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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• Out of date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