Fine-Scale Mapping of Coastal Plant Communities in the Northeastern USA

Home , High marsh, Low marsh, Tidal marsh

Wetlands https://doi.org/10.1007/s13157-018-1028-3

APPLIED WETLAND SCIENCE

Maureen D. Correll1,2 & Wouter Hantson1 & Thomas P. Hodgman3 & Brittany B. Cline1,4 & Chris S. Elphick 5 & W. Gregory Shriver4 & Elizabeth L. Tymkiw4 & Brian J. Olsen1

Received: 12 November 2017 /Accepted: 16 March 2018 # Society of Wetland Scientists 2018

Abstract Salt marshes of the northeastern United States are dynamic landscapes where the tidal flooding regime creates patterns of plant zonation based on differences in elevation, salinity, and local hydrology. These patterns of zonation can change quickly due to both natural and anthropogenic stressors, making tidal marshes vulnerable to degradation and loss. We compared several remote sensing techniques to develop a tool that accurately maps high- and low-marsh zonation to use in management and conservation planning for this ecosystem in the northeast USA. We found that random forests (RF) outperformed other classifier tools when applied to the most recent National Agricultural Imagery Program (NAIP) imagery, NAIP derivatives, and elevation data between coastal Maine and Virginia, USA. We then used RF methods to classify plant zonation within a 500-m buffer around coastal marsh delineated in the National Wetland Inventory. We found mean classification accuracies of 94% for high marsh, 76% for low marsh zones, and 90% overall map accuracy. The detailed output is a 3-m resolution continuous map of tidal marsh vegetation communities and cover classes that can be used in habitat modeling of marsh-obligate species or to monitor changes in marsh plant communities over time.

Keywords High marsh . NAIP . Random Forest . Remote sensing . Spartina . Tidal marsh

