proposed a prototype
implementation. It shows how to evolve the prototype into a practical tool and how to apply it into specific
applications (e.g. agricultural and environmental monitoring) when enough data is available.
IV - APPROACH AND METHODOLOGY
IV-1: METHODOLOGY
6
The project will deliver an integrated system (See figure) developed on top of standard platform of fast
Oracle databases of Saudi satellite images composed of its different regions at different resolutions supported
by clever images’ indexing techniques. Oracle is a powerful database management system that can deal
efficiently and effectively with both text and image data. We will use image enhancement add-on of Oracle
(plug-in of Oracle) to accelerate both storage and retrieval of the large collection of images.
The client’s GUI provide functionality for Queries, Browsing, and visualization. Indexing techniques are
implemented for both images and text knowledge databases. The data mining algorithms such as: Principal
Component Analysis (PCA), texture feature extraction, clustering, and spectral analysis algorithms for land
cover classification are implemented as Matlab scripts on top of its image processing toolbox.
Oracle add-on imagining provides various indexing techniques such as spatial indexes (e.g. Quadtree, Octree,
Grid, X-tree, and R-tree) for efficient image retrieval. In the future content-based retrieval may be
investigated to semantically chunk patches of images and cluster them by category, proximity, temporally.
The server will build a powerful library of algorithms on top of standard Matlab image processing toolbox.
The library will be basically a set of Matlab scripts that implements main remote sensing functions. For
example, time series analysis of time stamped sequence of images, images registration and spatial
transformation, images classification, object recognition, morphological transforms, and images analysis and
statistics. For example detecting changes in images over time. These differences may be due to actual change
in land cover, or differences in illumination, atmospheric conditions, sensor calibration or ground moisture
conditions. This relates to image registration with post-classification comparison, multi-date classification,
image regression, image rationing, principal components analysis, and change vector analysis.
Background knowledge databases are resembling GIS systems’ information but with artificial intelligence
flavor of reasoning and deduction capabilities. These text data will be stored in object-oriented databases and
will store specific information about Saudi Arabia. For example background information about Saudi’s
boarders strong points, locations of ground surveillance troops, and types of military equipments distributed
at these locations. Also background information about previous historical floods in Saudi Arabia with
statistical information and experiences gained at these time. History of previous climate changes happened
through the Kingdom with background information about the reasons and the effects of such changes.
The server will utilize some data mining algorithms such as image classification, clustering, association and
forecasting algorithms. For example Self-Organizing Map (SOM) clustering can be used for pre-processing
of images to produce more informed segmentation for complex images. K-means clustering can be used to
partition the texture feature space into subspaces in terms of the various image classes. Data mining
classification can be used to produce better indexing techniques for the huge number of images. Association
rules and decision trees can be used to correlate shape features, and spatial-temporal relationships.
The proposed system is based on standards components: Oracle, Matlab, and Data Mining tools. Saudi
satellite images and text data will be collected from the Saudi Geological Survey agency in Jeddah [4]. Other
background data and images will be collected and/or purchased from commercial sites on the Internet. As
such the project’s feasibility has been proven.
Graphical User Interface
Image
Repository
PCA
Land Cover
Classification
Texture
Feature
Extraction
Browsing / Query
Databases Module
ObjectOriented
Database
Clustering
Image
Database
Image Processing Module
Figure 1: One possible function of the proposed System that combines DM + RS + GIS + Background
Knowledge
The project will focus its scope to one area at a time. As a starter we will explore the rich area of Saudi
geological data, background knowledge, and specific hydrologic investigation queries. Domain-specific
experts and historical knowledge will be the main source of background text data. The Saudi Geological
7
Survey (SGS) agency will be the main source of image and text data. Other image data will be collected
and/or purchased from commercial Internet satellite agencies. At this point in time, the SGS is providing us
with data about Al Robh El-Kaly (Empty Quarter). Data about annual precipitation, field region gauges, and
