Transcript Module 1 - 3. CyberGIS for Scientific Discoveriesx

CYBER-GIS FOR SCIENTIFIC
DISCOVERIES
Global Forest Change
Hansen, M. C. et al (2013). High-Resolution Global
Maps of 21st-Century Forest Cover Change. Science,
342(6160), 850-853.
BACKGROUND
 Deforestation
 Global-scale
 High-resolution
 Data Continuity
RESULTS
WHY THE CLOUD?
 Data intensity
 1.3 million potential images
 Computing intensity
 ALL the processing
 Concurrent intensity
 Public Access
 Spatiotemporal intensity
 All Landsat 7
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
 20 terapixels of data processed
 20,000,000,000,000 pixels
 1 million CPU-core hours
 10,000 computers
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
 Lazy computation model
 Parallel computing
 Data management automation
 FlumeJava framework (for parallel distribution and
management)
LIVE DEMO
http://earthenginepartners.appspot.com/science-2013-global-forest
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.
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.
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.
CHALLENGES
 Storing data
 Querying data
 Disseminating data
 Mapping data
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
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
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
DATA & DATABASE
SYSTEM DIAGRAM OF THE IOBIS SEARCH INTERFACE
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.
RESULTS
 A biodiversity portal infrastructure based on opensource, standard-compliant applications in a Cloud
Computing environment
 The success of constructing such a complex and fullfeatured 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.
CENSUS OF MARINE LIFE RELEASE EVENT
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.