Joint Breakout Session 1: Intelligent use of data quantity vs focusing on data quality
Automatization of information extraction
to build up a crowd-sourced reference database
for vegetation changes
Jonas Eberle, Dr. Christian Hüttich, Prof. Christine Schmullius
Friedrich-Schiller-University Jena, Germany
Institute for Geography, Department for Earth Observation
www.eo.uni-jena.de
Jonas Eberle
25th March 2015
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Lots of data is freely available
• MODIS
• Sentinel
– Resolutions
– Resolutions
• Medium spatial (up to 250m)
• Up to daily temporal
• Medium spatial
• High spatial
• Different temporal resolutions
– Products
• Vegetation, Fire, LST, …
• Landsat
– Resolutions
• High spatial
• 8- 16-days temporal
• Climate station data
– Resolutions
• Hourly, Daily temporal
– Products
• Temperature, Precipiation, ...
Jonas Eberle
25th March 2015
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Lots of data is freely available
How to get these data?
• OpenSearch Web Service
– Sentinel – ESA Data Hub
• FTP/HTTP
– MODIS
– Climate station data (NOAA)
• Browser-based applications
• Google Earth Engine
– MODIS
– Landsat 1-8
Jonas Eberle
– USGS Earth Explorer
– NASA Reverb
– etc.
25th March 2015
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How to process available data?
1.
2.
3.
4.
5.
Find download files
Download data
Convert data
Clip data to study area
Apply quality masks
 For any dataset
 For different data formats
 For any time stamp
Jonas Eberle
25th March 2015
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Intelligent use of data quantity
• What is needed?
1. Automation in data access
2. Easy to use clients
3. Possibility to create / publish new information
• What should be achieved in our case?
– Try to explain a change detected in the time series
 Evaluate changes based on additional data
– Easy use of a wide range of datasets (no processing needed)
 Interactive change evaluation of spatial time-series data
 Create new knowledge and information
Jonas Eberle
25th March 2015
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Intelligent use of data quantity
1. Automation in data access
– Data Processing
Middleware
– Standardized data
publishing
• OGC Web Services
– Necessary inputs
• Name of dataset
• Geometry (Point,
Polygon)
• Further optional
parameters
Jonas Eberle
Figure: Multi-Source Data Processing Middleware
25th March 2015
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Intelligent use of data quantity
2. Easy to use clients
– Web Portal
• Modern web technologies
• Based on web services
• User accounts
– Mobile Application
• Data access & analysis tools
in the field
• GPS position
Jonas Eberle
25th March 2015
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Intelligent use of data quantity
3. Possibility to create / publish new information
– Scientific algorithms has to be linked to automated data access
 Provide scientific algorithms as web services
– Users can validate the results of these algorithms based on
additional datasets and create new information
• e.g., valid change areas
 We need automated data access linked with automated
execution of scientific algorithms!
Jonas Eberle
25th March 2015
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Earth Observation Monitor
Automated access, analysis, and monitoring
of global vegetation time-series data
Datasets:
• MODIS 16-Daily Vegetation
Index (NDVI, EVI)
Data access:
• Pixel or Polygon-based
extraction service
Analyses:
• Trend calculations
• Breakpoint detection
• Phenological parameters
Applications:
• Web Portal
• Mobile App (mobileEOM)
for iOS and Android
www.earth-observation-monitor.net
Earth Change Monitor
• Objectives
– Detect areas based on environmental change events in high
density time series data (MODIS)
– Provide high resolution images (Landsat) of the pre- and after
change events
– Add further datasets to distinguish between different types of
change
– Provide simple tools for users and developers with web services
and web interfaces.
 Make use of wide variety of available data (data quantity) to create
new information to build up a reference database.
Jonas Eberle
25th March 2015
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Intelligent use of data quantity
Concept of the „Earth Change Monitor“:
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Focusing on data quality
• We have to consider quality flags (if available!) when using data
– Good example: MODIS products
• Crowd-sourced reference database needs to be checked on
errors / wrong interpretations
– Access only to “experts”
– Users can gain “expert” status for their user account
– Users reference database vs. global reference database
 Use data quantity to increase quality of algorithm results
(e.g., break-point in vegetation time-series)
Jonas Eberle
25th March 2015
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Conclusions
• Crowd-sourced intiative can help scientists to better test their
algorithms for information extraction and benefit from the
input of users
• Web 2.0 leads us to a new way of
– how algorithms can be tested and
– how a crowd-sourced reference database can be build up
 We need simple web services for data access linked with
web services for algorithm execution registered to GEOSS
 EO time-series data are better useable and lead to new
knowledge to further improve algorithms and validated
reference information.
Jonas Eberle
25th March 2015
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Thank you for your attention!
Questions?
Contact information:
Jonas Eberle
Friedrich-Schiller-University
Institute for Geography
Department Earth Observation
Loebdergraben 32
07743 Jena, Germany
Acknowledgement:
Friedrich-Schiller-University Jena and EU FP7 EuRuCAS
project (No. 295068) for financing work and travel.
Jonas Eberle
25th March 2015
phone: +49 3641 94 88 89
email: jonas.eberle@uni-jena.de
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