Optimal use of new satellite resources. Research funded by NERC/CEH and JNCC.
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Transcript of Optimal use of new satellite resources. Research funded by NERC/CEH and JNCC.
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Optimal use of new satellite resources.Research funded by NERC/CEH and JNCC.
Rapid Land Cover Mapping
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Cumbrian Lakes Monitoring
UK-Atmospheric Chemistry and Air Quality Monitoring Network,
Isle of May Long Term Study,
UK Lake Ecological Observatories
Conwy Source to Sea
UK Upland waters Monitoring Network
Carbon Catchments
Wetland Core Monitoring,
COSMOS Soil Moisture Network
UK Land Cover Map
Countryside Survey
Welsh Govt. Environmental Monitoring
Biological Records Centre
UK Butterfly Monitoring Scheme,
Predatory Bird Monitoring Scheme
Remote sensing: a key component of CEH’s integrated UK observing capability
Soil observatories
UK Environmental Change Network
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National LCM – traditional recipe
Ingredients:
• Prepared satellite images
• Spatial framework
• Schema
• Field-data
• A maximum likelihood classifier
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Training and Validation: field campaign
LCM2007:
<20,000 useable training and validation points
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Training: History from 3 CEH LCMs
A region of Norfolk, Suffolk: ~21,000 training polygons; > 1.25 million training pixels
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Machine Learning
• WEKA toolkit from University of Waikato, NZ
• Explored a range of Machine Learning algorithms: Decision Trees, Boosting, Support Vector Machines, Random Forest
• Random Forest performed best
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Surface probability for each type, Arable
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Surface probability, Coniferous Woodland
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Results: < 1hr (previously 2-4 weeks)
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Norwich in 2002 as pixels
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Norwich as Land Parcels
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Lakenheath, Thetford Forest
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Lakenheath, Thetford Forest
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Accuracy
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Correspondence with CS
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Correspondence with CS
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Areal correspondence CS1998, Norfolk 2002
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Key points• Land cover history produces a richer set of training information than
conventional field campaigns and almost cost-free
• Used with non-parametric classification techniques rapid, more accurate classifications
• Stable training sites enable multiple classifications using the same training polygons (classify historical images).
• Consistent training sites, classification methods, thematic descriptions, spatial structure supports change detection
• Near real-time classification a sensible aspiration
• Field observations still essential for product validation