Landscape Position and Coastal Marsh Loss

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Discussion of how landscape position affects coastal salt marsh loss.

Discussion of how landscape position affects coastal salt marsh loss.

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  • 1. The Influence of Landscape Position on Coastal Marsh Loss Andrew S. Rogers, 2004
  • 2. Purpose
    • Determine likelihood of conversion of an area of marsh to open water.
    • To provide information that may help determine:
      • which areas are at risk
      • which areas can be saved
      • which areas can be restored
  • 3. Study Area
    • Two areas
    • ~160,000 hectares each
    • Atlantic Coast
    • More exposed
    • Higher tidal range
    • Bay areas
    • More sheltered
    • Larger
  • 4. Data
    • National Wetlands Inventory
    • Thematic Mapper Satellite Imagery
    • Coastal Marsh Project marsh surface classification.
    • U.S. Census Bureau TIGER files (roads)
    • Field Observations
  • 5. Research Issues
    • Most marsh studies take place on very small scales.
    • This may not be appropriate to understanding the function of salt and brackish marshes.
    • The landscape scale used here may better predict where marsh loss will occur than local measurements of various changing parameters.
  • 6. Marsh Loss
    • Loss of a parcel of marsh is a result of impacts to that parcel of marsh.
    • Types of impacts :
      • Herbivory
      • Lack of nutrients
      • Lack of sediment
      • Death of macrophytes
      • Loss of soil structure
      • Excessive waterlogging
      • Excessive salinity
      • Failure to keep pace with relative sea level rise
      • Human activities
  • 7. Hypotheses Marsh loss can be correlated with specific topological features The probability of a grid cell being completely open water Hypothesis 2. is positively related to its distance from the nearest tidal creek Hypothesis 4. is positively related to its distance from the nearest upland. Hypothesis 1. is negatively related to its distance from a road. Hypothesis 3. is negatively related to its distance upstream. Hypothesis 5. is negatively related to the size of the marsh parcel containing the grid cell .  
  • 8. Simplified Mass Balance Estuary/ Tidal Creek Upland Marsh PO 4 3- ,NO 3 - Sediment Ground Water Fe 3+ SO 3 2- ,Na + Sediment Surface Water Anoxic Layer Peat Air Oxic Layer
  • 9. Simplified Mass Balance Estuary/ Tidal Creek Upland Marsh PO 4 3- ,NO 3 - Sediment Ground Water Fe 3+ SO 3 2- ,Na + Sediment Incoming tide adds nutrients, salt and sediment. Runoff from uplands provides nutrients and sediment. Surface Water Anoxic Layer Peat Air Oxic Layer
  • 10. Simplified Mass Balance Estuary/ Tidal Creek Upland Marsh PO 4 3- ,NO 3 - Sediment Ground Water Fe 3+ SO 3 2- ,Na + Sediment A levee begins to build near the tidal creek, capturing sediments there. Levee Surface Water Anoxic Layer Peat Air Oxic Layer
  • 11. Simplified Mass Balance Estuary/ Tidal Creek Upland Marsh PO 4 3- ,NO 3 - Sediment Ground Water Fe 3+ SO 3 2- ,Na + Sediment Levee building results in high sediment input and frequent flushing at creek edge, low sediment input with longer water residence times in midmarsh areas. Levee Surface Water Anoxic Layer Peat Air Oxic Layer
  • 12. Simplified Mass Balance Estuary/ Tidal Creek Upland Marsh PO 4 3- ,NO 3 - Sediment Ground Water Fe 3+ SO 3 2- ,Na + Sediment Road Construction of a road can trap sediments on the landward side. Levee Surface Water Anoxic Layer Peat Air Oxic Layer
  • 13. Simplified Mass Balance Estuary/ Tidal Creek Upland Marsh Ground Water Fe 3+ SO 3 2- ,Na + Sediment Road Incoming spring tide can wash over the road leaving a pool of water that does not drain rapidly. Salinity can increase and plants may be stressed or drowned. Levee PO 4 3- ,NO 3 - Sediment Surface Water Anoxic Layer Peat Air Oxic Layer
  • 14. Predicting Marsh Loss Estuary/ Tidal Creek Upland Marsh PO 4 3- ,NO 3 - SO 3 2- ,Na + Sediment Accretion Sediment Ground Water Fe 3+ Sediment Sea Level Rise The marsh will tend to survive where sediment accretion and relative sea level height remain in balance and disappear elsewhere. Surface Water Anoxic Layer Peat Air Oxic Layer
  • 15. Aerial photo of Blackwater National Wildlife Refuge. A road transects the marsh from top to bottom. Note the much greater loss on the right, downstream from the road – possibly due to sediment trapping upstream of the road.
  • 16. Marsh Loss
  • 17. Distance from Upland
    • Actual drivers which distance from upland represents in the model are likely to be
    • elevation
    • freshwater runoff
    • nutrient supply
    • sediment supply
    • physical stability
  • 18. Distance Upstream
    • Distance upstream probably represents these drivers
    • plant community change with distance upstream
    • relative sea level rise
      • drowns the downstream marshes
      • kills species that are less tolerant to salt and anaerobic conditions further upstream.
    • increased sulfide from seawater sulfate ions
    • higher inputs of sediment upsteam
  • 19. Methods
    • Distance from land