aquifer characteristics of rocks at this area are provided. In summary, this project will deliver both a new
methodology as well as new results in the area of application.
Figure 2: High Level System Architecture of the proposed system
We will then move to define a wide scope of the proposed system, then designing and building the main
architecture, collect relevant images and knowledge data in order to populate the databases, then start testing
the system’s functionality through a number of Intra- and Inter- Correlation queries. We then investigate the
performance characteristics. Finally narrowing the project focus to a specific area of investigation (e.g.
military, climate, oil, vegetation) will depend on the rich data that we will find to allow such deeper
investigations.
Scope:
The project will deliver a prototype implementation of the proposed architecture. The prototype will have all
the functional and non-functional specifications listed in this proposal. A number of Intra- and InterCorrelation queries will be supplied within the prototype in the areas of military boarder surveillance,
monitoring ‘hot spots’, Oil and mineral resource exploration, monitoring oil spills, classification of
vegetation regions, discovering underground water pockets, monitor Atmospheric conditions (e.g. water
vapor, wind patterns, air temperature), and predict climate change. As time passes, the project scope will
focus further. As a starter we will explore the above wide areas (military, oil, vegetation, climate) until we
stumble into a rich area with data, background knowledge, and specific investigation queries.
Work breakdown structure- Deliverables:- who does what- see the Gantt Chart below
System requirements specifications: Sameh
System Architecture : Sultan
System Design: Sameh, Rashid
Databases Designs: Khalid
8
Matlab Scripts Design: Fakhry
Prototyping of critical sub-systems: Rashid, Sameh
System Detailed Design: Sameh, Sultan
Beta Version Implementation: Sameh, Khalid
Testing: Fakhry
Building Deployment Environment: Rashid
Bench Marking and Collecting Results (First Round): Sameh
System Tuning (Based on First Round Results): Khalid, Ali
Bench Marking and Collecting Results (Second Round Results): Fakhry
System Tuning (Based on Second Round Results): Sameh, Faisal
Bench Marking and Collecting Results (Third Round Results): Rachid
Version 1 Release
Results Documentation and Analysis with the Performance requirements
Detailed Code Documentation: Faisal
User and Installation Guide (Full How To): Ali
Methods: Saudi Satellite images will be collected from the Saudi Geological Survey Agency in Jeddah [4],
and from the Internet. There are many sites that sell satellite images (see [1][2][3]). There are various types
of satellite images. For example, visible imagery are day time pictures that show clouds as white, the ground
as grey, and water is darker. Terrain features such as rivers are lakes can be identified. Thunderstorm cloud
building can be detected. Infrared imagery measures heat radiation off surfaces. Whereas Water vapor
imagery measures moisture in the atmosphere. They also come at different resolutions (pixel size represents
surface area measured on the ground), different spectral resolutions (wavelength interval size on the
Electromagnetic spectrum), and different radiometric resolutions (several levels of brightness). It is important
to choose the image types that are suitable for the task under consideration.
Background Saudi information to populate the knowledge bases will be collected from various sources such
as ministries, the Saudi geological Survey Agency, and government information systems. As for the exposed
areas of Saudi Arabia that are exposed to spy satellites, we will collect information about currently rooming
satellites on top of the Kingdom. We will store information about the payload of each satellite and its spying
capabilities. The system will mine these information to answer queries about “what others are looking at?”
Data collection expected to take an extended period of time since these data are distributed at many places.
The project will focus its scope to one area at a time. As a starter we will explore the rich area of Saudi
geological data, background knowledge, and specific hydrologic investigation queries. Domain-specific
experts and historical knowledge will be the main source of background text data. The Saudi Geological
Survey (SGS) agency will be the main source of image and text data. One of the investigator is working in a
research project investigating Land Slides near Jeddah. He also has been working on the Empty quarter in a
previous research project. Other image data will be collected and/or purchased from commercial Internet
satellite agencies. At this point in time, the SGS is providing us with data about Al Robh El-Kaly (Empty
Quarter). Data about annual precipitation, field region gauges, and aquifer characteristics of rocks at this area
are provided. In summary, this project will deliver both a new methodology as well as new results in the area
of application.
9
Texture Feature Extraction

