Download - Landscape Position and Coastal Marsh Loss
The Influence of Landscape Position on Coastal Marsh Loss
Andrew S. Rogers, 2004
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
StudyArea
Two areas
~160,000 hectares each
Atlantic Coast
• More exposed
• Higher tidal range
Bay areas
• More sheltered
• Larger
Data
• National Wetlands Inventory
• Thematic Mapper Satellite Imagery
• Coastal Marsh Project marsh surface classification.
• U.S. Census Bureau TIGER files (roads)
• Field Observations
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.
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
HypothesesMarsh loss can be correlated with specific topological features
The probability of a grid cell being completely open waterHypothesis 2. is positively related to its distance from the nearest Hypothesis 2. is positively related to its distance from the nearest
tidal creektidal creekHypothesis 4. is positively related to its distance from the nearest Hypothesis 4. is positively related to its distance from the nearest
upland.upland.
Hypothesis 1. is negatively related to its distance from a road.negatively related to its distance from a road.
Hypothesis 3. is negatively related to its distance upstream.Hypothesis 3. is negatively related to its distance upstream.
Hypothesis 5. is negatively related to the size of the marsh parcel Hypothesis 5. is negatively related to the size of the marsh parcel containing the grid cellcontaining the grid cell
..
Estuary/Tidal Creek
Upland
Simplified Mass Balance
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic LayerPO4
3-,NO3-
Sediment
Ground Water Fe3+
SO32-,Na+
Sediment
Estuary/Tidal Creek
Upland
Simplified Mass Balance
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic LayerPO4
3-,NO3-
Sediment
Ground Water Fe3+
SO32-,Na+
Sediment
Incoming tide adds nutrients, salt and sediment. Runofffrom uplands provides nutrients and sediment.
Estuary/Tidal Creek
Upland
Simplified Mass Balance
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic LayerPO4
3-,NO3-
Sediment
Ground Water Fe3+
SO32-,Na+
Sediment
A levee begins to build near the tidal creek, capturingsediments there.
Levee
Estuary/Tidal Creek
Upland
Simplified Mass Balance
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic LayerPO4
3-,NO3-
Sediment
Ground Water Fe3+
SO32-,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
Estuary/Tidal Creek
Upland
Simplified Mass Balance
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic LayerPO4
3-,NO3-
Sediment
Ground Water Fe3+
SO32-,Na+
Sediment
Road
Construction of a road can trap sediments on the landward side.
Levee
Estuary/Tidal Creek
Upland
Simplified Mass Balance
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic Layer
Ground Water Fe3+
SO32-,Na+
Sediment
Road
Incoming spring tide can wash over the road leaving a pool of water that does not drain rapidly. Salinity canincrease and plants may be stressed or drowned.
Levee
PO43-,NO3
-
Sediment
Estuary/Tidal Creek
Upland
Predicting Marsh Loss
Marsh
Surface Water
Anoxic Layer
Peat
Air
Oxic LayerPO4
3-,NO3- SO3
2-,Na+
Sed
imen
tA
ccre
tion
Sediment
Ground Water Fe3+
Sediment
Sea
Lev
el R
ise
The marsh will tend to survive where sediment accretion and relative sea level height remain in balance and disappear elsewhere.
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.
Marsh Loss
Distance from Upland
Actual drivers which distance from upland represents in the model are likely to be
o elevationo freshwater runoff o nutrient supplyo sediment supplyo physical stability
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
Methods
Distance from land
CMP Remote Sensing ModelsAir 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.
Remote Sensing
1) represents spectrally pure endmembers (cover types)
2) Ri = linear, weighted sum of the radiances
3) fj = fractional coverages can be recovered
4) number of endmembers ≤ number of bands, i
5) ∑ fj = 1
6) 0 < fj < 1 for all fj's.
n
jijiji fR
1
e
Mixture Modeling Theory
Endmember Spectra
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
300 600 900 1200 1500 1800 2100 2400
Wavelength (nanometers)
Ref
lect
ance
Soil
Soil
Soil
Vegetation
Vegetation
Water
Water
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
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
Validation of remote sensing model
Table 14 Validation of Coastal Marsh Loss Project Data on the Delaware Bay
Actual Row Totals
Healthy Degraded
PredictedHealthy 178 1 179
Degraded 0 6 6
Column Totals 178 7 185
Percent predicted correctly
Healthy 99G-Adjusted 47.2051
Degraded 100Chi-square value 10.828
Total 99Level 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.
83% accurate for classification into four classes (Stevens, 1997).
Results
ln(probability) By ln(distance)Linear FitSummary of Fit Rsquare 0.971589Root Mean Square Error 8.718909Mean of Response -2.21375Observations (or Sum Wgts) 1862093
Effect of Tidal Creeks on Atlantic Coast Marshes
0.005
0.02
0.05
0.16
0.5
10 100 1000 10,000 distance from the nearest tidal water (meters)
prob
abil
ity
of d
egra
dati
on
Results
ln(probability) By ln(Distance from Land)Linear FitSummary of Fit Rsquare 0.871136Root Mean Square Error 13.42716Mean of Response -3.15712Observations (or Sum Wgts) 2178017
Effect of distance from uplands on marshes in the Chesapeake and Delaware Bays
prob
abil
ity
of d
egra
dati
on
0.06
0.1
0.16
0.25
0.40
0.63
1.0
10 100 1000 10,000 distance from nearest land (meters)
Results
ln(probability) By ln(Upstream Distance)
Linear FitSummary of Fit Rsquare 0.237701Root Mean Square Error 20.18101Mean of Response -3.12739Observations (or Sum Wgts) 2175076
Effect of Upstream Distance on Atlantic Coast Marshes
prob
abil
ity
of d
egra
dati
on
0.001
0.003
0.01
0.04
0.16
0.32
10 100 1000 10,000 100,000 distance upstream (meters)
Results
ln(probability) By ln(area)Polynomial Fit, degree=2Summary of Fit Rsquare 0.391991Root Mean Square Error 33.05177Mean of Response -2.21751Observations (or Sum Wgts) 1884171
Effect of marsh size on Atlantic Coast Marshes
prob
abil
ity
of d
egra
dati
on
0.01
0.025
0.06
0.16
0.40
0.1 1 10 100 1000 area (hectares)
ResultsHypothesis:Marsh loss is correlated with:
Predicted Correlation Correlation Found R-Square Conclusion
Distance from Roads
Negative Positive 0.419392 Rejected
Negative Positive 0.97685 Rejected
Distance from Tidal Creeks
Positive Positive 0.990931 Not rejected
Positive Positive 0.971589 Rejected
Distance Upstream
Negative Negative 0.264358 Not rejected
Negative Negative 0.237701 Not rejected
Distance from Upland
Positive Positive 0.871136 Not rejected
Positive Positive 0.88531 Not rejected
Area
Negative Variable 2nd order 0.280912 Rejected
Negative Variable 2nd order 0.391991 Rejected
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
Primary Drivers• Landscape or local? Management issues.• Marsh processes are influenced by events happening at the
landscape scale.
• Wildlife control• Burning
Conclusion
Marsh loss is driven, in part, by landscape scale features that are not measurable from local in situ measurements.
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
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)