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A Random Walk of Issues Related to Training Data and Land Cover Mapping

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Quantifying Error in Training Data for Mapping and Monitoring the Earth System - A Workshop on “Quantifying Error in Training Data for Mapping and Monitoring the Earth System” was held on January 8-9, 2019 at Clark University, with support from Omidyar Network’s Property Rights Initiative, now PlaceFund.

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A Random Walk of Issues Related to Training Data and Land Cover Mapping

  1. 1. A Random Walk of Issues Related to Training Data and Land Cover Mapping Mark Friedl Department of Earth and Environment, Boston University GeoInnovation, Indigo Ag Quantifying Error in Training Data and its Implications for Land Cover Mapping, Clark University, Jan 8- 9, 2019
  2. 2. MODIS Land Cover Type & Dynamics (MCD12Q1, MCD12Q2) 2 Land cover type (class) Land surface dynamics (phenology)
  3. 3. Arctic-Boreal Vulnerability Experiment (ABoVE)
  4. 4. MEaSUREs: A Moderate Spatial Resolution Data Record of 21st Century Global Land Cover, Land Use, and Land Cover Change
  5. 5. Commonalities & Differences • Commonalities: • Supervised: ensemble classifiers • Highly reliant on training data • Land cover over time • Differences: • Annual classifications, followed by post-processing (not designed for change) • Time series classification, including break-point detection (explicitly designed for change)
  6. 6. MODIS Land Cover Type Training data collected opportunistically, based on ecoregion stratification No systematic assessment data collected
  7. 7. Post-Processing: Hidden Markov Models 7
  8. 8. ABoVE & MEaSUREs: Continuous Change Detection and Classification (CCDC) • Exploit entire Landsat archive of available observations at each pixel • Estimate simple time series models, identify break points, classify segments Training & assessment data being collected via stratified random sample
  9. 9. Some (pretty obvious?) observations
  10. 10. 1. Machine Learning is (too?) good at fitting to data • Over fitting is real • So is Murphy’s Law • Representative samples matter • Training data can bias results • Impact depends on class separability Figure 1. Boreal forest cover changes between 2001‐2009 for Collection 5. Figure 2. Boreal forest cover changes between 2001‐2009 for Collection 5.1. Forest Change 2001-2009 50-100% decrease 20-50% decrease 10-20% decrease <10% change 10-20% increase 20-50% increase 50-100% increase non-forest land water
  11. 11. 2. Single date imagery not sufficient for training or classification • Temporal signature (seasonality, signature of land use e.g., rotations) can be just as (or more) important than spectral signature
  12. 12. 3. Need better tools to sample spectral- temporal data space • Clustering obvious solution, but is also a black art
  13. 13. 4. Evaluation of results requires formal design • Probability sample • Hard & expensive!
  14. 14. Some additional thoughts • Minimum mapping unit – pixels vs polygons…? • Land cover, land use generally do not exist in 10, 20, 30-m blocks • Role of classification scheme complexity in uncertainty/error? • Smaller # classes -> higher accuracy, but most general classes multi-modal • Land cover is not land use! • Really easy to make bad maps, we should try not to!

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