Texture feature representation

A two-dimensional Gabor function and its

statistics model
Fourier transform
2 


 2


1
 co-occurrence matrices
 exp  1  x  y   2jWx 
g ( x, y )  
2
 2 x y 
 y2 
 2


probability model


x


 Markov random fields parameters

v 2 
 1  (u  W ) 2


transform-based model
G(u , v )  exp 
 2 
 u2
 v 
 2
 Gabor wavelets



Feature representation
 Gabor wavelet transform of an image (PCA 1 region)

Wmn ( x, y )  I ( x1 , y1 ) g mn
( x  x1 , y  y1 )dx1dy1


mean and standard deviation of the magnitude of the coefficients

feature vector
 mn ( x, y ) 
 W
mn
( x, y ) dxdy
 mn ( x, y ) 

 W
f   0, 0 0, 0  0,1 0,1   S 1, K 1 S 1, K 1
mn
( x, y )   mn
 dxdy
2

Category-based Clustering

Partition the texture feature space into subspaces in terms of the
combined land cover classes
Open Water
Woody/Emergent Wetlands
Grasslands/Herbaceous
Pasture/Hay
Deciduous/Evergreen Forest
Row Crops/Small Grains/Fallow
Bare Rock/Sand/Clay
Residential/Industrial
water/wetlands river/grassland forest/pasture crops/pasture urban/grasslands
10
The above three figures demonstrate the details of figure 1 above. They show the type of interaction between
data mining algorithms of classification and cluster with the image processing algorithm of texture feature
extraction with background knowledge about vegetation areas.
IV-2: AVAILABLE RESOURCES
Facilities: The proposed system will use the Oracle database management system and Matlab with its image
processing tool box. A powerful development server will be needed. The imagery plug-in accelerator for
Oracle will be needed. Four data mining tools will be needed- these are: SAS Oracle data mining tool, CART
Salford tool, Discovery DM tool, and Wika DM tool. Data Visualization tool will be needed.
III- Estimates of Support Needs
Currently we have two students to work on the project. These students have started already working on the
implementation of two critical sub-systems. These students are currently taking the Data Mining course
CS471 with Dr. Ahmed Sameh. Two more students will be hired as research assistants and programmers in
this project.
Resource accessing and acquisitions: Oracle is the only resources available at PSU. Other hardware and
software need to be acquired (see the budget table below). The estimated cost for both the hardware and
software is 20,000 SR.
The following tables are extracts from the data provided by SGS to us.
10
11
IV-3: EXPECTED RESULTS/OUTPUTS
Deliverables in phase I: Beta Version I + its Benchmark + its Tuning
Deliverables in Phase II: Beta Version II + its Benchmark + its Tuning
Deliverables in Phase III: Beta Version III + its Benchmark + its Tuning
Deliverables in Phase IV: Final Version + User Manual
The Saudi Geological Survey in Jeddah as well as the Saudi Military Geological Survey in Riyadh might be
interested in acquiring the developed system. The methodology proposed in this project in new, we could
apply for a patent after proving its success.
12
The following is the project plan schedule. It represents those different tasks within the research and
estimated duration for each.
V - REFERENCES
1- NASA http://en.wikipedia.org/wiki/Earth_Observatory
2http://glcf.umiacs.umd.edu/data/landsat/
3- http://www.bom.gov.au/satellite/
4- The Saudi Geological Survey Agency http://www.sgs.org.sa/Arabic/Pages/default.aspx
5- The ITSC Algorithm development and Mining (Adam) System
http://geoinformatics.itsc.uah.edu/content/algorithm-development-and-mining-adam-system-earth-scienceapplications
6- NASA JPL Diamond Eye System
http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6383%2F1706
2%2F00787650.pdf%3Farnumber%3D787650&authDecision=-203
7- The Insightful VisiMine System
http://www.google.com/#hl=en&safe=active&biw=1045&bih=491&q=The+Insightful+VisiMine+System+&aq
=f&aqi=m1&aql=&oq=The+Insightful+VisiMine+System+&gs_rfai=&fp=fe2941a855446fc5
VI - ROLE(S) OF THE INVESTIGATOR(S)
(Attach a brief CV for each investigator following the format in Appendix A)
#
Name of Investigator
Area of contribution to the project
1
Ahmed Sameh
Data Mining
2
Rachid Zantout
Image Processing
13
3
Fakhry Kellah
4
Khalid Al-Mustafa
5
Ayman Kassem
6
Mohamed Sultan
Image Processing
Databases
Satellite Systems
General Consultant
VII - PROJECT SCHEDULE
PHASES OF PROJECT IMPLEMENTATION
Steps
Task
System requirements specifications: Sameh, Sid Ahmed,
System Architecture : Sultan
System Design: Sameh, Rashid
Databases Designs: Khalid
Matlab Scripts Design: Fakhry
Prototyping of critical sub-systems: Rashid, Sameh
System Detailed Design: Sameh, Sultan
Beta Version Implementation: Sameh, Khalid
Testing: Fakhry
Building Deployment Environment: Rashid
Bench Marking and Collecting Results (First Round): Sameh
System Tuning (Based on First Round Results): Khalid, Ali
Bench Marking and Collecting Results (Second Round Results): Fakhry
System Tuning (Based on Second Round Results): Sameh, Faisal
Bench Marking and Collecting Results (Third Round Results): Rachid
Version 1 Release
Results Documentation and Analysis with the Performance requirements
Detailed Code Documentation: Faisal
User and Installation Guide (Full How To): Ali
