Sentinel 2 Image Delivery Platform
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1 Sentinel 2 Image Delivery Platform Forest Industry Informatics - SCION Sam Damesin, Grant Pearse, Ralf Gommers, Andrew Gordon, Jonathan Dash March 2017
2 Imagine
3 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resource descriptions are inadequate on a national scale: Frequently not spatial Of insufficient resolution Incomplete Out of date Difficult and expensive to acquire This leads to poor decision making, inefficiency, lost productivity and reduced profit.
4 New technologies provide opportunities New satellites Sentinel 2A (2015) and 2B ( ) European Space Agency Free access Multi-spectral with 13 bands Spatial resolution up to 10 m Covering NZ every 5 days
5 New technologies provide opportunities New satellites Sentinel 2A (2015) and 2B ( ) 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.
6 Project details & Context
7 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
8 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 multibands 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
9 The longer term vision 1. Data capture Sentinel 2A and 2B 3. Storage and access Cataloguing Access esa 2. Pre-Processing Automated download Radiometric correction Ortho correction Cloud mask Mosaicing 4. Processing Classification Texture Spectral Indices
10 Potential derived products & users Digital Surface and Terrain model NDVI Natural forests Exotic forests Yields, age, species classification Local and central government Forest owners (large and small) Consultants Wood processors Log buyers Iwi Researchers Change detection
11 Web Map Service
12 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
13 Easy access
14 Example
15 Example
16 Land classification & cutover detection
17 Datasets Sentinel 2 imagery Central North Island 3 dates: Nov Dec Feb bands at 10m resolution: Red, Blue, Green Near Infra-Red
18 Datasets Sentinel 2 imagery Central North Island 3 dates: Nov Dec Feb bands at 10m resolution: Red, Blue, Green Near Infra-Red 2 study sites
19 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
20 Step 2: Machine learning classification November 2016
21 Step 2: Machine learning classification November 2016
22 Step 2: Machine learning classification December 2016
23 Step 2: Machine learning classification February 2017
24 Step 3: study sites
25 Step 3: study sites
26 Cutover detection application, validation Accuracy validation Manual definition of validation subset (see polygons on the right) Manual classes assignment 98% accuracy for this subset
27 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
28 Conclusion and next steps
29 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
30 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?
31 Acknowledgements Michael Watt, Bryan Graham: approval and funding Jonathan Dash, Grant Pearse: idea and active participation Andrew Gordon, Ralf Gommers: technical inputs
32 Prosperity from trees Mai i te ngahere oranga Scion is the trading name of the New Zealand Forest Research Institute Limited
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