National Park Service

U.S. Department of the Interior

Natural Resource Stewardship and Science

Spatially Explicit Modeling of Coastal Vegetation 

Change Associated with Projected Sea Level Rise 

The Potomac Estuary

Natural Resource Report NPS/NCRN/NRR—2015/1034
ON THIS PAGE

Monarch and black swallowtail butterflies on flowers at Dyke Marsh Wildlife-Preserve (GWMP)

NPS photograph by Thomas Paradis

ON THE COVER

Estuarine Open Water near Dyke Marsh Wildlife Preserve (GWMP).

NPS photograph by Thomas Paradis
Spatially Explicit Modeling of Coastal Vegetation

Change Associated with Projected Sea Level Rise

The Potomac Estuary

Natural Resource Report NPS/NCRN/NRR—2015/1034

Andrew J. Elmore1, Daniel Cadol2, Steven M. Guinn1, Geoffrey M. Sanders3, Katia AM Engelhardt1, 

Mathew C. Fitzpatrick1

1University of Maryland Center for Environmental Science

Appalachian Laboratory

301 Braddock Rd

Frostburg, MD 21532

2New Mexico Institute of Mining and Technology

Department of Earth & Environmental Science

Socorro, NM 87801

3National Park Service

National Capital Region I&M Program

Center for Urban Ecology

4598 MacArthur Blvd., NW

Washington, DC 20007

September 2015

U.S. Department of the Interior

National Park Service

Natural Resource Stewardship and Science 

Fort Collins, Colorado
ii

The National Park Service, Natural Resource Stewardship and Science office in Fort Collins, 

Colorado, publishes a range of reports that address natural resource topics. These reports are of 

interest and applicability to a broad audience in the National Park Service and others in natural 

resource management, including scientists, conservation and environmental constituencies, and the 

public.

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This report is available in digital format from the Capital Region Network Parks

(http://science.nature.nps.gov/im/units/ncrn/publications.cfm) and the Natural Resource Publications 

Management website (http://www.nature.nps.gov/publications/nrpm/). To receive this report in a

format optimized for screen readers, please email irma@nps.gov.

Please cite this publication as:

Elmore, A. J., D. Cadol, S. M. Guinn, G. M. Sanders, K. A. M. Engelhardt, and M. C. Fitzpatrick. 

2015. Spatially explicit modeling of coastal vegetation change associated with projected sea level 

rise: The Potomac estuary. Natural Resource Report NPS/NCRN/NRR—2015/1034. National Park 

Service, Fort Collins, Colorado.

NPS 800/129772, September 2015
iii

Contents

Page

Figures....................................................................................................................................................v

Tables...................................................................................................................................................vii

Appendices............................................................................................................................................ix

Executive Summary..............................................................................................................................xi

Acknowledgments...............................................................................................................................xiii

List of Acronyms ................................................................................................................................xiii

Introduction............................................................................................................................................1

Methods..................................................................................................................................................3

Study area .......................................................................................................................................3

Modeling overview.........................................................................................................................4

Elevation: measurement, validation, and change modeling ...........................................................4

LiDAR derived DEM ................................................................................................................4

Relative elevation calculation and DEM validation ..................................................................5

Elevation change model ............................................................................................................6

Land classification input layers....................................................................................................10

NPS commissioned vegetation layer.......................................................................................10

Forests .....................................................................................................................................10

Tidal flat ..................................................................................................................................11

Water mask..............................................................................................................................11

Weighted distance to water .....................................................................................................11

Developed land........................................................................................................................12

Initial classification map...............................................................................................................12

Sea Level Rise Scenarios..............................................................................................................13

Monte Carlo uncertainty analysis.................................................................................................14

Implementation of land class switching rules ..............................................................................15
iv

Contents (continued)

Page

Model output description..............................................................................................................17

Results and Discussion ........................................................................................................................18

Digital elevation model validation ...............................................................................................18

Initial classification results...........................................................................................................18

Effect of T0 correction on the initial vegetation classification ....................................................20

Projected trends in vegetation class areas ....................................................................................22

Sources and analysis of uncertainty in vegetation class areas......................................................28

Literature Cited ....................................................................................................................................31
v

Figures

Page

Figure 1: Eleven modeling regions were defined to cover all NPS lands along the 

Potomac Estuary within 5m of 2008 sea level.......................................................................................3

Figure 2: RTK-GPS was used to validate the DEM used in MAIM......................................................5

Figure 3. RTK GPS survey points used to generate the MAIM elevation change submodel. Details are discussed in Cadol et al. 2014..................................................................................6

Figure 4. The MAIM tide stick for the mean Monte Carlo permutation showing relative 

positions of each vegetation class and absolute positions in half tide units...........................................8

Figure 5. Decision tree used to generate the initial vegetation classification. .......................................9

Figure 6. Graphical depiction of the probability distributions (first and third quartiles) 

given for the boundaries between vegetation classes...........................................................................15

Figure 7. DEM elevations vs. RTK-GPS survey elevations showing bias and systematic 

error in the DEM used in this report (Feb 2012 only; 1341 pts)..........................................................18

Figure 8: Dyke Marsh as represented in the initial vegetation classification before the T0 

correction. ............................................................................................................................................20

Figure 9. Dyke Marsh Preserve vegetation classification map............................................................23

Figure 10. Change in vegetation class areas over time (solid lines) and the first and third 

quartiles of the class area distributions resulting from the Monte Carlo uncertainty 

modeling for the George Washington Memorial Parkway modeling region B. ..................................25

Figure 11: Model results presented in Tables A1, A2, and A3 can also be presented in 

graphical form......................................................................................................................................26

Figure 12: Model results for a large portion of Piscataway Park.........................................................27

Figure B1: The southern-most section of the George Washington Memorial Parkway 

habitat classification map.....................................................................................................................37

Figure B2: The central section of the George Washington Memorial Parkway habitat 

classification map.................................................................................................................................38

Figure B3: Central section of National Capital Parks – East along the Potomac River 

(Harmony Hall) habitat classification map ..........................................................................................39

Figure B4: Central section of National Capital Parks – East along the Potomac River 

(Oxon Cove) habitat classification map...............................................................................................40

Figure B5: Section of the George Washington Memorial Parkway around Dangerfield 

Island and Reagan National Airport.....................................................................................................41
vi

Figures (continued)

Page

Figure B6: Theodore Roosevelt Island habitat classification map. .....................................................42

Figure B7: Little Falls vegetation classification map ..........................................................................43

Figure B8: Lands of National Capital Parks – East along the Anacostia River including 

Anacostia Park and Kenilworth Park and Aquatic Gardens. ...............................................................44

Figure B9: National Capital Parks East along the Anacostia River.....................................................45

Figure B10: Theodore Roosevelt Island habitat classification map ....................................................46

Figure B11: The Piscataway Park section of National Capital Parks East habitat 

classification map.................................................................................................................................47

Figure B12: The eastern section of Piscataway Park (National Capital Parks East) habitat 

classification map.................................................................................................................................48
vii

Tables

Page

Table 1. Weighted distance from water (dw, unitless) thresholds for swamp forest vs. 

tidal swamp differentiation. .................................................................................................................13

Table 2. Projections for Maryland relative sea level rise. ...................................................................14

Table 3. Field validation of initial classification map .........................................................................19

Table 4: Vegetation class areas (ha) in the initial vegetation classification and class areas 

(and change) after the T0 correction, which accounts for differences between the initial 

classification and the MAIM tide stick................................................................................................21

Table A1. Summary of area (ha) and change over time for each vegetation class 

associated with the low SLR scenario and tabulated for the entire study region.................................33

Table A2. Summary of area (ha) and change over time for each vegetation class 

associated with the medium SLR scenario and tabulated for the entire study region..........................34

Table A3. Summary of area (ha) and change over time for each vegetation class 

associated with the medium SLR scenario and tabulated for the entire study region..........................35

ix

Appendices

Page

Appendix A: Summary statistics for SLR scenarios............................................................................33

Appendix B: Multi-temporal maps showing MAIM results for the high SLR scenario for 

each modeling region...........................................................................................................................35

xi

Executive Summary

Coastal environments are expected to respond to rising sea levels through migration inland. This 

process is limited by the availability of corridors of sufficiently flat, undeveloped land to be 

converted to wetland. Developed land protected from tidal influence through the construction of 

bulkheads and levies and natural areas of elevated land will decrease the area available for 

conversion to marsh and wetland forest over time, leading to a loss of biodiversity. Because these 

processes depend strongly on the spatial configuration of vegetation-elevation relationships, they 

must be modeled within a framework that accounts for the specific elevation ranges over which 

different vegetation classes persist, and elevation change (accretion net of settling and compaction)

across the full range of affected elevations. For the National Parks along the Potomac River Estuary, 

models must operate at both a high spatial resolution and over broad spatial extents to capture 

changes at scales relevant to park resource managers. This area encompasses multiple park entities, 

each with their own management needs and goals, but some practical applications of sea-level rise

impact projections common to all areas are: (1) targeting areas for pre-emptive actions such as 

removal of developed land features and marsh species plantings, and (2) strategic land management 

partnerships, with the goal of maintaining regional biodiversity.

This project produced a spatially explicit computational model (termed the Marsh Accretion and 

Inundation Model (MAIM)) and model results predicting the impact of user-defined sea-level rise 

scenarios on vegetation. The model takes as input detailed (1-m resolution) map layers representing 

elevation (generated from LiDAR) and initial vegetation classification. Vegetation classes are 

constrained to predetermined individual elevation ranges, based on NPS vegetation maps, plot 

inventory data, and digitalization of aerial photography. Uncertainty in these elevation ranges is 

represented through 100 Monte Carlo permutations of the vegetation class elevation boundaries. As 

sea-level rise progresses, the model identifies where vegetation classes will become unsuitable for 

their location, and adjusts the map accordingly. At each time step, elevation change is modeled using 

an empirical model based on real-time kinematic GPS surveys spanning 20 years of historic sea-level 

rise. The accretion sub-model is a non-linear function of elevation, but is not separately 

parameterized for each vegetation class, thus maintaining smooth topography gradients between 

vegetation classes. The model also takes into account current species distributions that are likely to 

persist longer under saturated soil conditions, thus realistically modeling elements of landscape 

change important for resource conservation. MAIM is not a dynamic model and therefore does not 

alter elevation change in response to accelerated sea-level rise or any other environmental conditions; 

MAIM assumes that the relationship between accretion and elevation observed in historical data is 

adequate for modeling future conditions. It also does not account for hydrologic interaction with the 

watershed that might be expected to increase inundation times in areas of high flow accumulation 

area. MAIM is not a sediment dynamics model, and therefore cannot respond to changes in 

suspended-sediment concentration in estuarine waters. Finally, MAIM does not model shoreline 

erosion, as this was not found to be a dominant predictable process over the majority of the study 

area.
xii

The combination of carefully parameterized modeled processes and simple run-time efficiency make 

MAIM an ideal tool for resource managers seeking to predict changes in biological resources over 

the next 50 to 100 years. The model output represents detailed vegetation change projections at 10-

year intervals, including the probability of occurrence for each modeled grid cell. Output also 

includes tables and line graphs that can be used to analyze trends and divergence in probability 

results over time. All results are included for 3 sea-level rise scenarios, all based on the best available 

scientific projections of future conditions. Because the study region includes extensive areas of 

developed land within 5m of current sea level, we also provide two case-scenarios, one in which 

developed land is protected from the effects of sea-level rise through the construction of bulkheads 

and levies and a second in which developed land is abandoned as it becomes inundated, leading to 

conversion to marsh habitat. Overall, the results from each scenario provide a unique landscape-level 

perspective of the impacts of sea-level rise for which park resources managers must be prepared.
xiii

Acknowledgments

The authors acknowledge the support of the National Park Service through a cooperative agreement 

with the University of Maryland Center for Environmental Science (UMCES), Appalachian 

Laboratory (#H3992060006). K Hawkins digitized the Potomac Estuary shorelines. J Cessna assisted 

with fieldwork. 

