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Enhancing Emissions Estimation with Land Use/Land Cover Data

Explore the applications of land use/land cover data for emissions tracking, management, and modeling. Evaluate data quality and resolution for regional planning. Make recommendations for improving data usage.

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Enhancing Emissions Estimation with Land Use/Land Cover Data

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  1. Land Use/Land Cover Data Project Planning Team – March 9-10

  2. Land Use/Land Cover Data Issues • Dated information – some as old as the mid-80s • Different data needs for various applications • Inconsistent quality and resolution used in various emissions estimation tools used for regional haze planning • Fire • Dust • Biogenics • Fugitive ammonia • Windblown dust

  3. Applications of Land Use/Land Cover Data for Planning Purposes • Emissions Tracking and Management • Dust • Fire • Context for “painting the Reasonable Progress picture” • Emissions data resolution • Area vs. Point for regulatory purposes • Near Class I areas Emissions Characterization • Dovetail into Technical Data Portal project • Technical Uses • Emissions modeling • Windblown dust • Fugitive Ammonia • Biogenics

  4. Deliverables - Land Use/Land Cover Data Project • April - catalogue available data, prepare complete listing of available data for WRAP region • May - evaluate data for use in emissions estimation technical tools, recommend changes, annotate catalogue above • June - process data into tools • June - final website report on project

  5. GIS Data Evaluation • Spatial Resolution • Need to balance increased resolution with processing requirements/resources • Depends on expected uses of data sets • Note data in Figure 4 is derived from Figure 3 data by re-sampling to 2-km resolution – Loss in spatial detail/resolution • Temporal Changes • Landuse changes over time • Relevance for urban areas; desert/barren lands; Ag lands; coastal areas • Note yellow areas in NE regions and at S end of Bay. Between 1986 and 1993 these are increased areas of barren lands – Possibly due to increased construction activity (urban sprawl) • Attribute classification • Data classifications for specific uses • More detailed categories (wetlands, urban lands) • Trade-offs between desired resolution/attributes and available resources/data uses

  6. Figure 1. 1986 LU Data

  7. Figure 2. 1993 LU Data

  8. Figure 3. 1993 LU Data (NLCD)

  9. Figure 4. 1993 LU Data (NLCD) As gridded for WRAP Analyses

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