Erdbeobachtung im Zeitalter von Google & Co: Die Chancen Kooperativer Netzwerke

Wolfgang Wagner wolfgang.wagner@geo.tuwien.ac.at

Department of Geodesy and Geoinformation (GEO) Vienna University of Technology (TU Wien)

http://www.geo.tuwien.ac.at/

Google Earth

Wikipedia: “Google Earth is a virtual globe, map and geographical information program that was originally called EarthViewer 3D, and was created by Keyhole, Inc, a Central Intelligence Agency (CIA) funded company acquired by Google in 2004.”

Global ENVISAT ASAR Backscatter Model

Sabel, D., Z. Bartalis, W. Wagner, M. Doubkova, J.-P. Klein (2012) Development of a Global Backscatter Model in support to the Sentinel-1 mission design, Remote Sensing of Environment, 120, 102-112.

Based on 110 000 ASAR Images

Google Earth Engine

https://earthengine.google.org/

Big Science

University of Maryland led study to map global forest loss and gain from 2000 to 2012 at a spatial resolution of 30 meters.

• 654 178 growing season Landsat 7 ETM+ images • 143 000 000 000 pixels

Hansen et al. (2013) High-resolution global maps of 21st-century forest cover change, Science, 342, 850-853.

How to Cope with the Latest Generation of Satellites?

Massive data centres and cloud computing capabilities are needed to cope with handling the exponentially increasing data volumes of earth observation satellites

• Compressed raw data volume per day for 2 satellites constellations – Sentinel-1: 1.8 Terabytes – Sentinel-2: 1.6 Terabytes

Start working on Google Earth Engine or other commercial cloud platforms

• Dependencies? • Costs? • Security? • Rights?

Increased specialisation and cooperation

Earth Observation Data Centre for Water Resources Monitoring

an Open & International Cooperation of

Mission Statement

The mission of EODC-Water GmbH is to work together with its partners from science, the public- and the private sectors in order to foster the use of earth observation (EO) data in support to monitoring of global water resources by

• providing collaborative information technology (IT) infrastructure for archiving, processing, and distributing EO data

• organising a collaborative software development process for establishing fully automatic EO data processing chains

Technical Goals • Set up and operate a collaborative cloud that connects participating data centres • Connect to satellite ground segments for receiving EO data • Process in-coming EO data in near-real-time (NRT) • Distribute NRT data through a rolling archive • Run a science integration and software development cloud platform • Maintain shared code libraries • Provide access to complete EO mission data archives • Enable, organise and carry out processing efforts

R&D and Operational Funding Copernicus, National, EUMETSAT, Industry, etc.

Existing Infrastructure at TU Wien

Server rooms • Cooling • Energy costs • Network

Robotic tape library

1. Petabyte Extension to 36 PB possible

Cloud Environments

EODC-Water Cooperation

Model

Based on the example of 52°North

Product Portfolio

Water resources management requires an integrated view of the complete water cycle

High-resolution data products are needed to connect the local and global scales

Satellite Temporal ProductWater Bodies Forest / Non Forest Rice Water Bodies Rice and growing statusSoil Moisture Soil Water IndexStart of Soil Water Season Drought / Wet IndicesFlooding SnowFreeze Forest / Non Forest Biomass IndicesLandcoverGlacier area Forest Functional parameters Evapotranspitation

static Glacier area dynamic Snow

dynamic

Sentinel 2

Sentinel 3

static

dynamic

Sentinel 1

static

Soil Moisture: From ENVISAT to Sentinel-1

Outlook

EODC-Water GmbH shall be founded in spring 2014 • Happy to include additional cooperation partners • Aims to become a collaborative ground segment for the Sentinel satellites • Cooperation with other data centres throughout Europe

Expected benefits for scientists

• Focus on scientific problems (rather than having to deal with standard processing tasks such as data management, geometric- and radiometric correction, etc.)

– test their algorithm on larger EO data sets – compare their algorithms to other state-of-the-art algorithms – validate their results with extensive reference data sets – participate in benchmarking activities

• Have an impact – Citations – Socio-economic benefits