1 / 12

Correlation Between Ground Vegetation Parameters and Remote Sensing Endmember Fractions

Explore the relationship between ground-based vegetation measurements and remotely sensed endmember fractions of varying resolutions. The study analyzes fire/fuel loads, Spectral Mixture Analysis (SMA), and Multiple Endmember SMA (MESMA) to assess biomass correlations. Acknowledges USFS funding. Investigates the importance of fire, increasing fuel loads, and urban encroachment. Discusses how to study fire fuel loads through ground-based sampling and correlating indices from image processing techniques.

abel
Download Presentation

Correlation Between Ground Vegetation Parameters and Remote Sensing Endmember Fractions

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. An assessment of correlation between vegetation parameters measured on the ground and endmember fractions from remotely sensed data of varying spatial resolution Seth Peterson Department of Geography, University of California, Santa Barbara

  2. Acknowledgements: USFS - 4 years of funding

  3. Presentation Overview 1) Fire / fuel loads 2) SMA 3) Sample Endmember fractions 4) MESMA 5) Sample Endmember fraction / biomass correlations

  4. Why is fire important? • Fuel loads have increased • Urban encroachment into wildlands • These processes may be different for different ecosystems (study sites are in 5 western states)

  5. How can we study fire fuel loads? • Massive amounts of ground-based sampling • Small, well-designed ground-based studies to calibrate large area remotely sensed scenes - Correlate different indices and products from image processing techniques with ground-based data

  6. Spectral Mixture Analysis (SMA) • Expresses pixel values as mixtures of the scene components, • called endmembers (EMs) • Typical EMs used are: • green vegetation (GV -- e.g. green leaves) • nonphotosynthetic vegetation (NPV -- e.g. bark, branches, litter) • rocks, soils • shade

  7. GV NPV Landsat TM imagery for MCAS Miramar with Endmember fraction images Soil Shade

  8. ADARdata, 1 m pixels Landsat TM data, 30 m pixels The mixed pixel problem / Endmember analysis GV Soil NPV

  9. GV soil soil NPV shade shade GV Feature space plots for the MCAS Miramar Landsat TM scene, with approximate EM locations Band 7 Band 4 Band3 Band4

  10. soil_2 NPV_2 Band 7 soil_1 NPV_1 shade_photo GV_2 GV_1 shade_phyto Band4 Multiple Endmember SMA (MESMA) • - Allows for flexibility in the number of EMs used to model each pixel • - Allows for flexibility in the type of EMs used to model each pixel • Modeled EM fractions will be most accurate when the fewest, • most appropriate EMs are used to model each pixel

  11. GV NPV Soil Shade EM Fractions vs. time for stands of chamise chaparral

  12. Summary Fire is a problem Remote Sensing is one way to look at it

More Related