  • 20. CMP Remote Sensing Models Air Photo Marsh Loss Model
    • Marsh loss can be attributed to:
      • Widening of tidal creeks.
      • Formation of interior ponds
      • Coalescence of interior ponds
    Satellite Image Marsh Loss Model
      • Areas that have open water now will have more open water later.
  • 21. Remote Sensing
    • 1)  represents spectrally pure endmembers (cover types)
    • 2) R i = linear, weighted sum of the radiances
    • 3) f j = fractional coverages can be recovered
    • 4) number of endmembers ≤ number of bands, i
    • 5) ∑ f j = 1
    • 6) 0 < f j < 1 for all f j 's.
    Mixture Modeling Theory
  • 22. 1 2 3 4 5 7 TM Bands Bands 1, 2 and 7 from Richards, 1986; Bands 3, 4 and 5 from CMP TM Images
  • 23. Remote Sensing
    • Equation 2 NDWI
    • NDWI = (Band3 - Band5) / (Band3 + Band5)
    • Equation 3 NDVI
    • NDVI = (Band4 - Band3) / (Band4 + Band3)
    • Equation 4 NDSI
    • NDSI = (Band5 - Band4) / (Band5 + Band4).
    Technique
  • 24. Validation of remote sensing model 83% accurate for classification into four classes (Stevens, 1997). Table 14 Validation of Coastal Marsh Loss Project Data on the Delaware Bay     Actual Row Totals Healthy Degraded   Predicted Healthy 178 1 179 Degraded 0 6 6   Column Totals 178 7 185 Percent predicted correctly     Healthy 99 G-Adjusted 47.205 1 Degraded 100 Chi-square value 10.828 Total 99 Level of Significance 0.001 1 A value of 10 -15 was added to the zero-valued cell in the contingency table to calculate the G value because the calculation involves a logarithm.
  • 25. Results ln(probability) By ln(distance) Linear Fit Summary of Fit Rsquare 0.971589 Root Mean Square Error 8.718909 Mean of Response -2.21375 Observations (or Sum Wgts)  1862093 Effect of Tidal Creeks on Atlantic Coast Marshes
  • 26. Results ln(probability) By ln(Distance from Land) Linear Fit Summary of Fit Rsquare 0.871136 Root Mean Square Error 13.42716 Mean of Response -3.15712 Observations (or Sum Wgts)  2178017 Effect of distance from uplands on marshes in the Chesapeake and Delaware Bays
  • 27. Results ln(probability) By ln(Upstream Distance) Linear Fit Summary of Fit Rsquare 0.237701 Root Mean Square Error 20.18101 Mean of Response -3.12739 Observations (or Sum Wgts)  2175076 Effect of Upstream Distance on Atlantic Coast Marshes
  • 28. Results ln(probability) By ln(area) Polynomial Fit, degree=2 Summary of Fit Rsquare 0.391991 Root Mean Square Error 33.05177 Mean of Response -2.21751 Observations (or Sum Wgts)  1884171 Effect of marsh size on Atlantic Coast Marshes
  • 29. Results Rejected 0.391991 Variable 2 nd order Negative Rejected 0.280912 Variable 2 nd order Negative Area Not rejected 0.88531 Positive Positive Not rejected 0.871136 Positive Positive Distance from Upland Not rejected 0.237701 Negative Negative Not rejected 0.264358 Negative Negative Distance Upstream Rejected 0.971589 Positive Positive Not rejected 0.990931 Positive Positive Distance from Tidal Creeks Rejected 0.97685 Positive Negative Rejected 0.419392 Positive Negative Distance from Roads Conclusion R-Square Correlation Found Predicted Correlation Hypothesis: Marsh loss is correlated with:
  • 30. Discussion
    • The goal of this research was to show that marsh loss is not a random process 
    • Hypotheses are supported across landscapes and marsh types 
    • Distance from land and distance upstream - impact both Atlantic Coast marshes and Chesapeake and Delaware Bay marshes
  • 31. Primary Drivers
    • Landscape or local? Management issues.
    • Marsh processes are influenced by events happening at the landscape scale.
    • Wildlife control
    • Burning
  • 32. Conclusion
    • Marsh loss is driven, in part, by landscape scale features that are not measurable from local in situ measurements.
  • 33. Further Study
    • What resolution of imagery would be best?
      • To differentiate tidal creeks that are sources of tidal water and sediment from those that are loci of marsh loss.
      • Impact of different plant species on the longevity of the marsh could be assessed statistically.
    • Differentiate between “other” and upland
    • Nutria population density  
    • Management techniques
    • Development
  • 34. Acknowledgements
    • Coastal Marsh Project PIs
    • Michael Kearney, John Townshend, William Lawrence
    • Coastal Marsh Project Graduate Students
    • Jennifer Stevens, Janine Savage, David Stutzer, Kate Eldred, Frank Lindsey and Eric Rizzo
    • Committee Members
    • Michael Kearney, John Townshend, Ivar Strand, Court Stevenson, Dave Wright
    • Undergraduate Students
    • Deanna Guerieri and Nicole Hale
    • Proofreaders, boat carriers, critics and other helpers
    • Beth Rogers, Michael Rogers, Chris Rogers, Pam Heberer, Lisa Wainger, SeJong Ju, William Rogers, Anna Hight, and Rae Benedict
    • Funding and support
    • NASA’s Mission to Planet Earth funded the Coastal Marsh Project
    • Grant number: NAGW3758, Mr. Alex Tuyahov, Program Manager.
    • University of Maryland College Park sponsored the Coastal Marsh Project
    • Chesapeake Biological Laboratory provided equipment and resources
    • Navair (National Range Sustainability Office)