1 / 18

Rapid Land Cover Mapping: Optimizing Satellite Resources for UK Environmental Monitoring

Research funded by NERC/CEH and JNCC for optimal use of satellite resources in rapid land cover mapping. Leveraging machine learning with WEKA toolkit, the study achieved quick and accurate classifications, including arable land and coniferous woodland. The approach utilized historical land cover data for richer training information and stable sites for multiple classifications, supporting change detection. While near-real-time classification is a goal, field observations remain essential for product validation. The method significantly reduced processing time compared to traditional approaches.

argyle
Download Presentation

Rapid Land Cover Mapping: Optimizing Satellite Resources for UK Environmental Monitoring

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Optimal use of new satellite resources. Research funded by NERC/CEH and JNCC. Rapid Land Cover Mapping

  2. Remote sensing: a key component of CEH’s integrated UK observing capability UK Environmental Change Network UK-Atmospheric Chemistry and Air Quality Monitoring Network, Carbon Catchments UK Upland waters Monitoring Network Cumbrian Lakes Monitoring Wetland Core Monitoring, Isle of May Long Term Study, Soil observatories Conwy Source to Sea COSMOS Soil Moisture Network UK Butterfly Monitoring Scheme, UK Lake Ecological Observatories Predatory Bird Monitoring Scheme Biological Records Centre Welsh Govt. Environmental Monitoring Countryside Survey UK Land Cover Map

  3. National LCM – traditional recipe Ingredients: Prepared satellite images Spatial framework Schema Field-data A maximum likelihood classifier

  4. LCM2007: <20,000 useable training and validation points Training and Validation: field campaign

  5. A region of Norfolk, Suffolk: ~21,000 training polygons; > 1.25 million training pixels Training: History from 3 CEH LCMs

  6. 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

  7. Surface probability for each type, Arable

  8. Surface probability, Coniferous Woodland

  9. Results: < 1hr (previously 2-4 weeks)

  10. Norwich in 2002 as pixels

  11. Norwich as Land Parcels

  12. Lakenheath, Thetford Forest

  13. Lakenheath, Thetford Forest

  14. Accuracy

  15. Correspondence with CS

  16. Correspondence with CS

  17. Areal correspondence CS1998, Norfolk 2002

  18. 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

More Related