Total duration for the proposed project
VIII - BUDGET OF THE PROPOSED RESEARCH (Budget in SAR)
14
Duration
(Months)
See
Ganett
Chart for
detailed
timing
24 Month
Amount Priority 1 = Max;
Amount
Requested
2 = Mod; Approved
3 = Low.
(SAR)
(SAR)
Item
A. Personnel* (Research Assistant)
48,000
For
Official
Use
1- Student Ahmed Al-Jabreen
2- Student Kamal Qarawi
3- Student Omar Al-Moughnee
4- Student Amro Al-Munajjed
B. Equipment* (List)
10,000
Oracle Server
C. Testing and Analysis* (Location/Laboratory)
Networking to Clients
D. Consumables* (List)
1000
Desk tools
E. Travel *(Local/Internat)
10,000
-Travel for Prof. Mohamed Sultan
(Michigan/Riyadh)
-Travel for Prof. Ayman Kassem (Zahran/Riyadh)
F. Software* (List)
10,000
Oracle Accelerator
Matlab
GIS – ARC –info Tools
SAS Data Mining Tools
Visualaization Google Earth
Oracle 9i Data Mining Tools
CART Pro Data Mining Tool
15
Viscovery Data Mining Tool
Wika Data Mining Tool
G. Other Items* (Itemize)
Total Amount Requested (SAR)
79,000
IX- JUSTIFICATION OF BUDGET (Justify each item listed in the budget in the previous section)
Item
A
Justification
Four for SR 500 a month for two years
Research Assistants
B
System Development Hardware
Oracle Server
C
For Final deployment
Networking to Clients
D
To be used by the team members
Desk tools
E
For the two outside PSU professors
Travel
F
For system development
Software
16
G
X - RELEASE TIME FOR RESEARCH TEAM MEMBERS
RELEASE TIME FROM TEACHING LOAD
#
PI
CI1
CI2
CI3
CI4
CI5
Time Commitment
Team Member
(hrs/weeks/terms)
Ahmed Sameh
4hrs/week
Mohamed Sultan
2hrs/week
Rachid Zantout
2hrs/week
Fakhry Khella
2hrs/week
Khalid El-Moustafa
2hrs/week
Ayman Kassem
2hrs/week
Teaching
Load Max
e.g. 1 course
FA11
XI - EXTERNAL FUNDING
#
Source of Funds
Amount (SAR)
1
None
2
3
17
Used for
…… costs
Appendix A: CV Format for Principal Investigator and Co-Investigators
(Two pages maximum, material should be related to submitted project)
Title and Name: Ahmed Sameh
Specialty: Artificial Intelligence
Department and College: Computer Science
Summary of Experience/Achievements Related to Research Proposal:
I’ve published the following publications in the area of this project:
1- Ahmed Sameh, Nazek El-Shazly “C
Calibrating Camera Shake Photographs Using Parallel DeConvolution” to appear in the in International Journal of Video & Image Processing and Network Security
IJVIPNS- Vol:10 Issue: 03 ISSN: 2077-1207.
2- Ahmed Sameh, “Data Mining Ant Colony for Classifiers” to appear in the International Journal
of Basic & Applied Sciences IJBAS- Vol:10 Issue: 03 ISSN: 2077-1223.
3- Ahmed Sameh, Khalid Magdy, "Parallel Ant Colony Optimization" to appear in the International Journal
of Research and Reviews in Computer Science (IJRRCS) in June 2010 issue (vol. 1, No. 2)
4- Ahmed Sameh, Nazly El-Shazly, “Removing Camera Shake from Single Photograph Using Parallel DeConvolution”, accepted for presentation and publication at the Computer Graphics, Visualization, Computer
Vision and Image Processing – MCCSIS 2010, to be held in Freiburg, Germany, July 27-29, 2010
5- Ahmed Sameh, Khalid magdy, “Data Mining Ant Colony for Classifiers”, accepted for presentation and
publication at the 6th Annual International Conference on Computer Science & Information Systems, to be
held in Athens, Greece, June 25-28, 2010
6-Essam A. Lotfy, and Mohamed A. Sameh, “Applying Neural Networks in Case-Based Reasoning
Adaptation for Cost Assessment of Steel Buildings”, in the ‘Quo Vadis Computational Intelligence Book
Chapter, published by ISCI series, Slovakia, August 2000
7- Dalia El-Mansy, Ahmed Sameh, “A Collaborative Inter-Data Grid Strong Semantic Model with Hybrid
Namespaces”, Journal of Software (JSW), Academic Publisher, Volume 3, Issue 1, January 2008
8- Ahmed Sameh, and Ayman Kassem, “3D Modeling and Simulation of Lumbar Spine Dynamics”, in the
International Journal of Human Factors Modelling and Simulation , Volume IJHFMS-942, 2007
9-Lotfy E.A, Mohamed A. Sameh," Applying Neural Networks in Case-Based Reasoning Adaptation for
Cost Assessment of Steel Buildings", International Journal of Computers and Applications, Vol. 24, No. 1,
Jan. 2002
10-Adhami Louai, Abdel-Malek Karim, McGowan Dennis, Mohamed A. Sameh, "A Partial Surface/Volume
Match for High Accuracy Object Localization", International Journal of Machine Graphics and Vision, vol
10, no. 2, 2001
11- Mohamed A. Sameh and Attia E. Emad, "Parallel 1D and 2D Vector Quantizers Using Kohonen SelfOrganizing Neural Network", in the International Journal of the Neural Computing and Applications, V.
(4), no. 2, Springer Verlag, London, 1996
12- Ahmed Sameh, Amgad Madkour, “Intelligent open Spaces: Learning User History Using Neural
Network for Future Prediction of Requested Resources”, Proceedings IEEE CSE'08, 11th IEEE International
Conference on Computational Science and Engineering, 16-18 July 2008, São Paulo, SP, Brazil. IEEE
Computer Society 2008, ISBN 978-0-7695-3193-9
13-Mohamed A. Sameh, "E-Access Custom Webber: A Multi-Protocol Stream Controller", Proceedings of
the IADIS International Conference on Applied Computing, Lisbon, Portugal, March 23-26, 2004
