USGSEROS Data Center Global Land Cover Project Experiences and Research Interests

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USGS/EROS Data Center Global Land Cover Project
Experiences and Research Interests

• GLC2000-JRC
• March 2001

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The USGS/IGBP Global Land Cover Database
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USGS/IGBP Global Land Cover Database Strategy
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Classification Methods

• Flexible land cover database
• Unsupervised multi-temporal classification of
  1992-1993 AVHRR NDVI data
• Classification implemented on a continent by
  continent basis

• Team interpretation to encourage consistency
• External peer review of draft results
• Validated IGBP land cover layer

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Continents to World Combine Maps

• Set rules for top and middle level classification
  systems
• Describe land cover, vegetation seasonality,
  structure, and leave longevity consistently

• Hold frequent project meetings to review
  consistency
• Accuracy measured separately for each mapping area

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Quality of Reference Data is an Important Factor
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Global Forest Cover Mapping
EROS Data Center FRA2000
Canopy Density Model
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EDC FRA2000 Project Estimating density of
forest canopy cover
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A New Global Forest Cover MapImproved USGS
global land cover database
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Current and Future RD Interests

• Continue global land cover database research
  using new coarse/moderate resolution sensors
• Test new techniques/algorithms
• Integrate satellite imagery with sampling-based
  field data

• Focus on attributes, themes that are useful for
  both science and land management

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Land Cover Techniques at EDC

• Unsupervised classification
• Decision-tree models
• Spectral mixture analysis
• Experimental Co-kriging, KNN, NN
• Continued emphasis on database strategy and its
  improvement
• Stratification before and after clustering

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Example of Tree Canopy Density
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Spatial Modeling Techniques for Satellite
Imagery-Field Data Integration

• Spatial models such as KNN, Co-kriging are
  nonparametric spatial statistics
• Potential tool for extending field measurements
  to image data/maps
• Mapping vegetation structure measured on
  permanent plots

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Key Experimental Vegetation Type and Structure
Variables

• Biomass
• Net primary productivity
• Canopy density
• Canopy height
• Age
• Size class
• DBH
• Vegetation species, types, associations

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Summary

• EDC is committed to continuing its global land
  cover RD
• Working with partners is important for USGS