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Sentinel 2 Image Delivery Platform
Forest Industry Informatics - SCION
Sam Damesin, Grant Pearse, Ralf Gommers, Andrew Gordon, Jonathan Dash
March 2017
Imagine
Currently not possible
National natural resource descriptions are inadequate
on a national scale:
• Frequently not spatial
• Of insufficient resolution
• Incomplete
• Out of date
• Difficult and expensive to acquire
This leads to poor decision making, inefficiency, lost
productivity and reduced profit.
New technologies provide opportunities
New satellites Sentinel 2A (2015)
and 2B (2016 2017)
• European Space Agency
• Free access
• Multi-spectral with 13 bands
• Spatial resolution up to 10 m
• Covering NZ every 5 days
New technologies provide opportunities
New satellites Sentinel 2A (2015)
and 2B (2016 2017)
• European Space Agency
• Free access
• Multi-spectral with 13 bands
• Spatial resolution up to 10 m
• Covering NZ every 5 days
BUT – There is no easy mechanism
to access, process, and store this
imagery.
Project details & Context
This project: proof of concept
Objectives:
▪ Deliver imagery in an easy-to-use manner for forestry companies
and scientists
• Enable further processing and analysis
• Compatible with existing tools and workflow
▪ Gauge interest for future development
Technical information:
▪ Build with Python and GDAL (Geospatial Data
Abstraction Library) for pre-processing steps
▪ Cloud infrastructure: 2-3 servers on Amazon
AWS, S3 storage
▪ Use GeoServer WMS (Web Map Service) for
publishing
Automated data processing pipeline
1. Download
• Get imagery for NZ
• Only cloud free 0-40%
2. Pre-process
• 4 bands
Red, Green, Blue and NIR
• For every collection day
Reprojection and multi-bands mosaic
3. Publish (WMS)
• Daily mosaic (capture day)
With gaps
• Monthly most recent mosaic
No gaps
Not ready yet!
4. Analyze
• Apply cutover detection algorithm as a proof of concept
The longer term vision
1. Data capture
Sentinel 2A and 2B
2. Pre-Processing
Automated download
Radiometric correction
Ortho correction
Cloud mask
Mosaicing
3. Storage and access
Cataloguing
Access
4. Processing
Classification
Texture
Spectral Indices
© esa
Potential derived products & users
Digital Surface and
Terrain model
NDVI
Natural forests
Exotic forests
Yields, age, species
classification
Change detection
• Local and central
government
• Forest owners
(large and small)
• Consultants
• Wood processors
• Log buyers
• Iwi
• Researchers
Web Map Service
Server hosting
▪ High bandwidth cloud server on Amazon Web
Service (EC2)
▪ Layers storage on Amazon Web Service (S3)
▪ GeoServer Web Mapping Service
• Open source, standard
Easy access
Example
Example
Land classification & cutover detection
Datasets
▪ Sentinel 2 imagery
▪ Central North Island
▪ 3 dates:
• Nov. 2016
• Dec. 2016
• Feb. 2017
▪ 4 bands at 10m resolution:
• Red, Blue, Green
• Near Infra-Red
Datasets
▪ Sentinel 2 imagery
▪ Central North Island
▪ 3 dates:
• Nov. 2016
• Dec. 2016
• Feb. 2017
▪ 4 bands at 10m resolution:
• Red, Blue, Green
• Near Infra-Red
▪ 2 study sites
Step 1: Machine learning training
▪ Python code
▪ Supervised machine
learning
▪ Random forest classifier
▪ 5 classes:
• Clouds
• Grassland/Farmland
• Harvested area
• Forested area:
young tree
• Forested area:
mature tree
Step 2: Machine learning classification
▪ November 2016
Step 2: Machine learning classification
▪ November 2016
Step 2: Machine learning classification
▪ December 2016
Step 2: Machine learning classification
▪ February 2017
Step 3: study sites
Step 3: study sites
Cutover detection application, validation
▪ Accuracy validation
• Manual definition of
validation subset (see
polygons on the right)
• Manual classes
assignment
• 98% accuracy for this
subset
Cutover detection application
▪ Band importance for the classification
• Band 1 (Red) importance: 31%
• Band 3 (Blue) importance: 24%
• Band 4 (NIR) importance: 22%
• Band 2 (Green) importance: 21%
▪ Successfully classify 5 classes on a small dataset
• Clouds
• Grassland/Farmland
• Harvested area
• Forested area: young tree
• Forested area: mature tree
Conclusion and next steps
Current status
▪ Operational prototype system
• Automatic pre-processing and storage
• Daily mosaic of cloud free New Zealand (sensing day)
− Between 0 and 40% cloud coverage
− Bands: Red, Green, Blue and NIR at 10m resolution
− Gaps for cloudy area
• Monthly up to date mosaic (not ready yet!)
− No gaps: most recent images used
• Hosted on Amazon Web Service using GeoServer
• Published via Web Map Service (standard service)
− Easily accessible via ArcGIS, QGIS and other GIS software
Future work
▪ Internal use for current and future research work, potentially new
product development
▪ Potentially further develop cutover detection algorithm
▪ Seeking feedback from industry on:
• Level of interest – who would access it?
• What products are of interest?
• Pathway to support ongoing access?
Acknowledgements
▪ Michael Watt, Bryan Graham:
approval and funding
▪ Jonathan Dash, Grant
Pearse: idea and active
participation
▪ Andrew Gordon, Ralf
Gommers: technical inputs
www.scionresearch.com
Scion is the trading name of the New Zealand Forest Research Institute Limited
Prosperity from trees Mai i te ngahere oranga