14-Mohamed A. Sameh, and Shenouda S., "Tera-Scale High Performance Distributed and Parallel SuperComputing at AUC", Proceedings of the 12th International Conference on Artificial Intelligence, Cairo, Feb.
18-20, 2004
18
15-Kassem Ayman, and Mohamed A. Sameh, “A Fast Technique for modeling and Control of Dynamic
System”, Proceedings of the 11th International Conference on Intelligent Systems on Emerging Technologies
(ICIS-2002), Boston, July 18-20, 2002
16-Mohamed A. Sameh, and Kaptan Noha, "Anytime Algorithms for Maximal Constraint Satisfaction",
Proceedings of the ISCA 14th International Conference on Computer Applications in Industry and
Engineering (CAINE' 2001), Nov. 27- 29, at Las Vegas, Nevada, 2001
17-Ghada A. Nasr, and Mohamed A. Sameh, “ Evolution of Recurrent Cascade Correlation Networks with a
Distributed Collaborative Species”, Proceedings of the IEEE Symposium on Computations of Evolutionary
Computation and Neural Networks, San Antonio, TX, May 2000
18- Mohamed A. Sameh, Botros A. Kamal, "2D and 3D Fractal Rendering and Animation", Proceedings of
the Seventh Eurographics Workshop on Computer Animation and Simulation, Aug. 31st- Sept. 2nd, in
Poitiers, France, 1996
19- Mohamed A. Sameh, "A Transputer-Based Neural Architecture for Off-line Recognition of
Unconstrained Cursive Handwritten Arabic Text", Proceedings of the IASTED International Conference on
Modelling, Identification, and Control, held in Innsbruck, Austria, Feb. 20-23, 1995
20- Mohamed A. Sameh, "Integrating Stochastic Grid Maps into Cooperative Mobile Robots", in the
Proceedings of the IEEE-IMACS International Conference on Signal Processing, Robotics, and Neural
Networks, Lille, France, April 25-27, 1994
21-Mohamed A. Sameh, "Robotics Databases", Proceedings of the 6th International Conference on
CAD/CAM, Robotics, and Factories of the Future, England, 1991
22-Mohamed A. Sameh, "A Robust Vision System for three Dimensional Facial Shape Acquisition,
Recognition, and Understanding", Proceedings of the 1st Golden West International Conference on
Intelligent Systems, Reno, Nevada, 1991
23-Armstrong, W.W., Mohamed A. Sameh, "A Mixed-flow Query Processing Strategy for a Multiprocessor
Database Machine", IEEE Proceedings of the 5th International Conference on Distributed Computing
Systems, Denver, Colorado, 1985
Sultan’s Short CV:
Mohamed I. Sultan
Mohamed Sultan, Professor and Chair
Office: 269 387-5487;
Department of Geosciences
Lab: 269 387-5513
Western Michigan University
email: [email protected]
1185 Rood Hall
Web page: http://www.esrs.wmich.edu/
Kalamazoo, MI 49008-5241 U.S.A.
Professional Preparation
Washington University, St. Louis, MO
Remote Sensing
Post Doc. 1985-1988
Washington University, St. Louis, MO
Geochemistry
Ph.D.
1984
Ain Shams University, Cairo, Egypt
Stratigraphy
M.Sc.
1978
Ain Shams University, Cairo, Egypt
Geology
B.Sc.
1974
Appointments
2004Professor and Chair – Department of Geosciences, Western Michigan University
2002-2004
Professor – Department of Geology, University at Buffalo, Amherst, NY.
1996-2002
Project Manager, International Programs – Environmental Research Division,
Argonne National Laboratory, Argonne, IL..
1988-1996
Senior Research Scientist – National Aeronautics and Space Administration
(NASA)
Earth and Planetary Remote Sensing Facility, Department of Earth and Planetary
Sciences, McDonnell Center for the Space Sciences, Washington University,
St. Louis,
MO.
1984-1988
Research Associate – Department of Earth and Planetary Sciences, Washington
University, St. Louis, MO.
Honor, Awards
19
Fellow Geological Society America, GSA Annual Meeting, (2009- present)
Farouk El-Baz Award for Desert Research, Quaternary Geology and Geomorphology Division,
Geological Society of America (1999)
Associate Editor – Geological Society America, Bulletin (2004-2007)
Research Professor – Department of Geology, University at Buffalo, Amherst, NY (2004-present).
Adjunct Associate Professor – Department of Earth and Environmental Sciences, University of
Illinois, Chicago, IL (1997-present)
Adjunct Full Professor – Department of Geophysics, Cairo University, Giza, Egypt (1996-2007)
Adjunct Full Professor – Department of Geology, Ain Shams University, Cairo, Egypt (2000present)
Fellow, McDonnell Center for the Space Sciences, Washington University, St. Louis, MO
(1988-1996)
Current Support
Sultan, M., Principal Investigator – Integration of Grace Data with Inferences from Hydrologic
Models, Geochemical Data, and Field Data for a Better Understanding of the TimeDependant Water Storage Variability in Large-Scale Aquifers: Case Studies from North
Africa (NASA Earth Science Division)(2008-2011)
Sultan, M., Principal Investigator – Detailed Studies of Landslides in Jazan Area, Saudi Arabia
(Saudi Geological Survey)(2010-2013)
Sultan, M., Principal Investigator – The Hydrologic Role of Faults in the Mojave Desert: Fracture
Controlled Mountain Front Groundwater Flow, San Bernardino Mountains (Mojave Water
Agency)(2009-2011)
Sultan, M., Principal Investigator - Assessment and Development of Renewable Groundwater
Resources in the Quetta Valley, Pakistan (US State Department)(2007-2010)
Sultan, M., Principal Investigator - Assessment and Development of Alternative Water Resources
