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Remote Sensing of Vibrio spp. bacteria in the Chesapeake Bay Estuary, MD

Remote Sensing of Vibrio spp. bacteria in the Chesapeake Bay Estuary, MD. Erin Urquhart 1 , Ben Zaitchik 1 , Seth Guikema 1 1 Johns Hopkins University. Vibrio in Chesapeake Bay. Sunlight. Climate. ENVIRONMENT. PHYSICAL PARAMETERS Precipitation Circulation Sea surface height

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Remote Sensing of Vibrio spp. bacteria in the Chesapeake Bay Estuary, MD

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  1. Remote Sensing of Vibrio spp. bacteriain the Chesapeake Bay Estuary, MD Erin Urquhart1, Ben Zaitchik1, Seth Guikema1 1Johns Hopkins University

  2. Vibrio in Chesapeake Bay Sunlight Climate ENVIRONMENT • PHYSICAL PARAMETERS • Precipitation • Circulation • Sea surface height • BIOLOGICAL PARAMETERS • SST • Salinity • Nutrients • pH HUMANS • Bacteria • Copepods • Shellfish • Rec. water Untreated sewage Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II • V. cholerae • V. parahaemolyticus • V. vulnificus

  3. Vibrio in Chesapeake Bay * V. vulnificus * V. parahaemolyticus Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  4. Remote Sensing of Vibrio spp.in Chesapeake Bay z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Urquhart et al. (2012) RSE Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  5. Percent Satellite Coverage by Month Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  6. Percent Satellite Coverage by Month & Station Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  7. Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  8. Remote Sensing of Vibrio spp.in Chesapeake Bay z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Urquhart et al. (2013) RSE Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  9. z(V.c.)= -1.1939 + (0.1233 * Temp) – (0.1997 * Saln) – (0.0324 * (Temp * Saln) z(V.v.)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  10. V. vulnificus & V. parahaemolyticus Field Sampling Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  11. Vibrio spp. Modeling in the Chesapeake Bay • V. vulnificus and V. parahaemolyticus • 148 surface samples • Mar.-Sept. (2011 & 2012) • Probability of presence algorithms • Generalized Linear Model (GLM) • Generalized Additive Model (GAM) • Random Forest (RF) • Optimal prediction point • Bacteria abundance algorithms • HYBRID abundance algorithms • GAM/RF Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  12. Vibrio spp. Modeling in the Chesapeake Bay • Probability of presence • Abundance • HYBRID abundance Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  13. Summary Urquhart et al. 2nd Symposium on Advances in Geospatial Technologies for Health August 27, 2013 Session 30: ISPRS: Infectious and Vector-borne Diseases II

  14. Acknowledgments Johns Hopkins University, Rebecca Murphy, Matt Hoffman, Darryn Waugh Cornell University, Dr. Bruce Monger University of Delaware, Erick Geiger University of Maryland, Bradd Haley, Elisa Taviani, Arlene Chen, Rita Colwell, Anwar Huq NASA Goddard, Molly Brown, Carlos Del Castillo Funding Sources Johns Hopkins University, NASA, NSF, NIH

  15. Satellite-derived Salinity Algorithms • - MODIS-Aqua Ocean Color Standard Products • 10 Remote sensing reflectances (visible) • 2003-2010 • - In situ – remote sensed measurement matchups • 68 CBay Program in situ stations • Single pass RS ocean color data • 1km radius RS averaging • 2003-2010 - Salinity Prediction Models • GLM • GAM • ANN • MARS • CART • BCART • RF • BART - Cross- validation study

  16. Satellite-derived Salinity Algorithms • Top performing prediction models: GAM andANN • All models outperform MEAN • GLM and GAM are fairly generalizable in a • cross-validation study Urquhart et al. (2012). RSE

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