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Remote Sensing of Vibrio spp

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Presentation on theme: "Remote Sensing of Vibrio spp"— Presentation transcript:

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

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

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

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

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

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

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

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

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

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

12 Vibrio spp. Modeling in the Chesapeake Bay
Probability of presence V. vulnificus V. parahaemolyticus Model GLM GAM RF ACC 0.63 0.72 0.68 0.62 0.67 Abundance V. vulnificus V. parahaemolyticus Model GLM GAM RF MEAN MAE 4.69 4.79 3.87 4.39 7.43 7.51 5.76 6.34 HYBRID abundance V. vulnificus V. parahaemolyticus ABUNDANCE 3.87 4.39 5.76 6.34 HYBRID/P 2.79 4.30 4.36 5.83 HYBRID 2.94 3.44 5.26 6.12 Urquhart et al nd Symposium on Advances in Geospatial Technologies for Health August 27, Session 30: ISPRS: Infectious and Vector-borne Diseases II

13 Summary Urquhart et al nd Symposium on Advances in Geospatial Technologies for Health August 27, 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) - In situ – remote sensed measurement matchups 68 CBay Program in situ stations Single pass RS ocean color data 1km radius RS averaging Introduce NEURAL NETWORK A BIT HERE! - Salinity Prediction Models GLM CART GAM BCART ANN RF MARS BART - Cross- validation study

16 Satellite-derived Salinity Algorithms
GAM ANN GLM CART BCART RF MEAN BART MARS MAE 1.82 1.85 1.93 2.39 2.38 2.06 3.72 2.04 1.98 RMSE 2.50 2.53 3.03 3.01 2.67 4.69 2.60 2.52 MAE GLM GAM ANN MEAN East for West 2.1 1.8 1.7 3.3 West for East 2.6 2.8 4.0 4.1 North for South 3.4 5.9 5.7 South for North 3.0 6.4 6.1 High for Low 2.3 4.2 Low for High 2.5 3.9 MENTION HERE ABOUT POOR ACCURACY IN UPPER BAY Top performing prediction models: GAM and ANN All models outperform MEAN GLM and GAM are fairly generalizable in a cross-validation study Urquhart et al. (2012). RSE

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