List of Acronyms

HTU Half Tide Units

SLR Sea-level rise

UMCES University of Maryland Center for Environmental Science

NPS National Park Service

SLAMM Sea Level Affecting Marshes Model

MAIM Marsh Accretion and Inundation Model

LiDAR Light Detection and Ranging

NCR National Capital Region

DEM Digital Elevation Model

NAIP National Agriculture Inventory Program

MTL Mean Tide Level

GT Great diurnal tide range

NAVD88 North American Vertical Datum 1988

MCCC Maryland Climate Change Commission

IPCC Intergovernmental Panel on Climate Change

DDL Developed Dry Land

UDL Undeveloped Dry Land

WFS Wetland Forest Species

TS Transitional Shrub

WF Wetland Forest

IFF Irregularly Flooded Forest

EFM Ephemerally Flooded Marsh

IFM Irregularly Flooded Marsh

RFM Regularly Flooded Marsh

TF Tidal Flats

EOW Open Estuarine Water

HUD Heads Up Digitized

1

Introduction

Marsh habitats occupy a limited range of relative elevations within the tidal frame. As mean sea level 

changes, elevations that fall into this range respond by migrating landward or seaward. However, the 

area available for migration of lower-elevation tidal ecosystems can be limited by engineering 

solutions employed to protect infrastructure (e.g., from the effects of sea-level rise) that create a fixed 

landscape (Titus et al., 2009). In other areas, elevated topography can cause the same effect, such that 

sea-level rise (SLR) poses a risk to coastal habitats that are prevented from landward migration by

natural or man-made constraints (Nicholls, 1997, Nicholls et al., 1999, Nicholls and Cazenave, 

2010). Models of these processes are needed to accurately predict the future area of marsh habitat 

under differing scenarios of sea-level rise. Effective management of these natural resources depends 

in part on the capabilities of these models to describe the resource extent and expected change over 

time.

Most work studying the response of coastal marshes to SLR has been conducted in saltwater marshes 

where marsh species are additionally affected by the salinity stress gradient and show distinct 

zonation (Bertness and Ellison, 1987). While saltwater systems dominant many coastal environments 

(e.g., Gulf of Mexico), this work was conducted along the Potomac River estuary where extensive 

freshwater marshes border National Park Service lands, exhibit extensive biological resources, and 

receive visitors from around the world. For example, Dyke Marsh Wildlife Preserve, part of the 

George Washington Memorial parkway, was designated by Congress as a “unique and precious 

ecosystem” (House Resolution 701), and funding has been provided for restoration and conservation 

efforts. Freshwater marshes occur in most estuarine systems, where tidal action extends far upstream 

within large river systems. These systems are important ecosystem components, determining overall 

rates of nutrient retention and providing important ecosystem services including flood control and 

habitat for many rare, threatened, and endangered species.

In freshwater tidal marshes, individual species are loosely zoned by the gradient of inundation time 

and resultant anoxic stress, though a great deal of overlap between species is observed (Odum, 1988).

The relative elevations of marsh surfaces are affected by both sea-level change and surface-elevation 

change. Sea level change can include both global eustatic components and regional gravitational or 

circulation effects (Sallenger et al., 2012). 

Surface-elevation change can include contributions from sedimentation, erosion, accretion of 

biomass at the surface or in the root zone, decomposition of biomass, compaction of sediment, 

regional subsidence (e.g., due to groundwater or hydrocarbon extraction), and isotactic adjustment 

(Pasternack and Brush, 1998, Zedler et al., 1999, Redfield, 1972, Myrick and Leopold, 1963, Nyman 

et al., 2006, Neubauer, 2008). Efforts to model changes in marsh habitat distributions based on 

relative elevation need to incorporate surface-elevation change, sea-level change, and interactions 

between the two (Cadol et al., 2014). 

Marsh distribution models that are controlled by relative elevation (e.g. the Sea Level Affecting 

Marshes Model (SLAMM)) have been broadly employed to assist land managers in planning for 

SLR (e.g., Craft et al., 2009). To a large extent, these models are optimized for saltwater systems and 

for modeling at spatial scales (generally at 30-m resolution and extents of large estuaries) that make 
2

it reasonable to ignore fine-grain variation in topography and land-cover. They have also typically 

been designed to use as input the most widely available sources for digital elevation models (DEMs) 

and land-cover maps, which are typically the National Elevation Data (NED) and National Land 

Cover Data (NLCD), respectively. This report presents the development and testing of the Marsh 

Accretion and Inundation Model (MAIM), a spatially explicit elevation-tracking and vegetation 

class-switching model that is optimized to run at a spatial grain of 1m/pixel. The control parameters 

were calibrated for the freshwater tidal reach of the Potomac estuary and the model was run using 

topographic data derived from Light Detection and Ranging (LiDAR) surveys to assist with resource 

planning in the National Capital Region of the National Park Service. The purpose of this Natural 

Resource Data Series report is to describe the model mechanics and the model results for three SLR 

scenarios for which MAIM was initially run. We also describe the result of these scenarios for two 

cases, one in which developed land was protected and a second in which developed land was allowed 

to inundate and switch to the most elevation-appropriate vegetation class. We provide sufficient 

information and description of the results so that the resulting maps of vegetation change can be used 

as a stand-alone data series describing the potential impact of future SLR. Subsequent publications 

will include more detailed description and analysis of the modeling outputs.
3

Methods

Study area

The National Capital Region 

(NCR) of the U.S. National 

Park Service, centered on the 

District of Columbia, includes 

parts of three U.S. states 

(Maryland, Virginia, and West 

Virginia). The region is a 

highly fragmented patchwork 

of forest, agriculture, and 

developed land. The parks 

were designed to protect both 

locations of historic and 

cultural interest as well as 

regionally and nationally 

significant aspects of natural 

resources, including wetland 

and forest resources. This array 

of managed parkland situated 

among urban and agricultural 

land-use poses unique 

challenges to the management 

of natural resources. Of 

specific interest to this project 

are lands that lie adjacent to 

both the tidal Potomac and 

Anacostia Rivers at less than 

five meters of elevation. The 

tidal Potomac River extends 

from the area around Chain 

Bridge, just up river from Washington, D.C., down to the confluence with the Chesapeake Bay (ca. 

115 miles); the tidal Anacostia River extends from Bladensburg, MD down to the confluence with 

the Potomac River (ca. 8.5 miles) (Figure 1). The primary park units involved in the study are the 

George Washington Memorial Parkway, the National Mall and Memorial Parks and National Capital 

Parks – East including Piscataway Park, Fort Washington, Harmony Hall, Fort Foote, Oxon Cove 

Park, Anacostia Park and Kenilworth Park and Aquatic Gardens.

These lands contain a diversity of resources ranging from historical and cultural sites (e.g. the 

National Mall) to important natural resources and wildlife habitats including the northern most tidal 

marshes along the Potomac River. Rising sea-levels could impact these resources in many ways 

including the inundation and displacement of resources in low-lying areas and the increased impact 

Figure 1: Eleven modeling regions were defined to 

cover all NPS lands along the Potomac Estuary within 

5m of 2008 sea level.
4

from storms. The upper reaches of the tidal Potomac River also extend into the Potomac Gorge, one 

of the most biologically diverse areas in the region. The gorge supports over thirty distinct vegetation 

communities many of which are globally rare. The Potomac Gorge is also home to a diversity of 

wildlife including bald eagles and rare invertebrates. 

Modeling overview

MAIM requires six inputs: (1) a high-resolution digital elevation model (DEM), (2) an initial land 

classification raster, (3) a SLR scenario, (4) surface elevation change functions for each land class, 

(5) elevation range limits for each land class described as probability density functions, and (6) land 

class switching rules. At each time step, MAIM alters the surface elevation of each cell according to 

the surface-elevation-change function, changes sea level according to the SLR scenario provided, 

determines if the relative elevation of any cells have left the range assigned to the land class of that 

cell, and if so, switches the land class of that cell according to the user defined rules. For each model 

run, elevation ranges for each land class are picked stochastically from pre-determined probability 

density functions. Therefore, the model can be run repeatedly in a Monte Carlo experiment, and the 

probability of obtaining the most frequent class at each cell and time step can be output by the model. 

Outputs at each time step are: (1) a DEM, (2) a land-classification raster, and (3) a majority classprobability raster. A single input parameter file provides model control, including all vegetation class 

definitions and associated elevation ranges.

Elevation: measurement, validation, and change modeling

Information on elevation for this project was provided by a digital elevation model (DEM) generated 

from Light Detection and Ranging (LiDAR). We directly collected (via Real-time Kinematic Global 

Positioning System (RTK-GPS)), or acquired from the literature, additional geo-locations used to 

validate the DEM, build vegetation-elevation relationships, and build an elevation-change model (to 

account for sediment accretion and compaction). Details of these procedures are provided below.

LiDAR derived DEM

Light Detection and Ranging remote sensing platforms are ideally suited for spatially continuous 

topography mapping (Lefsky et al., 2002). LiDAR typically operates from an aircraft, sending pulses 

of light to the surface at regular intervals. The time delay between the transmitted and return pulse is 

used to calculate the elevation of the ground surface. Light reflection from vegetation canopies is 

seen in the data as a separate return pulse that must be filtered out of the data to reveal bare-earth 

returns for use in constructing a DEM of the earth surface (Blair et al., 1999). Marsh elevation 

accuracies as high as ±4cm have been reported from LiDAR (e.g., Montane and Torres, 2006). 