in the Sinai Peninsula, Egypt (NATO Science for Peace and Security)(2007-2011)
Sultan, M. Co-P.I - A Proposal to Evaluate the Jet Propulsion Laboratory Mars Exploration Public
Engagement Program and Mars Student Imaging Project, (NASA)(2007-2010)
Sultan, M., Principle Investigator, The Mesopotamian marshlands from disintegration to restoration
(NSF) (2005-2010)
Manuscripts (2008-2010)
1. Sultan, M., Metwally, S., Milewski, A, Becker, D., Ahmed, M., Sauck, W., Soliman, F.,
Sturchio, N., Wagdi, A., Becker, R., and Benjamin, S., 2010, Modern Recharge to the
Nubian Aquifer, Sinai Peninsula: Geochemical, Geophysical, and Modeling Constraints, J.
Hydrology, (in review)
2. Ahmed,M., Sultan, M., Wahr, J., Yan, E., Milewski, A., Sauck, W., Becker, R., Welton, B.,
2010, Integration of GRACE data with traditional datasets for a better understanding of the
time-dependent water partitioning in African watersheds, Geology (in review)
3. Sagintayev, Z., Sultan, M., Khan, SD, Khan, SA, Mahmood, K., Yan, E., Milewski, A.,
Marsala, P., 2010, A Remote Sensing Contribution to Hydrologic Modeling in Arid and
Inaccessible Watersheds, Pishin Lora Basin, Pakistan, J. Hydrological Processes (in
review)
4. Sultan, M., Fawzy, A., Metwally, S., Becker, R., Milewski, A., Sauck, W., Sturchio, N. C.,
Mohamed, A.M.M., Wagdy, A., El Alfy, Z., Becker, D., Sagintayev, Z., El Sayed, M., and
Welton, B., 2010, Red Sea rifting controls on aquifer distribution: constraints from geophysical,
isotopic, and remote sensing data, Geological Society America Bulletin (in press).
5. Becker, R. H., and Sultan, M., 2009, Land Subsidence in the Nile Delta: Inferences from
Radar Interferometry, Holocene, vol 19, no. 6, p. 1-6.
6. Becker, R.H., Sultan, M., Boyer, G.L., Twiss, M.R., and Konopko, E., 2009, Mapping cyanbacterial
blooms in the Great Lakes using MODIS, Journal Great Lakes Research, v. 35, no 3, p. 447-453.
7. Khan, S. D., Mahmood, K., Sultan, M. I., Khan, A. S., Xiong, Y., Sagintayev, Z., 2010.
Trace element geochemistry of groundwater from Quetta Valley, western Pakistan. Journal
of Environmental Earth Sciences, 60, 573-582.
8. Makarewicz, J.C., Boyer, G.L., Atkinson, J., Lewis, T.W., Guenther, W., Arnold, M., Becker, R.,
Sultan, M., Spatial distribution of the cyanotoxin microcystin in the Lake Ontario ecosystem:
20
9.
10.
11.
12.
13.
14.
15.
coastal embayments, rivers, nearshore and offshore and upland lakes, Great Lakes (in press).
Milewski, A., Sultan, M., Eugene, Y., Abdeldayem, A., and Abdel Gelil, K., 2009, A Remote
Sensing Solution for Estimating Runoff and Recharge in Arid Environments, Journal of Hydrology,
v. 373, p. 1-14, v. 35, p. 2001-2010.
Milewski, A., Sultan, M., Markondiah Jayaprakash, S., Balekai, R., and Becker, R. (2009b),
RESDEM, a Tool for Integrating Temporal Remote Sensing Data for use in Hydrogeologic
Investigations, Journal of Computers and Geosciences, v. 35, p. 2001-2010.
El-Sayed M. Abdelrahman, Essa, K.S., Abo-Ezz, E.R., Sultan, M., Sauck, W.A., and Gharieb, A.G.,
2008, New least-squares algorithm for model parameters estimation using self-potential
anomalies, Computers and Geosciences, v. 34, p. 1569-15767.
Forman, S., Sagintayev, Z., Sultan, M., Smith, S., Kendall, M., Marin, L., and Becker, R.,
2008, The migration of parabolic dunes and wetland formation at Cape Cod National Sea
Shore, MA: Landscape response to a legacy of human disturbance, Holocene, v. 18, no 5,
p. 765-774.
Jones, C., Sultan, M., Yan, E., Milewski, A., Hussein, M., Al-Dousari, A., Al-Kaisy, S.,
Becker, R., 2008, Hydrologic Impacts of Engineering Projects on the Tigris-Euphrates
System and its Marshlands, Journal of Hydrology, v. 353, p. 59-75
(doi:10.1016/j.jhydrol.2008.01.029).
Sultan, M., Sturchio, N., El Sefry, S., Milewski, A., Becker, R., Nasr, I., 2008, Geochemical,
Isotopic, and Modeling Constraints on the Origin and Evolution of the Rub Al Khali
Groundwater Aquifer System, Arabian Peninsula, Journal of Hydrology, v. 356, p. 70– 83.
Sultan, M., Wagdy, A., Manocha, N., Sauck, W., Abdel Gelil, K., Youssef, A.F., Becker, R.,
Milewski, A., Jones, C., 2008, An Integrated Approach for Identifying Aquifers in
Transcurrent Fault Systems: The Najd Shear System of the Arabian Nubian Shield v.
349(3-4), p 475-488. doi:10.1016/j.jhydrol.2007.11.029.
Research Interests
The use of holistic and interdisciplinary approaches to characterizing and interpreting the
Earth system and its component parts represents one of the most significant advances in
Earth and Environmental Science in the past several decades. My research group applies an
interdisciplinary research approach that takes advantage of the best available tools to address
a wide range of timely and complex geologic and environmental problems. I believe that the
adoption of interdisciplinary research approaches leads most directly to the resolution of
complex geologic and environmental problems. This approach often requires acquisition of
considerable expertise in disciplines other than the area of one’s own primary training, coupled
with close interaction with specialists in other fields. I believe that an interdisciplinary
