1. CYBER-GIS FOR SCIENTIFIC DISCOVERIES

2. Global Forest Change Hansen, M. C. et al (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850-853.

3. BACKGROUND  Deforestation  Global-scale  High-resolution  Data Continuity

4. RESULTS

5. WHY THE CLOUD?  Data intensity  1.3 million potential images  Computing intensity  ALL the processing  Concurrent intensity  Public Access  Spatiotemporal intensity  All Landsat 7

6. HOW THE CLOUD?  Google Earth Engine  654,178 images  Image resampling, ToA Reflectance, Noise Removal, Image Normalization  Cloud-free composite  per pixel cloud (the fluffy ones) screening  Per band reflectance value processing metrics

7.  20 terapixels of data processed  20,000,000,000,000 pixels  1 million CPU-core hours  10,000 computers

8. GOOGLE EARTH ENGINE  DaaS, SaaS, PaaS, IaaS  Houses nearly all Landsat 4, 5, 7, 8 data  Applications:  detecting deforestation  land cover classification  biomass and carbon  mapping remote areas

9.  Lazy computation model  Parallel computing  Data management automation  FlumeJava framework (for parallel distribution and management)

10. LIVE DEMO http://earthenginepartners.appspot.com/science-2013-global-forest

11. Global Marine Biogeography Fujioka, E., Berghe, E. V., Donnelly, B., Castillo, J., Cleary, J., Holmes, C., & Halpin, P. (2012). Advancing Global Marine Biogeography Research With Open ‐ source GIS Software And Cloud Computing. Transactions In GIS, 16(2), 143-160.

12. CLOUD  The expression cloud is commonly used in science to describe a large agglomeration of objects that visually appear from a distance as a cloud  It describes any set of things whose details are not inspected further in a given context.

13. BACKGROUND  In marine biology, the Census of Marine Life is the catalyst for global data aggregation effort.  An Ocean Biogeographic Information System (OBIS) developed to coordinate aggregation of global marine biogeographic data.

14. CHALLENGES  Storing data  Querying data  Disseminating data  Mapping data

15. GOAL  To build a user-friendly, powerful, manageable, interoperable and flexible system  To broaden the number of search and query criteria that could be combined (geographic space, time, depth, biological classification)  To package these options into an interface that would allow for easy queries, while not limiting more complex queries

16. SPECIFIC OBJECTIVES  An intuitive system to browse the biological classification and to integrate results over the hierarchy  Create summarized views of data holdings for efficient extraction and rendering  All query results to be downloadable in common GIS formats and web service standards with enhanced interoperability for other databases or products

17. OBIS TECHNOLOGIES  Database – PostGISl,PostgreSQL  Mapping engine – GeoServer  Search interface – OpenLayers  Front end –Drupal  Built on a Cloud Computing environment  Improved the performance and online user experience  Maintained a standards-compliant and interoperable framework

18. DATA & DATABASE

19. SYSTEM DIAGRAM OF THE IOBIS SEARCH INTERFACE

20. NOTABLE CHALLENGES  The inability of OGC standards to make a layer highly searchable while providing rich query options.  Complexity grows rapidly when more search options are provided.  Extracting a large number of location data from the database and mapping individual points within an acceptable response time (e.g. 30 seconds)  Point locations overlapping extensively make it difficult to grasp the global distribution of a group of interest.

21. RESULTS  A biodiversity portal infrastructure based on open- source, standard-compliant applications in a Cloud Computing environment  The success of constructing such a complex and full- featured system proves the maturity and prowess of the components  The use of the Amazon EC2 cloud enabled the development to scale up to meet the expected challenges of a widely covered international release event.

22. CENSUS OF MARINE LIFE RELEASE EVENT

23. DISCUSSIONS & FACTS  Compared with terrestrial animals, marine creatures tend to have longer migration paths and broader home ranges.  Commonly used projections severely distort the polar regions and make it hard to grasp the species distribution or movement around the poles.  User inputs and spatial analyses also need to be dealt with under the polar projection.