LiDAR digital elevation models are therefore far superior to National Elevation Data (NED; (Li 

and Wong, 2010)), and suitable to model sea-level rise at the time-scale decades and to use as a 

base map for plant community modeling (Morris et al., 2005, Mason et al., 2006). However, a 

major challenge of using LiDAR surveys for coastal mapping is that the light from near-infrared 

(NIR) lasers is complete absorbed by water. Further, remaining uncertainty in elevation must be 

considered because a difference of ±4cm elevation can still be considered significant for marsh 

plant communities.
5

For this project, we leveraged an existing LiDAR survey provided by the National Park Service, 

National Capital Region GIS Office. The data encompasses a large area around Washington, DC 

including all park lands along the tidal Potomac and Anacostia Rivers, extending upriver to areas that 

are currently non-tidal. The LiDAR survey was acquired between 10/31/2008 and 12/23/2008, and 

therefore a range of tidal stages and sea levels are represented. The raw data are one meter posting,

first and last return, and were used prior to this project by technicians at Science Applications 

International Corporation (SAIC) to produce a 1-meter bare earth DEM. SAIC performed a limited 

validation of the resulting DEM based on a total of 45 control points. The mean vertical error (offset) 

between the DEM and the control points was measured to be 0.004m with a root mean square error 

(RMSE) of 0.107m. Horizontal accuracy was determined by SAIC to be 0.5 meters. 

Many important details regarding the DEM generation and validation were not provided to the 

project. For example, we were not provided the locations of the ground control points used in 

validation, though we suspect most were on higher, hard ground devoid of vegetative cover. We were 

also not provided with exact time of day of LiDAR acquisition, thus making tidal stage difficult to 

infer. In some areas, tidal flats were exposed and produced LiDAR returns of sufficient density to 

constrain the DEM. In other areas, most of the low-marsh plant community (typically inundated for 

more than 12 hours of the day) was obscured by 

water. These characteristics motivated further work 

(as described below) to generate additional spatial 

data layers and to validate the DEM across a range 

of marsh vegetation types.

Relative elevation calculation and DEM 

validation

Elevations in MAIM are either entered in half tide 

units (HTUs) or converted to them. HTUs are a 

unit of elevation relative to mean tide level (MTL). 

Zero on the HTU scale is set to MTL, and 

distances are normalized using half of the great 

diurnal tide range (GT), which is defined as the 

elevation difference between mean higher high 

water (MHHW) and mean lower low water

(MLLW). MHHW and MLLW are tidal datums

calculated by averaging the higher high water 

events and lower low water events of each tidal 

day (NOAA 2013). When sea-level rises, MTL 

rises and the whole scale shifts with it. Elevations 

are converted from meters relative to any standard 

datum to HTUs using the equation

Figure 2: RTK-GPS was used to 

validate the DEM used in MAIM.
6

(1)

where EHTU is the elevation in HTUs, Em is the elevation in meters relative to the datum being used 

(e.g., NAVD88), MTL is the mean tide level relative to the datum, and GT is as defined above. The 

reverse operation is used by MAIM to convert elevations from HTUs to meters for output. HTUs 

enable comparison of marsh behavior across tide ranges. The conversion requires knowledge of the 

MTL relative to the datum across the entire study area. The National Oceanic Atmospheric 

Administration (NOAA) Center for Operational Oceanographic Products and Services provides this 

information and tools for converting any elevation measured in a vertical datum to an equivalent tidal 

datum (NOAA 2013).

Between February and July 2012, we collected ~2200 RTK-GPS locations across a large portion of 

the marsh habitat of the study region (Figures 2 & 3). Each location was positioned at the mineral 

soil – atmosphere boundary, and therefore was below any plant litter present at the site. The 

elevations in the validation dataset ranged from -0.4 to 

0.8m referenced to the NAVD88 datum. We performed a 

relatively simple statistical analysis by regressing the 

vertical position of the RTK-GPS position against the 

vertical position extracted from the LiDAR DEM for the 

same location. We did not validate the DEM after 

conversion to HTU, further information on the accuracy 

of the NOAA CO-OPS conversion is provided elsewhere

(National Oceanographic and Atmospheric 

Administration 2015).

Elevation-change model

Tidal marshes in equilibrium with rising sea level 

accumulate material (sediment and biomass) at a rate that 

enables the marsh surface to maintain elevation relative 

to sea level. Inundation, erosion, and marsh area loss 

occur where alterations to ecogeomorphic feedbacks 

cause surface elevation change to be less than sea-level 

rise (SLR). Relationships between marsh-platform 

elevation relative to mean tide level and surface-elevation 

change have been observed in tidal salt marshes, where 

lower areas of the marsh platform have greater rates of 

elevation change (Morris et al., 2002). However, 

understanding elevation change in the freshwater marshes 

studied here required new relationships, which were 

developed by measuring elevation change over time 

through repeat RTK-GPS surveys. We compared each 

survey with the 2008 DEM to measure change in surface 

EHTU=

(Em - MTL)

(GT / 2)

Figure 3. RTK GPS survey points 

used to generate the MAIM 

elevation change sub-model. 

Details are discussed in Cadol et 

al. 2014. 
7

elevation between the date of the survey and 2008. Elevation change measured in each survey was 

then compared against elevation in 2008 to reveal elevation-dependent rates of elevation change in 

our study site. In general, increased elevation is associated with decreased elevation change. This 

relationship, however, is not monotonic but includes a local reversal: the rate of elevation gain 

increased between 0.2 and 0.3 m elevation and decreased from 0.3m to the highest marsh elevations 

(~1m). Therefore, we observed a local maximum elevation gain rate at ~0.3 m elevation. The 

localized accretion maximum could be caused by the elevation-controlled distribution of marsh plant 

species, among other possibilities. The results of this exercise were described in detail by Cadol et al. 

(2014).
8

Figure 4. The MAIM tide stick for the mean Monte Carlo permutation showing 

relative positions of each vegetation class and absolute positions in half tide 

units.
9

Figure 5. Decision tree used to generate the initial vegetation classification.
10

MAIM applies the surface elevation change function at each time step, thus modeling the combined 

effects of sedimentation, biomass accumulation, and compaction. The elevation change function is 

static through the modeling period, but as elevation changes due to SLR, the surface elevation change 

due to sedimentation and biomass deposition varies. Surface elevation change rates for swamps and 

tidal flats were given fixed accretion values due to the paucity of data relating elevation change to 

elevation in these habitat types. We elected to use elevation gain rates of 0.003 m/yr for swamp 

forests, 0.0011 m/yr for tidal swamps, and 0.001 m/yr for tidal flats. These values matched those 

used in SLAMM (Craft et al., 2009 and references therein).

Land classification input layers

MAIM requires user-defined land classes and an initial land-classification raster to begin the habitatswitching routine. For our study areas on the Potomac estuary, land was classified using a decision 

tree (Figure 5) based on 5 input data sets: (1) LiDAR derived DEM, (2) an NPS commissioned 

vegetation classification polygon layer, (3) a Heads Up Digitized (HUD) forest cover mask, (4) a 

HUD open water mask (representing the low tide coastline), (5) a weighted distance from water map, 

and (6) a HUD developed land mask.

NPS commissioned vegetation layer

The NPS provided a vector file consisting of non-overlapping polygons classified by vegetation 

community. The NPS maps were based on digitalization of aerial photography flown in April of 

2004 followed by vegetation community inventories at 425 plots and validation at 1895 rapid 

assessment locations (Hazler et al., 2012). Forest structure was finely classified, while marsh 

structure tended to be more coarsely delineated; vegetation classes were merged into the following 

broad categories: (1) Developed, including buildings, paved surfaces, and landscaped park units; (2) 

Transitional, including vinelands and transitional shrub-scrub areas; (3) Marsh, including all marsh 

communities; (4) Tidal Swamp, including Ash-Blackgum and Sweetgum-Tuliptree communities; (5) 

Forest Swamp, including forests with Maple-Ash swamp communities and some Oak floodplain 

swamps; (6) Floodplain Forest, forests dominated by upland trees that are early successional species 

not tolerant of frequently saturated soils; and (7) Successional Forest, later stage successional forests.

Due to the coarse representation of marshes, we found shorelines to be only crudely represented and 

inconsistent in their representation of tidal stage. Because the maps were generated for NPS lands, 

lands within 5 m of the MTL but outside of the NPS park land boundaries were not represented in the 

NPS maps. Therefore, we conducted a series of digitization exercises to remedy these weaknesses 

and define additional land classes (detailed in the following sections).

Forests

Forest areas were manually digitized from 2009 Near Infra-Red (NIR) NAIP aerial photographic 

imagery at 1 m resolution. This exercise included all modeled lands and therefore filled gaps in the 

NPS vegetation layer occurring in areas outside NPS lands. All NAIP imagery was acquired during 

the summer growing season (leaf-on) and under clear-sky conditions. We compared our digitized 

forest maps with the NPS maps and where the newly digitized maps exhibited greater detail and 

positional accuracy along forest boundaries we used the newly created forest layer in place of the 

NPS vegetation maps. In cases where only the boundaries differed, we joined NPS map attributes to 

our forest layer so that these attributes could be used in the initial classification tree.
11

Tidal flat

Tidal Flat is submerged habitat that may be vegetated with submerged aquatic vegetation (SAV) or 

may be barren mud flat. These surfaces were not sufficiently represented in the NPS vegetation map 

provided to the project, likely due to the fact that they exist within an elevation range that is 

inundated nearly 100% of the time. In a few areas, the LiDAR DEM was created at sufficiently low 

tide to expose Tidal Flat, particularly where off-shore winds helped to expose these surfaces to the 

air. However, for all other shorelines, surface elevation in the area of Tidal Flats was not 

characterized by the LiDAR survey. Therefore, we were presented with two problems: (1) our input 

datasets did not adequately depict Tidal Flat, and (2) we could not use an elevation range to represent 

Tidal Flat because we lacked elevation data in the areas where Tidal Flats occur. Tidal Flat

sometimes extended considerable distance from its boundary with Regularly Flooded Marsh and only 

good bathymetric data would be capable of mapping their occurrence accurately. To solve these 

problems, we used NAIP imagery to digitize a line representing the boundary between Tidal Flat and 

open water along all shorelines in the study region. This line identified the boundary between 

shallow, lighter colored water and deeper darker estuarine open water. We assigned this outer 

boundary of tidal flat an elevation of -1.3HTU based on the lowest elevation of tidal flat we could 

measure anywhere in the LiDAR survey (i.e., the lower limit of Tidal Flat); we then used linear 

interpolation to calculate an elevation for every cell between this outer boundary and the closest 

shoreline grid cell with a LiDAR elevation. While this is certainly an approximation, it achieved the 

desired result of providing a continuous elevation gradient through the range expected for Tidal Flat.

Water mask

The NPS vegetation maps did not consistently represent the boundary between any of the vegetation 

classes and open water. Therefore, we used the boundary between tidal flat and open water 

(described above) to define the extent of the Estuarine Open Water class. The Estuarine Open Water 

class defines an area that is not modeled by the MAIM, and therefore elevation change is zero in all 

time steps for this area. We explored an automated shoreline delineation routine based on spectral 

differences between land and water and found it to be insufficient in areas where trees shadowed 

water, obscuring shoreline details.