researcher should spend his or her efforts to acquire adequate expertise in the various fields
and disciplines that are appropriate for addressing the problem under investigation. This
acquired multidisciplinary expertise advances collaborations on interdisciplinary projects in
many ways. It provides the researcher with a comprehensive understanding of the capabilities
and limitations of the various methodologies that are being applied by his co-workers, and it
provides a common platform for interactions between researchers coming from different
backgrounds. My publication and funding record covers a wide range of disciplines (remote
sensing, GIS, hydrology, surface runoff and groundwater flow modeling, geophysics,
geochemistry, geochronology and isotope geochemistry, tectonics, Precambrian geology, and
Quaternary geology) and attests to the fact that I pursued an interdisciplinary research
approach early in my career.
Locations of geographic interest include arid and semi-arid areas worldwide where demand for
freshwater supplies is on the rise because of increasing populations and limited water
supplies. The increasing demand on conventional freshwater supplies in Middle Eastern,
Saharan, and the Arabian Peninsula countries could contribute to political instabilities and
extreme stresses on the freshwater ecosystem (lakes, ponds, rivers, streams, wetlands, and
groundwater). Several of my ongoing projects address the potential influences of natural
processes, global change, and regional human activities on hydrologic systems and
landforms. Under NSF funding, my collaborators and I are developing and applying an
integrated systems approach to assess, monitor, and model the recent and future impacts of
21
changes in the landscape and land cover associated with the major agricultural development
projects in the Tigris-Euphrates watershed. Using NSF funding, we applied an interdisciplinary
(geochemistry, hydrologic modeling) approach to examine the hydrologic and geomorphologic
impacts of the Aswan High Dam. In the upstream, we applied radar interferometric techniques
to investigate the impacts of reduced river sediment load, now impounded behind the dam, on
land subsidence in the Nile Delta. In the downstream, we constructed a calibrated hydrologic
model that showed that increasing sediment thickness at the bottom of Lake Nasser reduces
recharge to the underlying aquifer and promotes encroachment of rising Lake Nasser water
onto surrounding lands. Using UNDP funding, we developed integrated cost-effective
methodologies for the assessment and sustainable management of groundwater resources in
arid lands using the Eastern Desert of Egypt as a test site. The success of our applications in
the Eastern Desert of Egypt led to three new projects, a NATO-funded project in the Sinai
Peninsula, a USAID-funded project in the Quetta region in Pakistan, and a Mojave water
agency-funded project in the Lucerne Valley, in southwest USA, where the developed
methodologies are being applied. For the latter project we are using hydrologic models,
remote sensing, and geophysical methods to evaluate the role of the transcurrent fault
systems in channeling groundwater from the mountains to the surrounding lowlands. In the
Arabian Peninsula, we demonstrated (using isotopic, geochemical, remote sensing, and GIS)
that the Empty Quarter aquifers were largely recharged by precipitation during previous wet
climatic periods over the Red Sea Hills, yet are still receiving modern precipitation in the
prevailing dry climatic periods such as those being witnessed nowadays.
One of the most exciting research areas that we are currently involved in is the applications of
GRACE temporal gravity data for the assessment of water storage variability in the African
watersheds. Results show that temporal and spatially smoothed (250 km; Gaussian) mass
variations are largely controlled by elements of the hydrologic cycle such as runoff, infiltration,
and groundwater flow, and that these mass variations are probably modulated, but not
obscured by noise as previously thought. If true, our findings suggest that: (1) it is possible to
use GRACE to investigate temporal local responses of a much larger suite of (smaller)
hydrologic systems (watersheds, lakes, rivers, marshes, etc.) and domains (e.g., source
areas, lowlands) within watersheds and sub-basins world-wide, and (2) GRACE data could
potentially be used to calibrate land surface models that are being used to drive climatic
models and thus provide confidence in the results (e.g., climatic projections) obtained by such
models.
A comprehensive understanding of Earth systems sciences requires substantial integration
among scientific disciplines in terms of concepts, understanding, skills, and problem-solving
techniques. The scale of existing global geologic data sets, their extremely uneven
documentation, and the relative scarcity of user-friendly access tools are major obstacles to
interdisciplinary research. New approaches entail the application of GIS technologies on a
global scale, to spatial-temporal integration, visualization, and analysis of geochemical,
