Grid for Flood Extent Grid for Flood Extent Extraction from SARExtraction from SARImageryImageryS. Skakun1, N. Kussul1, E. Lupian2,V. Savorsky3, Yu.Tischenko3,L. Hluchy4, P. Kopp5

1Space Research Institute NASU-NSAU, Ukraine2Space Research Institute RAS (IKI RAN), Russia3Institute of Radio-Engineering and Electronics of RAS, Russia4Institute of Informatics SAS, Slovakia5Centre National d’Etudes Spatiales (CNES), France

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OutlineOutline

• GEOSS and Current Trends• Specific of EO Applications• Grid in EO Domain• Grid Infrastructure at Space Research Institute• Food Extent Extraction via Grid• Test Study Areas

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GEOSS StructureGEOSS Structure

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Data Fusion for Disaster Data Fusion for Disaster MonitoringMonitoring

Basic servics

• NDVI• Land

Cover• DEM• LAI• ...

Input dat

Models

Data assimilation• In-situ

• SYNOP• RAOB

• ДЗЗ

• AIRS• AMSR-E• QuikScat

MeteorologyWRF/MM5

Hydrology

SVAT

Applied services

Users Interested organizations

AgricultureDisastersCharter

Water resources authority

Hydrometcenter

Biodiversity assessment

Yield predictionDrought

monitoringFlood monitoring

& predction

Data asimilation• MODIS• MSG

Input data (satellite, in-situ)

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Specific of EO Tasks (1/2)Specific of EO Tasks (1/2)

• Data of different spatial and temporal resolutions

• Heterogeneous data processing

• The need for near real-time data access

• Operational access to long-term data archives

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Specific of EO Tasks (2/2)Specific of EO Tasks (2/2)

• Using models for trends prediction

• Complex data assimilation

• The need for high-performance computing

• Visualization of spatially distributed data

• Informational security enforcement

Atm

osph

ere

Oce

an

Biosphere

Cryosphere

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Grid is Grid is convinientconvinient environmentenvironmentfor solving these problemsfor solving these problems

• Integration of multi-source data;• High-performance computing for

computationally intensive tasks and the need for processing in the near real-time for fast response;

• Security issues regarding satellite data policy;• Need for managing large volumes of satellite

data;• Can facilitate interactions between different

actors by providing a standard infrastructure and a collaborative framework to share data, algorithms, storage resources, and processing capabilities

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Grid infrastructureGrid infrastructure

Архів супутниковихданих

Glite StorageElement

Glite ComputingElement

Обчислювальний

кластер

Cтанція прийомуEUMETCast

Обчислювальний кластер

Архів супутникових даних

Grid-сервер на базі GT4

Grid-сегментІКД НАНУ-НКАУ

Кластер СК ІТ-3

Grid-сервер на базі GT4

Grid-сегмент ІК НАНУ

Grid-портал

Grid-сегмент Remote Sensing Ground Station (Китай)

InterGrid

Grid-cерверина базі GT2

Український академічний

Grid-сегмент

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Grid portalGrid portal

Enabling user access via Grid portal

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Resources: HardwareResources: Hardware

• Space Research Institute resources:– Clusters:

• 2x2 AMD Opteron Gigabit Ethernet interconnect• 20 AMD Opteron cores & Infiniband interconnect

– Storage: up to 100 Tb

• Institute of Cybernetics:– SKIT-1 (48 CPU), SKIT-2 (64 CPU), SKIT-3 (300

cores) – SKIT-3 – 3TFlops, 5th position in TOP50 of NIS

countries

• Ukrainian Academic Grid Infrastructure

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Flood Extent Extractionfrom Satellite Imagery

Архів супутниковихданих

Glite StorageElement

Glite ComputingElement

Обчислювальний

кластер

Cтанція прийому

EUMETCast

Обчислювальний кластер

Архів супутникових даних

Grid-сервер на базі GT4

Grid-сегментІКД НАНУ-НКАУ

Кластер СКІТ-3

Grid-сервер на базі GT4

Grid-сегмент ІК НАНУ

Grid-портал

Grid-сегмент Remote Sensing Ground Station (Китай)

InterGrid

Grid-cерверина базі GT2

Український академічний

Grid-сегмент

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Flood Monitoring Flood Monitoring ––Access to Satellite DataAccess to Satellite Data

Currently, EnvisatASAR WSM can be

accessed & processed!

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Flood Extent ExtractionFlood Extent ExtractionWorkflowWorkflow

1. Transformation of raw data to lat/long projection.2. Orthorectification using DEM => to remove shadowing

effects.3. Image calibration.4. Geocoding & co-registration with other data sets.5. Image processing using Kohonen neural networks (NN)

i. Image segmentation.ii. NN calibration.iii. Image classification.

6. Flood extent estimation using information of water bodies

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Submission via Grid PortalSubmission via Grid Portal

• Deployed using GridSphere Framework• Portal provides access to

– satellite data (Envisat ASAR WSM) and– computational resources of the Grid infrastructure

• Through the portal users specify the input data, run the job, and get the output, i.e. flood extent

• The output product is a binary GeoTiff file with – 1 value indicating the presence of water– 0 value indicating no water

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Specifying Job Submission Specifying Job Submission ParametersParameters

Executable file to run

Current directory

Executable parameters, i.e. input and output

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Job Submission Status Job Submission Status --CompletedCompleted

Job is completed

It took approximately >30 min to process a single SAR image on a single workstation.

The use of Grid computing resources allowed us to reduce the time to <1 min !

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Future ActionsFuture Actions

• To integrate a catalogue of satellite data (Envisat ASAR WSM) with a Grid portal and processing facilities– allow the users to easily search, process and

visualize the EO data (in particular, Envisat ASAR WSM)

• Automatic generation of KML files to be visualized in the Google Earth

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Flood monitoring (Ukraine, Flood monitoring (Ukraine, TiszaTisza River, 2001)River, 2001)

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Flood monitoring (China, Flood monitoring (China, HuaiheHuaihe River, 2007)River, 2007)

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Flood monitoring (Mozambique, Flood monitoring (Mozambique, Zambezi River, 2008)Zambezi River, 2008)

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Flood Flood –– India & Nepal, 2008India & Nepal, 2008

Flood waters derived from ESA’s Envisat satellite ima gery(20 August 2008)

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Flood Flood –– Thailand & Laos, 2008Thailand & Laos, 2008

Flood waters derived from ESA’s Envisat satellite ima gery(16 & 20 August 2008)

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Thank You!