Weighted distance to water

A weighted distance-to-water measure was calculated to differentiate between Irregularly Flooded 

Forest, where there is a strong tidal influence, and Wetland Forest, which are potentially as wet as 

Irregularly Flooded Forest, but where there is minimal tidal influence. The two forest types might 

contain the same forest species, but different hydrologic processes influence the habitat. To represent 

this difference we generated a cost-distance to water, which was meant to reflect the potential for 

tidal water to reach these forests. The cost-distance was calculated for each grid cell as the distance 

to the nearest shoreline weighted by the elevation squared, and is therefore a unit-less number. We 

then studied the NPS vegetation maps and determined an average threshold cost distance of 150 best 

represented the boundary between Wetland Forest and Irregularly Flooded Forest (Table 1). Forests 

closer to tidal water than this threshold distance were mapped as Irregularly Flooded Forest and 

forests more distant from water were mapped as Wetland Forest.
12

Developed land

The NPS vegetation maps provided a representation for developed dry land, however there were 

areas of the modeling region outside of the extent of this map product for which we required a 

representation of developed land. For these areas we produced a HUD developed land layer that was 

based on 2009 NAIP imagery. Rather than merging our HUD developed layer with the NPS 

developed layer, we kept these classes separate, yet MAIM applies the same class switching rules to 

each.

Initial classification map

From the described inputs we defined and mapped 14 classes: Developed Dry Land, Undeveloped 

Dry Land, Dry Land Swamp Species, NPS Developed Dry Land, Managed Open Space, Agriculture, 

Wetland Forest, Ephemerally Flooded Marsh, Transitional Scrub, Regularly Flooded Marsh, Tidal 

Flat, Irregularly Flooded Forest, Irregularly Flooded Marsh, and Estuarine Open Water. This initial 

classification was completed one time, and represents land cover temporally consistent with our input 

data, 2008-2009. Land was classified according to the flowchart in Figure 5, as explained below.

 Developed Dry Land included both hard (pavement, buildings) and soft (pasture, open space, 

landscaping, golf courses) development, and was delineated from the NPS vegetation maps 

and the 2009 NAIP imagery. The NPS Developed Dry Land, Managed Open Space, and 

Agriculture classes were derived directly from the NPS vegetation maps. These classes were 

all subcategories of developed land in the NPS vegetation maps.

 Undeveloped Dry Land included floodplain forest, successional deciduous forest, and mesic 

mixed hardwood dry land elevations were above 2.1 HTU (roughly 1 m elevation), i.e., twice 

the elevation of the mean high high water with no record of tidal flooding in last 8 years. 

Undeveloped Dry Land that, based on the NPS vegetation maps, contained tree species 

characteristic of swamp forests were reclassified into the Dry Land Swamp Species class.

The forest species considered in this class were Sweetgum (Liquidambar styraciflua), Red 

Maple (Acer rubrum), Pumkin Ash (Fraxinus profunda), Green Ash (Fraxinus 

pennsylvanica), and Blackgum species (Nyssa biflora and sylvatica).

 Wetland forests, called swamp forests in the NPS vegetation map, included maple-ash swamp 

and some oak floodplain swamp. Wetland forest elevations were mostly above 1.3 half tide 

units, i.e., above typical spring high high water. Distance to water thresholds (Table 1) were 

established from training data obtained from the NPS vegetation map and applied to all 

forests (in manually-digitized forest mask) to delineate wetland forests (farther from the 

shore) from irregularly flooded forests (closer to open water).

 Irregularly Flooded Forests, also called tidal swamps, included swamp blackgum-ash and 

sweetgum-tulip tree temporarily flooded forest in the NPS vegetation maps. Elevations were 

0.7-1.2 HTU, i.e., locations inundated between 0-6 hours most days. 

 Transitional Shrub areas included the NPS vegetation classes named disturbed woody 

wetland, successional herbaceous wetland, vineland, shrubland, and all marsh lands higher 

than 2.1 HTUs. Elevations for Transitional Shrub were quite variable, but were typically 

between 1.9-2.1 half tide units, i.e., inundated only during unusual high tide events. 
13

 Ephemerally Flooded Marsh included the highest marsh elevations. The predominant 

vegetation was Typha sp., though the class included some dogwood stands within marshes. 

Ephemerally flooded marsh elevations were between 1.7-1.9 half tide units. Some hydrologic 

influence was expected from the tides, but the surface was not inundated most days. 

 Irregularly Flooded Marsh were areas dominated by Typha sp., Schoenoplectus sp., and 

Impatiens sp., with significant populations of Acorus sp. and Peltandra sp. in places. This is 

the most biologically diverse vegetation class. Elevations were between 0.5-1.7 half tide 

units, i.e., locations inundated 0-6 hours most days. 

 Regularly Flooded Marsh areas were dominated by Nuphar sp. and Peltandra sp. Elevations 

were between -0.5 and 0.5 half tide units, i.e., location inundated 6-18 hours most days. 

 Tidal Flat areas were manually digitized as described above.

 Estuarine Open Water was manually digitized as described above.

Table 1. Weighted distance from water (dw, unitless) thresholds for swamp forest vs. 

tidal swamp differentiation.

Area Best dw

threshold

% error for 

each class

Threshold dw

used

% of NPS EFF

switched to WF

% of NPS WF 

switched to EFF

GWMP a 1100 0 150 13 0

GWMP b 108 6 150 1 12

GWMP c 108 23 150 16 26

NACE c 180 0 150 1 0

NACE d 500 1 400 2 0

Anacostia

(NACE a & b)

156 10 150 10 9

Little Falls no swamp identified in NPS data set

Piscataway 130 21 150 17 24

EFF- ephemerally flooded forest

WF- Wetland forest

Initial vegetation classification validation

For verification, the classification maps were compared to point habitat classifications collected in 

the field. Transects were walked through areas with variable habitat, and classification points were 

collected both within large habitat patches as well as at boundaries between patches, creating a 

rigorous verification data set. We also explored the application of generalized dissimilarity modeling 

(GDM) to classify communities, using the Normalized Difference Vegetation Index (NDVI), slope, 

and elevation as input parameters. In some sites this method was effective in separating high- and 

low- marsh, but in most sites the GDM classifications were deemed unsatisfactory and are not 

reported here.

Sea-level rise Scenarios

Three SLR scenarios were adopted from work by the Scientific and Technical Working Group of the 

Maryland Climate Change Commission (MCCC; (Boesch et al., 2013)) and used in MAIM to drive 

changes in vegetation. The MCCC reported that projections provided by the Intergovernmental Panel 

on Climate Change 4th Assessment Report (2007) were too conservative in that they did not include 
14

flows from the polar ice sheets, which is consistent with other recent literature (Rahmstorf et al., 

2012). Further, the MCCC report accounted for local variation in SLR due to regional ocean 

dynamics and vertical land movement. However, the MCCC did not account for the potential for a 

weakening of the Gulf Stream, which has the potential to raise sea levels significantly (e.g., > 0.20m) 

in the next decade (Ezer et al., 2013). Understandably, the MCCC did not provide annual estimates 

of SLR, suitable for direct input to MAIM, and instead offered projections of low, medium, and high 

SLR to 2050 and 2100 (Table 2). We therefore adopted these values for 2100 and scaled the IPCC 

4th Assessment Report A1B scenario to these values in 2100 to arrive at annual low, best, and high 

projections. The difference between the scaled A1B scenario and the projections provided by the 

MCCC for 2050 was 5, 1, and 0cm for the low, medium, and high scenarios, respectively.

Table 2. Projections for Maryland relative sea-level rise.

Projections Thermal 

(m)

Glaciers 

(m)

Greenland 

(m)

Antarctica 

(m)

Dynamic 

(m)

VLM 

(m)

Relative SLR

(m) (ft)

2050 medium 0.10 0.05 0.03 0.09 0.09 0.075 0.4 1.4

2050 low 0.04 0.05 0.02 0.04 0.07 0.065 0.3 0.9

2050 high 0.19 0.06 0.05 0.16 0.10 0.085 0.7 2.1

2100 medium 0.24 0.13 0.10 0.30 0.17 0.15 1.1 3.7

2100 low 0.10 0.12 0.08 0.10 0.13 0.13 0.7 2.1

2100 high 0.46 0.17 0.17 0.58 0.19 0.17 1.7 5.7

Monte Carlo uncertainty analysis

Of the initial MAIM inputs (a DEM, an initial vegetation classification, SLR scenarios, surface 

elevation change functions, elevation range limits for each vegetation class, and vegetation class 

switching rules), only the SLR scenario and the elevation range limits for each vegetation class 

change between model runs. The SLR scenario changes depending on the type of scenario the user 

decides to model. As described above, we considered three SLR scenarios, the output of which can 

be used to explore the sensitivity of the model results to a large range in possible sea levels by the 

year 2100. While this does provide some insight into the uncertainty inherent to the MAIM modeling 

results, the difference between the three scenario results is primarily due to the interactions between 

the elevation change function and SLR. This interaction does not include uncertainty in our 

understanding of how vegetation community composition changes with elevation, nor does it address 

uncertainty in the DEM used to initiate modeling.

Therefore, we sought a method for modeling uncertainty in the maps produced by MAIM for any one 

SLR scenario. The main sources of uncertainty in vegetation maps of this type are the DEM itself 

and the rules used to constrain vegetation classes to a particular elevation range. Our validation of the 

DEM revealed that the presence of high marsh vegetation caused anomalously high LiDAR returns 

in some locations. These caused a bias towards over predicting the surface elevation, and increased 

variance (spatially) due to variation in vegetation density. When comparing the initial vegetation 

class to independently collected field observations (see initial vegetation classification validation 

section above) we estimated uncertainty in the elevation (and therefore horizontal position) of the 
15

vegetation class boundaries. Therefore, we decided 

that vegetation boundaries could not be described 

as a single, static elevation (referenced to the 

DEM), and should instead be considered 

probability distributions (Figure 6). The mean 

value of each probability distribution function was 

defined as the elevation used to form the initial 

vegetation classification. The width (standard 

deviation) of each distribution was set to best 

capture the range in variability we observed in the 

field validation exercise. 

For each model run, elevation ranges for each land 

class were picked stochastically from the predetermined Gaussian probability density functions. 

For vegetation classes with shared boundaries, the 

upper boundary of the lower elevation class was 

always chosen to be the same elevation as the 

lower boundary of the higher elevation class. Class 

boundaries were repeatedly selected (100 times for 

each SLR scenario) and used as input to MAIM in 

a Monte Carlo experiment. After all 100 Monte 

Carlo permutations were completed, we calculated 

the majority class for each grid cell and assembled 

a map of these “most probable” results. In addition 

to the majority class map, we produced a 

companion map showing the number of Monte 

Carlo iterations that resulted in the majority class. 

For time series analysis, we calculated the first, second, and third quartile class areas across all 100 

permutations.

Implementation of land class switching rules

The Monte Carlo implementation leads to the generation of elevation boundaries for each pair of 

classes, but land class switching rules determine which classes can change. This is described 

pictorially in the tide stick (Figure 4), but details are provided here for classes that have unique 

switching rules and for the initial modeled time step that differs from all later time steps. 