geophysical, remote sensing, and geodetic data sets. Applications of information technology in
geosciences are not restricted to the compilation, visualization, and distribution of geologic
data sets, but also in the use of these data sets to apply online dynamic models and
simulations for various geologic processes. The application of web-based GIS technologies is
especially advantageous in developing countries, where obtaining basic data sets that are
relevant to geologic applications, such as digital topography, aerial photography, satellite data,
and geologic maps, is often cost prohibitive. We expanded our expertise in the general area
of geoinformatics and we are applying the acquired experience to address environmental and
tectonic problems of interest. Using the Google Earth Interface, we have now developed a
comprehensive web-based GIS (http://www.esrs.wmich.edu/webmap/) that encompasses all
the databases we generated and the custom tools that we constructed throughout the years
for the distribution, analysis, visualization, and modeling of accumulated data sets. Examples
of these databases are the Egyptian database, Saudi database, African GRACE database,
and the Mojave database.
22
We developed our research capabilities and expertise in the area of aquatic remote sensing,
specifically in limnologic (inland or fresh water) and coastal remote sensing. To expand our
research activities in this area, we established a direct downlink and processing system for
remote sensing data. This system acquires and examines real-time data over the Great
Lakes, where there is great interest, expertise, and ongoing research at UB. The system has
been acquired, installed, and is currently fully operational. We have real-time access to the
Advanced Very High Resolution Radiometer (AVHRR) L-band sensor data transmitted by
National Oceanic and Atmospheric Administration (NOAA) satellites, as well as the Orbview-2
satellite that carries the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument
(http://isis.geology.buffalo.edu/UB-receiving-Station-Lakes.htm). Together with scientists from
SUNY - College of Environmental Science and Forestry, SUNY- Univ. at Buffalo, SUNY
Brockport, Univ. of Vermont, and Univ. of Tennessee we explored the utility of chlorophyll
extraction techniques to map the spatial and temporal variations in algal blooms in Lake Erie
and Lake Ontario with predictions from a hydrodynamic and particle tracking model to
determine transport pathways. The project goal is to develop a system that uses remote
sensing to identify the formation of a bloom and can predict the movement of the bloom
through modeling.
All of the projects that are described above have one thing in common: they impact large
sectors of the population in many ways. One research area that I would like to venture into is
to complement our existing efforts by bringing on board expertise in the general area of Social
Sciences. I can think of many ways in which such expertise can advance our ongoing
research activities. One way would be to examine, assess, and model the impacts of our
findings on impacted populations. For example, we could assess population migration
patterns that are related to development of the major engineering projects in the Tigris
Euphrates watershed. Social scientists could also provide guidelines as to how our
methodologies and approaches can be optimized to factor in the social aspects that will
eventually dictate whether our developed methodologies will be practical enough or appealing
enough to be adopted/implemented. Breaching the gap between physical and social sciences
is becoming more and more a necessity for the success of many of the applied projects today
and many of the funding agencies are realizing the importance of such approach. Physical
scientists from various disciplines have done quite well working together to address complex
environmental problems. Such integrated research will benefit from expanding existing
models to encompass social disciplines as well.
Appendix B: Evaluations and Approvals
COLLEGE REVIEW COMMITTEE Evaluation and Recommendation
Excellent
Item/ Evaluation
Research methodology
Research objectives
Research originality
Research contribution
Research applicability and relevance
23
Very
Good
Good
Weak
Overall evaluation
Recommendations of College Committee
Approved
Amount of Budget Approved by College Committee:
Disapproved
(SAR)
Chair College Committee - Title and Full Name:
Signature:
Date:
Recommendations of the College Council
/
Approved
/
Disapproved
Dean of the College Council - Title and Full Name
Signature:
Date:
/
/
PSU INSTITUTIONAL RESEARCH COMMITTEE (IRC) Recommendation
Recommendation of the PSU IRC
Approved
Disapproved
Chair IRC Committee - Title and Full Name:
Signature:
Date:
24
/
/
PSU EXTERNAL REVIEW PANEL FOR RESEARCH PROPOSALS Recommendation
Recommendation of the Eternal Review Committee.
Approved: Amount of grant approved:
Disapproved:
Postponed:
Directed to:
Chair of External Review Panel - Title and Full Name:
Signature:
( SAR)
Date:
Recommendation of University Council
/
/
Approved
Signature:
Date:
25
Disapproved
/
/