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

USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests. GLC2000-JRC March 2001. The USGS/IGBP Global Land Cover Database. USGS/IGBP Global Land Cover Database Strategy. Classification Methods. Flexible land cover database

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

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  1. USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests GLC2000-JRC March 2001

  2. The USGS/IGBP Global Land Cover Database

  3. USGS/IGBP Global Land Cover Database Strategy

  4. 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

  5. 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

  6. Quality of Reference Data is an Important Factor

  7. EROS Data Center FRA2000 50% FOREST 100% AG 100% FOREST BRIGHTMODEL Channel 2 (NIR) DARKMODEL AVHRR Channel 1 (Visible) Global Forest Cover Mapping Canopy Density Model

  8. EDC FRA2000 Project:— Estimating density of forest canopy cover …

  9. A New Global Forest Cover MapImproved USGS global land cover database

  10. Sampling-based field data Current and Future R&D 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

  11. 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

  12. 0 100% Example of Tree Canopy Density

  13. Sample1: U1, V1 at (x1, y1) Calculate U0 at (x0, y0) Sample2: U2, V2 at (x2, y2) Sample3: V3 at (x3, y3) 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 =

  14. Key Experimental Vegetation Type and Structure Variables • Biomass • Net primary productivity • Canopy density • Canopy height • Age • Size class • DBH • Vegetation species, types, associations

  15. Summary • EDC is committed to continuing its global land cover R&D • Working with partners is important for USGS

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