In MAIM, some classes are mapped only once (i.e., in the initial classification) and cannot be created

during the modeling period, but all others are created as the class above them becomes too low in 

elevation to support that type of vegetation. Initial classes that cannot be created, include all forms of 

development, Dry Land Swamp Species, and Transitional Shrub. These classes, which are some of

the higher elevation classes modeled, will switch to a marsh category if their elevation becomes 

sufficiently low. For all other classes, the boundaries are described by an elevation that is both the 

lowest elevation of one class and the upper elevation of another class (described in the “tide stick” 

Figure 6. Graphical depiction of the 

probability distributions (first and third 

quartiles) given for the boundaries

between vegetation classes.
16

Figure 4). Thus the boundary between these vegetation classes must follow the elevation contour as 

parameterized at model initiation, creating the lower elevation class at the expense of the higher 

elevation class as relative elevation decreases with SLR.

After MAIM modeling begins, the initial classification is modified to adhere to the tide stick. The 

first modeling interval is called the “T0 correction,” which in our implementation corresponds with 

2010. The T0 correction takes as input the initial classification (representing conditions in 2008), 

which is static for all modeling runs, the DEM, and the most recent Monte Carlo permutation of the 

tide stick. MAIM then adjusts the surface elevation of each cell according to the accretion rules and 

SLR scenario provided. MAIM then determines if the relative elevation of any cells are outside the 

range (either higher or lower) assigned to the land class of that cell. This T0 correction is the only 

point in MAIM modeling where a class can be switched to a higher-elevation class, because after the 

T0 correction, the elevation of grid cell must be within the elevation range for its corresponding 

vegetation class. For example, each of the elevation thresholds shown in grey in Figure 5 will have a 

slightly different value depending on the current Monte Carlo permutation. Therefore, the relative 

areas of each of these classes will be adjusted between 2008 and 2010 to account for the difference 

between the values in Figure 5 and the actual tide stick being used. Additionally, in the initial 

classification, separation of forest into the various forest classes (e.g., Undeveloped Dry Land, 

Wetland Forest, Irregularly Flooded Forest) was accomplished using a combination of data layers 

(e.g., NPS vegetation maps, digitized aerial photography, weighted distance to water, etc.), but not 

elevation. If the Monte Carlo permutation of the tide stick designates that an area mapped as 

Irregularly Flooded Forest is above the maximum elevation for this class, this area will be converted 

to Wetland Forest in the T0 correction. In short, even though we took great pains to map these forest 

types using a wide variety of map data sources, MAIM requires that vegetation classes adhere to the 

tide stick and therefore each vegetation class must conform to the predetermined minimum and 

maximum elevation. However, complexity is still maintained in areas where vegetation class 

elevation ranges overlap; it is in these areas that the initial vegetation classification differentiates 

between two classes despite having the same elevation. For Wetland Forest and Irregularly Flooded 

Forest (the situation just described), this would occur between approximately 1.2 and 1.7 HTU, with 

the exact values of the bounds of this range depending on the Monte Carlo permutation.

For all further time steps sea level is assumed only to rise and not fall, and to be greater than or equal 

to net accretion, therefore at each time step vegetation classes can only (1) remain the same, or (2) 

switch to classes positioned lower on the tide stick. The rate at which classes switch is determined by 

the SLR scenario and the rate of elevation change modeled by accretion net of compaction (see 

Cadol et al (2014) and above section titled Relative Elevation and Elevation Change Calculation). In 

theory, sedimentation could exceed SLR to result in a net increase in elevation, and this could be 

modeled by MAIM. In the current implementation, accretion is only higher than SLR at specific 

elevations corresponding with elevations receiving the most sediment and biomass (plant litter) 

deposition, respectively. As SLR accelerates, it eventually exceeds these elevation change rates, 

reducing the chance that MAIM output includes marsh vegetation at an elevation above its maximum 

range as described in the tide stick.
17

Model output description

MAIM produces majority class maps, probability maps, and summary statistics for each modeling 

region. The majority class maps show, for each grid cell, the vegetation class that was produced in 

the majority of Monte Carlo permutations of the MAIM tide stick. Hidden behind this number, is the 

possibility for a variety of vegetation class results due to the different permutations. Therefore, 

probability maps are included in the output, which show the number of permutations (out of 100 

total) in which the majority vegetation class was modeled at that time step. In practice, we found 

utility in visualizing the probability maps as a semi-transparent grey-colored overlay on the majority 

map. Areas that produced a low probability of the majority class are shown in dark grey (i.e., areas 

where Monte Carlo permutations resulted in several different vegetation classes).

Summary statistics take two forms: (1) lists of the 1st, 2nd, and 3rd quartile areas (calculated from the 

Monte Carlo permutations) of each vegetation class at each time step, and (2) tabulated areas of the 

majority class areas. Each of these types of statistics can be plotted against time to examine the 

trajectory of vegetation class areas in response to sea-level rise.
18

Results and Discussion

Digital elevation model validation

The LiDAR derived DEM 

covered the entire study region, 

with a spatial resolution of 1 m 

and a vertical resolution of 0.01 

m. In 2012, we conducted a series

of RTK-GPS surveys of the 

sediment surface at Dyke Marsh 

Preserve (DMP) consisting of 

~2200 ground surface 

observations. Additional surveys 

were conducted at marshes along 

the Anacostia River; however, 

these surveys were not as 

extensive. A comparison of the 

DMP survey with the LiDARderived DEM shows that for most 

of the low marsh and lowerelevation sections of the high 

marsh the DEM accurately represents the mineral soil surface. At higher elevations of the marsh, 

however, dense plant litter intercepts the LiDAR returns resulting in a bias towards higher elevations 

compared with the RTK survey. In particular, there are zones of the marsh with tall (> 2 m) and 

dense Typha stands, and in these areas the DEM reports elevations that are as much as 1 m higher 

than measurements collected using real-time kinematic global positioning system (RTK-GPS) survey 

equipment (Figure 7). Another weakness of the DEM is that water level was at ~ 0 m elevation 

(NAVD88 datum) at the time of the LiDAR survey, thus any surfaces below this elevation were 

measured as returns from the water not bare earth. It is important to note, however, that elevation 

thresholds used in classification decisions applied to this DEM were optimized for this DEM using 

field observations of vegetation type and pre-existing NPS vegetation classification maps as training 

data. Thus the resultant maps are reliable, while the threshold values themselves may lack relevance 

in some areas due to the effects described above. Uncertainty derived from the variance in Figure 7 is 

also accounted for by the Monte Carlo implementation, as described in the methods.

Initial classification results

It was found that elevation thresholds adequately delineated the boundaries between several 

important land cover types (Table 3). The elevation 0.5 HTU was highly successful at separating 

Ephemerally Flooded Marsh from Irregularly Flooded Marsh. We used 1.7 HTU as an upper limit to 

Irregularly Flooded Marsh because in areas with accurate elevation data in the DEM this was the 

limit of strong tidal influence. There were some areas within active tidal marshes, however, where 

Figure 7. DEM elevations vs. RTK-GPS survey 

elevations showing bias and systematic error in the 

DEM used in this report (Feb 2012 only; 1341 pts)
19

the DEM bias was quite strong. The standing vegetation reflected the LiDAR laser pulses and caused 

the DEM surface to be much higher than the actual ground surface. In these cases our classification 

scheme assigns the area to the Ephemerally Flooded Marsh or Transitional Shrub categories. This 

can be seen in the middle of Dyke Marsh Preserve in the 2008 map (Figure 8), but these areas are 

quickly converted to Irregularly Flooded Marsh in ensuing years of the model run (Figure 9).

The threshold value of weighted distance from Estuarine Open Water used in classifying 

Ephemerally Flooded Forest and Wetland Forest was found separately for each analysis area by 

inspecting histograms of weight distance values for areas classified as tidal swamp and swamp forest 

in the NPS vegetation maps. Typically there was good clustering and a threshold value was clear. 

The best threshold was considered to be the one for which an equal percentage of misclassification 

occurred for each of the two forest types. A threshold of 150 was acceptable for all but one site, 

where the threshold was set at 400 (Table 1; note that the weighted distance does not have 

meaningful units and should be considered a relative metric). Although we relied on the NPS 

classification polygons to set a threshold, we were not comfortable using the polygon areas directly 

in our classification system due to the coarseness of the mapping. For example, there were often 

small areas within larger upland forest polygons that our system was able to identify as likely 

swamps due to the locally low elevation.

Table 3. Field validation of initial classification map

 All types exact: Number/percent of collected points that were correctly classified in the initial classification map as tidal

flats, regularly flooded marsh, irregularly flooded marsh, ephemerally flooded marsh, transitional marsh, or tidal swamp.

 IM+EM exact: Same as all types exact, except that the irregularly flooded marsh and ephemerally flooded marsh were 

combined into a single category

 All types w/in 1 m: Number/percent of collected points that were within 1 meter of a cell of the same classification in the 

initial classification map,

 IM+EM w/in 1 m: Same as all types w/in 1 m, except that the irregularly flooded marsh and ephemerally flooded marsh 

were combined into a single category.

Little Hunting Creek Piscataway Southern Dyke Marsh Total

n = 158 n = 45 n = 43 n = 246

# correct % correct # correct % correct # correct % correct # correct % correct

All types

exact 101 64% 18 40% 39 91% 158 64%

IM+EM

exact 110 70% 29 64% 39 91% 178 72%

All types 

w/in 1 m 116 73% 20 44% 40 93% 176 72%

IM+EM 

w/in 1 m 122 77% 31 69% 40 93% 193 78%
20

Transitional Shrub areas were 

primarily identified from the NPS 

classification polygons. There was 

little consistency in terms of 

elevation or distance from water for 

the distribution of these areas. 

Rather, land use history seems to be 

the dominant control on the 

establishment of transitional 

vegetation, as we have defined it. At 

issue may be the very concept of a 

transitional zone within a tidal 

freshwater setting. In tidal salt 

marshes, transitional zones form 

where occasional storm inundation 

increases soil salinity to the point 

that preexisting vegetation cannot 

handle the stress and a new, salttolerant suite of shrubs is able to 

colonize. These shrubs can only 

persist if inundation and soils 

saturation periods do not increase 

beyond their threshold for anoxic 

stress toleration. Because our area 

does not have saline water, the salt 

stress that initiates this transition is 

not present.

Effect of T0 correction on the initial vegetation classification

Before simulation modeling begins, the initial vegetation classification must be adjusted for 

differences between the initial classification decision tree and the tide stick chosen for that Monte 

Carlo model run. This includes not only adjustments due to differences between the elevation 

thresholds used in the initial classification and the tide stick, but also adjustments due to places in the 

initial classification that were based on map products unrelated to elevation (see section titled 

Implementation of land class switching rules). The effect of this correction can be anywhere from noeffect at all to fairly large changes in class areas. Previous work using the SLAMM model has shown 

that adjustments during T0 generally indicate the degree of correspondence between the initial 

vegetation classification and the digital elevation model (Craft et al. 2009). A lack of correspondence 

between these two layers could be due to data issues (vegetation mapping errors) and/or spatial 

variation in the effect of elevation on vegetation. While elevation is a strong predictor of vegetation 

in tidal marsh systems, it is only weakly related to forest type, particularly in areas subject 

Figure 8: Dyke Marsh as represented in the initial 

vegetation classification before the T0 correction.
21

Table 4: Vegetation class areas (ha) in the initial vegetation classification and class 

areas (and change) after the T0 correction, which accounts for differences between the 

initial classification and the MAIM tide stick (Figure 4).

