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)