Vegetation class 2008 2010

Developed dry land 717 717 (0%)

Undeveloped dry land 556 551 (-1%)

Dry land, swamp species 163 144 (-12%)

NPS developed dry land 176 176 (0%)

Managed open space 328 328 (0%)

Agriculture 93 93 (0%)

Wetland forest 37 69 (84%)

Ephemerally flooded marsh 30 29 (-6%)

Transitional scrub 58 63 (7%)

Regularly flooded marsh 147 132 (-11%)

Tidal flat 197 176 (-11%)

Estuarine open water 4105 4148 (1%)

Irregularly flooded marsh 128 169 (33%)

Irregularly flooded forest 190 131 (-31%)

to a large upstream contributing area that works to maintain saturated soils for much of the year,

despite no measurable tidal influence. In our application, variability in the tide stick introduced by 

the Monte Carlo modeling provides an additional source for change in the T0 correction. Finally, the 

T0 correction includes the calculation of small elevation changes and associated shifts in vegetation 

class boundaries due to accretion processes operating between 2008 and 2010.

The impact of the T0 correction in MAIM can be evaluated by comparing the 2008 vegetation maps 

(aka the initial vegetation map) with the 2010 vegetation maps (Figure 8 and 9), or by examining the 

change in tabulated areas of each class between these two years (Table 4). The 2008 vegetation maps 

are the same for all modeling runs (across all Monte Carlo permutations), so probability is not 

calculated for these maps. The 2010 vegetation maps differ with the 2008 maps in areas constrained 

by elevation, for example, as with all of the marsh vegetation classes. However, developed areas are 

forced to remain constant between in the T0 correction, even in the unprotected development case.

Therefore, all of the developed land classes have equivalent area in 2008 and 2010 (Table 4). The 

largest adjustments made in the T0 correction were to classes at the interface of forest and marsh. 

There are five classes that are involved in this transition: (1) Wetland forest, (2) Irregularly Flooded 

Forest, (3) Ephemerally Flooded Marsh, (4) Irregularly Flooded Marsh, and (5) Dry Land, swamp 

species. In this area we used a variety of non-elevation related mapping approaches to define the area 

of each class. Once MAIM begins, however, the class boundaries must match the pre-determined 

elevation ranges of each class (note that overlapping elevation ranges occur in some places). For 

example, the large change in Wetland Forest area (84%) is due to differences between the mapped 

area and the elevation sensitivity of this class.
22

Projected trends in vegetation class areas

It is useful to examine trends in vegetation class areas over time in tabular (Appendix A), graphical 

(Figure 10), and map views (Figure 9 and Appendix B). In this section we will use these unique 

perspectives to report and discuss changes in vegetation class areas (1) across the three SLR 

scenarios, and (2) between model runs in which developed land was protected or unprotected from 

change. We begin with the results for protected development. As would be expected, the magnitude 

of change increased from the low to high SLR scenario, with the greatest changes occurring in the 

vegetation classes that are inundated at low elevations but are not created at high elevation. This 

includes Tidal Flat at the lowest elevations. Even for the low SLR scenario, Tidal Flat was inundated 

at almost all locations and Regularly Flooded Marsh (just above Tidal Flat in elevation) was capable 

of gaining sufficient elevation (through accretion net of compaction) to avoid conversion to Tidal 

Flat. Therefore, Tidal Flat inundated at low elevations was not replaced. Similarly for Irregularly 

Flooded Forest, lower elevation forests that were converted to Ephemerally or Irregularly Flooded 

Marsh were not replaced at an equal pace by higher-elevation forest flooding. This could be due, for 

example, to the fact that there is relatively small forest area throughout the study region, limiting the 

area that can be converted. The same primary effects were seen in the medium SLR scenario results, 

but the magnitude was greater. Here, Irregularly Flooded Forest decreased by 78% between 2010 and 

2080, yet the model results predict large increases in area for Regularly Flooded Marsh. Tidal Flat 

area decreased less than in the low SLR scenario due to some conversion of Regularly Flooded 

Marsh to Tidal Flat. The results for the high SLR scenario are more complex because Regularly 

Flooded Marsh initially gains area, but is eventually unable to maintain elevation through accretion 

above SLR. Therefore, the modeling results exhibit late-time (2080-2100) decreases in Regularly 

Flooded Marsh and increases in Tidal Flat. The early gains in Regularly Flooded Marsh are at the 

expense of Irregularly Flooded Forest and Wetland Forest. These forest classes are converted to 

Irregularly Flooded Marsh simultaneously with conversion of low lying Irregularly Flooded Marsh to 

Regularly Flooded Marsh. The area of Irregularly Flooded Marsh remains relatively constant in the 

high SLR scenario, indicating a migration inland of this habitat. Undeveloped dry land, which is 

primarily forest, decreased by 20%, 30%, and 43% in the low, medium, and high SLR scenarios 

respectively, reflecting the fact that forests will be converted to wetland under SLR. In summary, the 

overall effect of the different SLR scenarios can be described as a shift in the balance between 

accretion and SLR, with the fate of the lowest marsh vegetation classes (Regularly Flooded Marsh 

and Tidal Flat) being most impacted. In only limited areas is marsh area as a whole seen to increase 

as it moves into dry land. Presumably this is due to a paucity of flat, undeveloped dry land.
23

Figure 9. Dyke Marsh Preserve vegetation classification map in 2010 (upper left), 

2030 (upper right), 2050 (lower left), and 2080 (lower right) under the high SLR 

scenario. Trajectories of vegetation class areas for this modeling region are 

presented in Figure 10.
24

The effect of leaving development unprotected from the effects of SLR was quite large across all 

SLR scenarios, but greatest for high SLR (Figure 11). Logically, the effect was also greatest 

wherever development is highest, particularly at low elevations. This was notably true for 

Agriculture (a developed land class) at Piscataway Park where portions of grazing and hay fields at 

low elevations are predicted to convert to Ephemerally Flooded Marsh and Irregularly Flooded 

Marsh by 2080 (Figure 12). Because the model did not consider the generation of forest in developed 

areas (the accurate prediction of where forests would be planted seemed tenuous at best), none of the 

forest class statistics differed between the protected and unprotected developments scenarios. 

Similarly, development itself exhibited no net change in area in the protected scenarios. In the 

unprotected scenarios, developed dry land decreased by 8%, 11%, and 24% by 2100 for the low, 

medium, and high SLR scenarios. When left unprotected, NPS developed dry land (constrained to 

just NPS parklands depicted in the NPS-provided vegetation maps) decreased by 10%, 20%, 35% 

across the three scenarios, respectively. Similarly, Managed Open Space decreased by 19%, 29%, 

and 44% across the three scenarios, which could be considered a great opportunity for establishing 

wetlands in currently dry land. These decreases in the different classes of developed land led to an 

increase in marsh area in the unprotected development case relative to the protected development 

case. Although this difference was small (tens of hectares) in the case of low SLR, the difference was 

quite large in the other two scenarios with marsh classes like Ephemerally Flooded Marsh increasing 

by over 100% in the unprotected case, but increasing only marginally in the development protected 

case. Clearly there are large trade-offs to consider, but the modeling results show the benefit to marsh 

persistence, if development is allowed to transition to one of the flooded marsh vegetation types.

Appendix B contains map outputs for all study regions illustrating the changes to all habitat areas 

(including developed land classes) under the high sea-level rise scenario between 2010, 2030, 2050 

and 2080. 
25

Figure 10. Change in vegetation class areas over time (solid lines) and the first 

and third quartiles of the class area distributions resulting from the Monte Carlo 

uncertainty modeling for the George Washington Memorial Parkway modeling 

region B. In this example, the high SLR scenario was used and developed land 

was allowed to change class. All other scenario and modeling region outputs are 

included in the data series.
26

Figure 11: Model results presented in Tables A1, A2, and A3 can also be 

presented in graphical form. Here the low (L), medium (M), and high (H) SLR 

scenario results can be compared for both development protected (P) and 

unprotected (UP) cases. Protection of development has a universal effect of 

increasing the impact (decreasing the resulting vegetation class area) of sea-level 

rise.

Irregularly Flooded Marsh

Year

Area (ha)

2010202020302040205020602070208020902100

100

150

200

250

Low SLR Protected

Low SLR Non-Protected

Medium SLR Protected

Medium SLR Non-Protected

High SLR Protected

High SLR Non-Protected
27

Figure 12: Model results for a large portion of Piscataway Park. Modeling begins 

at 2010 (top). Results for 2080 can be compared between the medium (left) and 

high (right) SLR scenarios. Additionally, scenarios with protected (upper) and 

unprotected development (lower) can be compared for each SLR scenario.
28

Sources and analysis of uncertainty in vegetation class areas

There are several known sources of uncertainty in these results. The first comes from the elevation 

change model, which is dependent on elevation but assumes that the relationship between elevation 

and elevation change will remain static over time. Instead, the model is likely to change in the future 

due to changes in environmental drivers, like suspended-sediment concentrations in near-shore water 

(Kirwan et al., 2010), that directly influence marsh-surface elevations. Further, while information on 

vegetation was collected from the entire study region in the course of developing the vegetation 

maps, the elevation change model was developed using data solely from Dyke Marsh Wildlife 

Preserve. We attempted to model uncertainty in future elevation by Monte Carlo modeling of the 

response of vegetation classes to elevation change. This resulted in uncertainty maps, showing where 

the full suite of Monte Carlo in model runs resulted in multiple possible vegetation classes in any 

model year. The areas of greatest uncertainty typically outline the major vegetation classes, with the 

center of each class area having the least uncertainty and the edges having the greatest (Figures 9 & 

10). For some marsh areas, large flat surfaces behaved similarly in that multiple possible vegetation 

classes were modeled, represented by large grey areas in the map. Management decisions based on 

these maps should be restricted where possible to the areas that are not grey as these are the areas 

where the model had the most certain result.

Uncertainty can also be studied using the time series of the first, second, and third quartile estimates 

for vegetation class area over time (e.g., Figure 10). The Monte Carlo experiment included 100 

different representations for how vegetation classes responded to elevation change above sea level. 

For each time step and vegetation class, Figure 10 shows the median vegetation class area as a solid 

line. The first and third quartiles are shown as dotted lines. As uncertainty in vegetation class areas 

increases over time, the spread between the first and third quartile increases. This occurs perhaps less 

often than one would expect, but it is certainly a consistent feature of the various marsh classes. For 

the forest classes, the uncertainty in vegetation class area decreases with time. This is due to a 

reduction in overall forest area and resulting limitation in the land area capable of causing uncertainty

in area. For example, under high SLR nearly the entirety of Irregularly Flooded Forest and more than 

half of all other types of forests are converted to one of the other vegetation classes. This is a 

consistent pattern that shows little variation between the Monte Carlo runs over time.

We also report uncertainty in certain classes that are expected to be most impacted by processes we 

did not model. The most obvious example of this type is Tidal Flat. We digitized Tidal Flat by hand 

from aerial imagery due to the fact that the near-infrared LiDAR signal is completely absorbed by 

water and does not return near shore bathymetry. Further, our mapping ability was limited by the 

availability of imagery and the degree to which water clarity allowed detection of tidal flats. Beyond 

this first mapping of tidal flats, the model included only one pathway for creating tidal flat (through 

inundation and conversion of Regularly Flooded Marsh). However, tidal flats are seen to form in 

many areas of the estuary, presumably due to shifting currents and the resulting delivery and 

deposition of sediments from upland sources. Only a dynamic hydrologic sediment transport model 

would be capable of estimating future locations where this process would occur. Without 
29

representation of sediment dynamics in the estuary, our model results show the removal of tidal flats 

through inundation only, which in some cases might be quite appropriate, but will underestimate tidal 

flat area wherever sediment delivery is high or increases over time.

We did not model shoreline change due to any other process other than SLR. Shoreline process we 

did not model, therefore, include erosion and anthropogenic changes to shorelines such as hardening 

and build-out events. While modeling anthropogenic influences to shorelines were outside the goals 

of this project, early in the project we did intend to model shoreline erosion. We digitized shorelines 

at multiple time periods, but did not find consistent evidence of erosion or shoreline sediment 

deposition that could be parameterized and modeled across the study region. We include these 

shoreline representations in the dataset, but do not use them to model change. This being said, 

shorelines do recede as a function of SLR, even in the absence of erosion. Wherever shorelines were 

inundated above the rate of elevation change predicted our elevation change model, shorelines were 

seen to retreat landward in modeling results.

31

Literature Cited

Bertness, M. D. & Ellison, A. M. (1987) DETERMINANTS OF PATTERN IN A NEW-ENGLAND 

SALT-MARSH PLANT COMMUNITY. Ecological Monographs, 57, 129-147.

Blair, J. B., Rabine, D. L. & Hofton, M. A. (1999) The Laser Vegetation Imaging Sensor: a mediumaltitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. Isprs 

Journal of Photogrammetry and Remote Sensing, 54, 115-122.

Boesch, D. F., Atkinson, L. P., Boicourt, W. C., Boon, J. D., Cahoon, D. R., Dalrymple, R. A., Ezer, T., 

Horton, B. P., Johnson, Z. P., Kopp, R. E., Li, M., Moss, R. H., Parris, A. & Sommerfield, C. K. 

(2013) Updating Maryland's Sea-level Rise Projections. Special report of the scientific and 

technical working group to the Maryland Climate Change Commission. University of Maryland 

Center for Environmental Science, Cambridge, MD.

Cadol, D., Engelhardt, K., Elmore, A. & Sanders, G. (2014) Elevation-dependent surface elevation gain in 

a tidal freshwater marsh and implications for marsh persistence. Limnology and Oceanography,

59, 1065-1080.

Craft, C., Clough, J., Ehman, J., Joye, S., Park, R., Pennings, S., Guo, H. & Machmuller, M. (2009) 

Forecasting the effects of accelerated sea-level rise on tidal marsh ecosystem services. Frontiers 

in Ecology and Environment, 7, 73-78.

Hazler, K. G., Fleming, G., Taverna, K., Teague, J. & Smart, L. (2012) Draft vegetation classification and 

maps for the National Park Service, National Capital Region parks.

Kirwan, M. L., Guntenspergen, G. R., D'Alpaos, A., Morris, J. T., Mudd, S. M. & Temmerman, S. (2010) 

Limits on the adaptability of coastal marshes to rising sea level. Geophysical Research Letters,

37.

Lefsky, M. A., Cohen, W. B., Parker, G. G. & Harding, D. J. (2002) Lidar remote sensing for ecosystem 

studies. Bioscience, 52, 19-30.

Li, J. & Wong, D. W. S. (2010) Effects of DEM sources on hydrologic applications. Computers 

Environment and Urban Systems, 34, 251-261.

Mason, D. C., Scott, T. R. & Wang, H. J. (2006) Extraction of tidal channel networks from airborne 

scanning laser altimetry. Isprs Journal of Photogrammetry and Remote Sensing, 61, 67-83.

Montane, J. M. & Torres, R. (2006) Accuracy assessment of lidar saltmarsh topographic data using RTK 

GPS. Photogrammetric Engineering and Remote Sensing, 72, 961-967.

Morris, J. T., Porter, D., Neet, M., Noble, P. A., Schmidt, L., Lapine, L. A. & Jensen, J. R. (2005) 

Integrating LIDAR elevation data, multi-spectral imagery and neural network modelling for 

marsh characterization. International Journal of Remote Sensing, 26, 5221-5234.

Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerfve, B. & Cahoon, D. R. (2002) Responses of 

coastal wetlands to rising sea level. Ecology, 83, 2869-2877.

Myrick, R. M. & Leopold, L. B. (1963) Hydraulic geometry of a small tidal estuary. U.S. Government 

Print Office, Washington, DC.

Neubauer, S. C. (2008) Contributions of mineral and organic components to tidal freshwater marsh 

accretion. Estuarine Coastal and Shelf Science, 78, 78-88.

Nicholls, K. W. (1997) Predicted reduction in basal melt rates of an Antactic ice shelf in a warmer 

climate. Nature, 388, 460-462.

Nicholls, R. J. & Cazenave, A. (2010) Sea-Level Rise and Its Impact on Coastal Zones. Science, 328,

1517-1520.

Nicholls, R. J., Hoozemans, F. M. J. & Marchand, M. (1999) Increasing flood risk and wetland losses due 

to global sea-level rise: regional and global analyses. Global Environmental Change-Human and 

Policy Dimensions, 9, S69-S87.

National Oceanographic and Atmospheric Administration. 2015. Vertical Datum Transformation: 

Estimation of vertical uncertainties in VDatum. Accessed October 26, 2015. 

http://vdatum.noaa.gov/docs/est_uncertainties.html
32

Nyman, J. A., Walters, R. J., Delaune, R. D. & Patrick, W. H., Jr. (2006) Marsh vertical accretion via 

vegetative growth. Estuarine Coastal and Shelf Science, 69, 370-380.

Odum, W. E. (1988) COMPARATIVE ECOLOGY OF TIDAL FRESH-WATER AND SALT 

MARSHES. Annual Review of Ecology and Systematics, 19, 147-176.

Pasternack, G. B. & Brush, G. S. (1998) Sedimentation cycles in a river-mouth tidal freshwater marsh. 

Estuaries, 21, 407-415.

Rahmstorf, S., Foster, G. & Cazenave, A. (2012) Comparing climate projections to observations up to 

2011. Environmental Research Letters, 7.

Redfield, A. C. (1972) Development of a New England Salt Marsh. Ecological Monographs, 42, 201-237.

Sallenger, A. H., Jr., Doran, K. S. & Howd, P. A. (2012) Hotspot of accelerated sea-level rise on the 

Atlantic coast of North America. Nature Climate Change, 2, 884-888.

Titus, J. G., Hudgens, D. E., Trescott, D. L., Craghan, M., Nuckols, W. H., Hershner, C. H., Kassakian, J. 

M., Linn, C. J., Merrit, P. G., McCue, T. M., O'Connell, J. F., Tanski, J. & Wang, J. (2009) State 

and local governments plan of development of most land vulnerable to rising sea level along the 

US Atlantic coast. Environmental Research Letters, 4, 1-7.

Zedler, J. B., Callaway, J. C., Desmond, J. S., Vivian-Smith, G., Williams, G. D., Sullivan, G., Brewster, 

A. E. & Bradshaw, B. K. (1999) Californian salt-marsh vegetation: An improved model of spatial 

pattern. Ecosystems, 2, 19-35.
33

Appendix A: Summary statistics for SLR scenarios

Summary statistics are included here for each scenario modeled and for both the development 

protected and unprotected cases.

Table A1. Summary of area (ha) and change over time for each vegetation class 

associated with the low SLR scenario and tabulated for the entire study region.

Low SLR, Development Protected

Vegetation class 2010 2020 2030 2050 2080 2100

Developed dry land 717 717 (0%) 717 (0%) 717 (0%) 717 (0%) 717 (0%)

Undeveloped dry land 551 543 (-1%) 534 (-3%) 509 (-8%) 468 (-15%) 442 (-20%)

Dry land, swamp species 144 141 (-2%) 138 (-4%) 130 (-9%) 116 (-19%) 108 (-25%)

NPS developed dry land 176 176 (0%) 176 (0%) 176 (0%) 176 (0%) 176 (0%)

Managed open space 328 328 (0%) 328 (0%) 328 (0%) 328 (0%) 328 (0%)

Agriculture 93 93 (0%) 93 (0%) 93 (0%) 93 (0%) 93 (0%)

Wetland forest 69 71 (3%) 72 (4%) 71 (3%) 55 (-20%) 36 (-47%)

Ephemerally flooded marsh 29 27 (-6%) 26 (-8%) 37 (31%) 33 (16%) 30 (4%)

Transitional scrub 63 70 (11%) 77 (22%) 76 (21%) 71 (13%) 66 (5%)

Regularly flooded marsh 132 165 (25%) 160 (21%) 161 (23%) 259 (97%) 323 (145%)

Tidal flat 176 164 (-7%) 155 (-12%) 116 (-34%) 29 (-83%) 32 (-82%)

Estuarine open water 4148 4160 (0%) 4174 (1%) 4220 (2%) 4319 (4%) 4335 (5%)

Irregularly flooded marsh 169 143 (-15%) 156 (-8%) 205 (21%) 213 (25%) 194 (14%)

Irregularly flooded forest 131 127 (-3%) 120 (-9%) 87 (-34%) 47 (-64%) 46 (-65%)

Low SLR, Development Unprotected

Vegetation class 2010 2020 2030 2050 2080 2100

Developed dry land 717 691 (-4%) 689 (-4%) 684 (-5%) 671 (-6%) 662 (-8%)

Undeveloped dry land 551 543 (-1%) 534 (-3%) 509 (-8%) 468 (-15%) 442 (-20%)

Dry land, swamp species 144 141 (-2%) 138 (-4%) 130 (-9%) 116 (-19%) 108 (-25%)

NPS developed dry land 176 169 (-4%) 169 (-4%) 167 (-5%) 163 (-8%) 158 (-10%)

Managed open space 328 310 (-5%) 308 (-6%) 300 (-9%) 282 (-14%) 267 (-19%)

Agriculture 93 89 (-4%) 89 (-4%) 88 (-5%) 86 (-8%) 84 (-9%)

Wetland forest 69 71 (3%) 72 (4%) 71 (3%) 55 (-20%) 36 (-47%)

Ephemerally flooded marsh 29 27 (-6%) 76 (167%) 48 (68%) 50 (75%) 49 (72%)

Transitional scrub 63 123 (97%) 85 (36%) 88 (41%) 89 (42%) 85 (36%)

Regularly flooded marsh 132 165 (25%) 160 (21%) 182 (39%) 272 (107%) 338 (157%)

Tidal flat 176 164 (-7%) 155 (-12%) 116 (-34%) 31 (-82%) 35 (-80%)

Estuarine open water 4148 4160 (0%) 4174 (1%) 4220 (2%) 4329 (4%) 4345 (5%)

Irregularly flooded marsh 169 143 (-15%) 156 (-8%) 236 (39%) 266 (57%) 271 (60%)

Irregularly flooded forest 131 127 (-3%) 120 (-9%) 87 (-34%) 47 (-64%) 46 (-65%)
34

Table A2. Summary of area (ha) and change over time for each vegetation class 

associated with the medium SLR scenario and tabulated for the entire study region

Medium SLR, Development Protected

Vegetation class 2010 2020 2030 2050 2080 2100

Developed dry land 717 717 (0%) 717 (0%) 717 (0%) 717 (0%) 717 (0%)

Undeveloped dry land 553 541 (-2%) 525 (-5%) 489 (-12%) 427 (-23%) 388 (-30%)

Dry land, swamp species 143 140 (-2%) 135 (-5%) 123 (-14%) 103 (-28%) 90 (-37%)

NPS developed dry land 176 176 (0%) 176 (0%) 176 (0%) 176 (0%) 176 (0%)

Managed open space 328 328 (0%) 328 (0%) 328 (0%) 328 (0%) 328 (0%)

Agriculture 93 93 (0%) 93 (0%) 93 (0%) 93 (0%) 93 (0%)

Wetland forest 70 71 (1%) 70 (-0%) 57 (-18%) 25 (-64%) 24 (-66%)

Ephemerally flooded 

marsh 28 25 (-9%) 30 (7%) 36 (30%) 34 (20%) 31 (9%)

Transitional scrub 61 71 (17%) 78 (28%) 76 (26%) 68 (13%) 59 (-3%)

Regularly flooded marsh 131 166 (27%) 164 (26%) 232 (77%) 301 (130%) 281 (115%)

Tidal flat 176 160 (-9%) 146 (-17%) 81 (-54%) 118 (-33%) 158 (-10%)

Estuarine open water 4150 4168 (0%) 4193 (1%) 4280 (3%) 4357 (5%) 4425 (7%)

Irregularly flooded marsh 169 145 (-15%) 161 (-5%) 173 (2%) 148 (-12%) 141 (-17%)

Irregularly flooded forest 131 125 (-5%) 109 (-17%) 63 (-52%) 29 (-78%) 14 (-89%)

Medium SLR, Development Unprotected

Vegetation class 2010 2020 2030 2050 2080 2100

Developed dry land 717 690 (-4%) 687 (-4%) 677 (-6%) 656 (-9%) 635 (-11%)

Undeveloped dry land 553 541 (-2%) 525 (-5%) 489 (-12%) 427 (-23%) 388 (-30%)

Dry land, swamp species 143 140 (-2%) 135 (-5%) 123 (-14%) 103 (-28%) 90 (-37%)

NPS developed dry land 176 169 (-4%) 168 (-5%) 165 (-6%) 154 (-13%) 140 (-20%)

Managed open space 328 309 (-6%) 305 (-7%) 290 (-12%) 257 (-22%) 234 (-29%)

Agriculture 93 89 (-4%) 89 (-4%) 87 (-6%) 83 (-11%) 78 (-15%)

Wetland forest 70 71 (1%) 70 (-0%) 57 (-18%) 25 (-64%) 24 (-66%)

Ephemerally flooded 

marsh 28 25 (-9%) 83 (199%) 51 (83%) 58 (109%) 60 (115%)

Transitional scrub 61 128 (111%) 90 (48%) 96 (58%) 96 (59%) 96 (59%)

Regularly flooded marsh 131 166 (27%) 164 (26%) 257 (97%) 323 (147%) 317 (142%)

Tidal flat 176 160 (-9%) 146 (-17%) 81 (-54%) 125 (-29%) 166 (-6%)

Estuarine open water 4150 4168 (0%) 4193 (1%) 4280 (3%) 4369 (5%) 4441 (7%)

Irregularly flooded marsh 169 145 (-15%) 161 (-5%) 209 (23%) 220 (30%) 241 (43%)

Irregularly flooded forest 131 125 (-5%) 109 (-17%) 63 (-52%) 29 (-78%) 14 (-89%)
35

Table A3. Summary of area (ha) and change over time for each vegetation class 

associated with the medium SLR scenario and tabulated for the entire study region

High SLR, Development Protected

Vegetation class 2010 2020 2030 2050 2080 2100

Developed dry land 717 717 (0%) 717 (0%) 717 (0%) 717 (0%) 717 (0%)

Undeveloped dry land 551 532 (-3%) 510 (-7%) 455 (-17%) 368 (-33%) 315 (-43%)

Dry land, swamp species 143 137 (-4%) 130 (-9%) 112 (-22%) 82 (-43%) 61 (-57%)

NPS developed dry land 176 176 (0%) 176 (0%) 176 (0%) 176 (0%) 176 (0%)

Managed open space 328 328 (0%) 328 (0%) 328 (0%) 328 (0%) 328 (0%)

Agriculture 93 93 (0%) 93 (0%) 93 (0%) 93 (0%) 93 (0%)

Wetland forest 70 69 (-1%) 63 (-10%) 26 (-63%) 23 (-67%) 23 (-67%)

Ephemerally flooded marsh 29 24 (-17%) 37 (30%) 41 (43%) 41 (44%) 33 (17%)

Transitional scrub 63 79 (26%) 81 (29%) 79 (26%) 64 (1%) 51 (-19%)

Regularly flooded marsh 132 173 (32%) 188 (43%) 264 (100%) 185 (41%) 139 (6%)

Tidal flat 177 154 (-13%) 132 (-26%) 102 (-42%) 205 (16%) 136 (-23%)

Estuarine open water 4148 4181 (1%) 4222 (2%) 4347 (5%) 4533 (9%) 4751 (15%)

Irregularly flooded marsh 168 145 (-14%) 157 (-7%) 137 (-18%) 96 (-43%) 88 (-48%)

Irregularly flooded forest 131 117 (-10%) 90 (-31%) 47 (-64%) 13 (-90%) 13 (-90%)

High SLR, Development Unprotected

Vegetation class 2010 2020 2030 2050 2080 2100

Developed dry land 717 688 (-4%) 684 (-5%) 665 (-7%) 617 (-14%) 546 (-24%)

Undeveloped dry land 551 532 (-3%) 510 (-7%) 455 (-17%) 368 (-33%) 315 (-43%)

Dry land, swamp species 143 137 (-4%) 130 (-9%) 112 (-22%) 82 (-43%) 61 (-57%)

NPS developed dry land 176 168 (-4%) 167 (-5%) 160 (-9%) 133 (-24%) 115 (-35%)

Managed open space 328 307 (-7%) 300 (-9%) 274 (-17%) 220 (-33%) 183 (-44%)

Agriculture 93 89 (-4%) 88 (-5%) 85 (-8%) 75 (-19%) 65 (-30%)

Wetland forest 70 69 (-1%) 63 (-10%) 26 (-63%) 23 (-67%) 23 (-67%)

Ephemerally flooded marsh 29 24 (-17%) 96 (238%) 65 (126%) 87 (204%) 103 (260%)

Transitional scrub 63 141 (124%) 97 (54%) 112 (78%) 124 (98%) 119 (89%)

Regularly flooded marsh 132 173 (32%) 188 (43%) 297 (126%) 239 (82%) 234 (78%)

Tidal flat 177 154 (-13%) 132 (-26%) 102 (-42%) 217 (23%) 166 (-6%)

Estuarine open water 4148 4181 (1%) 4222 (2%) 4347 (5%) 4556 (10%) 4787 (15%)

Irregularly flooded marsh 168 145 (-14%) 157 (-7%) 178 (6%) 170 (1%) 197 (17%)

Irregularly flooded forest 131 117 (-10%) 90 (-31%) 47 (-64%) 13 (-90%) 13 (-90%)

37

Appendix B: Multi-temporal maps showing MAIM results for 

the high SLR scenario for each modeling region

Figure B1: The southern-most section of the George Washington Memorial 

Parkway habitat classification map in 2010 (upper left), 2030 (middle left), 2050 

(lower left), and 2080 (upper right) under high SLR scenario. 
38

Figure B2: The central section of the George Washington Memorial Parkway 

habitat classification map in 2010 (upper left), 2030 (upper right), 2050 (lower left), 

and 2080 (lower right) under high SLR scenario.
39

Figure B3: Central section of National Capital Parks – East along the Potomac 

River (Harmony Hall) habitat classification map in 2010 (upper left), 2030 (upper 

middle), 2050 (upper right), and 2080 (lower left) under high SLR scenario.
40

Figure B4: Central section of National Capital Parks – East along the Potomac 

River (Oxon Cove) habitat classification map in 2010 (upper left), 2030 (upper 

middle), 2050 (upper left), and 2080 (lower left) under high SLR scenario.
41

Figure B5: Section of the George Washington Memorial Parkway around 

Dangerfield Island and Reagan National Airport. Habitat classification map in 

2010 (upper left), 2030 (upper right), 2050 (lower left), and 2080 (lower right) under 

high SLR scenario.
42

Figure B6: Theodore Roosevelt Island habitat classification map in 2010 (upper left), 2030 (upper right), 2050 

(lower left), and 2080 (lower right) under high SLR scenario.
43

Figure B7: Little Falls vegetation classification map in 2010 (upper left), 2030 (upper middle), 2050 (upper right), 

and 2080 (lower left) under high SLR scenario.
44

Figure B8: Lands of National Capital Parks – East along the Anacostia River including Anacostia Park and 

Kenilworth Park and Aquatic Gardens. Habitat classification map in 2010 (upper left), 2030 (upper middle), 2050 

(upper right), and 2080 (lower left) under high SLR scenario.
45

Figure B9: National Capital Parks East along the Anacostia River. Habitat 

classification map in 2010 (upper left), 2030 (middle left), 2050 (lower left), and 

2080 (upper right) under high SLR scenario.
46

Figure B10: Theodore Roosevelt Island habitat classification map in 2010 (upper left), 2030 (upper middle), 2050 

(upper right), and 2080 (lower left) under high SLR scenario.
47

Figure B11: The Piscataway Park section of National Capital Parks East habitat 

classification map in 2010 (upper left), 2030 (middle left), 2050 (bottom left), and 

2080 (upper right) under high SLR scenario.
48

Figure B12: The eastern section of Piscataway Park (National Capital Parks East) habitat classification map in 2010 

(upper left), 2030 (upper middle), 2050 (upper right), and 2080 (lower left) under high SLR scenario.
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