Introduction globe. These marshes serve as a gateway between land and sea for humans and wildlife alike, act as a buffer against coastal Coastal marshes are among the world’smostproductiveeco- storms, and provide critical nutrients to marine food webs systems and provide significant services to humans across the (Barbier et al. 2011). Tidal marshes also support and protect biodiversity by providing habitat to marine and estuarine fish, crustacean populations, and migratory birds (Boesch and Electronic supplementary material The online version of this article Turner 1984;Master1992; Brown et al. 2002). (https://doi.org/10.1007/s13157-018-1028-3) contains supplementary ’ material, which is available to authorized users. Within the world s tidal marsh systems, those located along the Atlantic coast of the United States support the * Maureen D. Correll highest number of terrestrial vertebrate specialists de- [email protected] scribed worldwide (Greenberg et al. 2006). This suite of species includes herpetofauna and mammals, but the ma- 1 School of Biology and Ecology, The University of Maine, jority of described vertebrate specialists are birds. Several Orono, ME 04469, USA species are limited completely to these marshes during the 2 Bird Conservancy of the Rockies, Fort Collins, CO 80521, USA breeding season, several of which are in decline (Correll et al. 2017), with extinction predicted for the saltmarsh 3 Maine Department of Inland Fisheries and Wildlife, Bangor, ME 04401, USA sparrow within 50 years (Correll et al. 2017;Fieldetal. 2017a, c). 4 Department of Entomology and Wildlife Ecology, The University of Delaware, Newark, DE 19716, USA These declining species nest predominantly within the high- marsh zone, one of several vegetation communities found with- 5 Department of Ecology and Evolutionary Biology and Center for Conservation and Biodiversity, University of Connecticut, in coastal marshes. High marsh differs from other marsh areas Storrs, CT 06269, USA in elevation, salinity, and frequency of inundation (Bertness Wetlands and Ellison 1987; Pennings and Callaway 1992; Ewanchuk The physical and biological characteristics that differentiate and Bertness 2004) and is characterized by flooding during high marsh from low marsh and other marsh plant communi- spring tides linked to the lunar cycle. In the northeastern ties are potentially detectible using remotely-sensed multi- United States, the plant species Spartina patens, short-form spectral and hyperspectral imagery. Both types of imagery S. alterniflora, Distichlis spicata,andJuncus gerardii charac- can record wavelengths of light outside of the visible range terize high-marsh zones, which also include Salicornia spp., for humans, with hyperspectral imagery recording reflectance Glaux maritima,andSolidago sempervirens (Nixon and values in much finer detail and precision (hundreds of indi- Oviatt 1973;Bertness1991; Emery et al. 2001, Ewanchuk vidual bands recorded) than multispectral imagery (several and Bertness 2004). Conversely, low marsh is characterized wide-ranging bands recorded, e.g. red green blue, or RGB, by daily tidal flooding and is a near monoculture of tall form imagery). Several studies have previously demonstrated dis- S. alterniflora. The surrounding terrestrial border experiences tinct spectral differences between tidal marsh species using infrequent inundation by salt water during extreme tides and hyperspectral imagery (Rosso et al. 2005; Belluco et al. storms, and is characterized by a more diverse flora that is often 2006; Yang 2009). Such imagery, when combined with eleva- dominated by Iva frutescens and Typha spp. (Miller and Egler tion data, has previously produced high-accuracy classifica- 1950; Ewanchuk and Bertness 2004). Introduced Phragmites tions of tidal marsh vegetation communities, albeit at smaller australis (hereafter Phragmites) also occurs within this ecosys- spatial scales (e.g. Hladik et al. 2013). tem, especially around the borders of disturbed marshes Hyperspectral imagery is costly, however, especially across (Chambers et al. 1999; Philipp and Field 2005). large landscapes (Adam et al. 2010); Belluco et al. (2006) These plant community zones can be quickly altered by compared several aerial and satellite sensors with changing both natural and anthropogenic stressors such as sea-level rise, spatial and spectral resolution, and although hyperspectral nutrient run-off from adjacent uplands, and the spread of in- imagery performed slightly better than the multispectral, troduced species (Day et al. 2008). The significant increase in spatial resolution was the most important factor in classifier sea level during recent decades poses one of the largest threats performance. Belluco et al. (2006)recommendtheuseof to these marsh ecosystems. As sea levels encroach on the multispectral satellite imagery for the mapping of marsh veg- marshes’ seaward side and upland marsh migration is limited etation. Beside the visible spectrum (RGB), multispectral im- by human-developed coastal infrastructure (Field et al. 2017b) agery should also include infrared (IR) reflectance values to and upland habitats (Field et al. 2016), a Bpinching effect^ can allow differentiation of vegetation types, calculation of vege- occur, resulting in marsh loss. Coastal marshes can combat tation indices (e.g. the Normalized Difference Vegetation rising sea levels through vertical growth, or accretion Index or NDVI, Rouse et al. 1974) and detection of soil mois- (Kirwan et al. 2016), but when the rate of sea-level rise ex- ture differences (Jin and Sader 2005;Pettorellietal.2005), ceeds the rate of accretion, marsh area will decline (Crosby particularly in tidal wetlands (Klemas 2011). The IR spectrum et al. 2016). Rising sea levels can also drive invasion of high has previously been used as a tool to predict tidal marsh com- marsh areas with flood-tolerant low marsh species (Donnelly munities both in smaller regions within the northeastern and Bertness 2001; Field et al. 2016), causing transition from United States (Gilmore et al. 2008; Hoover et al. 2010; high to low marsh (Kirwan et al. 2016). This pattern, however, Meiman et al. 2012) and elsewhere (Isacch et al. 2006;Liu is not ubiquitous to all marshes (Kirwan and Guntenspergen et al. 2010). An exception to this has been in the classification 2010; Wilson et al. 2014). In addition to sea-level rise, ex- of invasive Phragmites that often borders tidal marshes. treme storm events that flood the coastline have been shown Large-scale classification of this wetland class has met with to permanently alter marsh structure within days (Day et al. some success (e.g. Bourgeau-Chavez et al. 2015,Longetal. 2008) and can have a lasting effect on plant community struc- 2017), however success is limited when using RGB and IR ture and saltmarsh degradation. inputs alone (Samiappan et al. 2017). A large-scale effort to Marsh degradation and rapid change is likely to continue map coastal Phragmites in the northeastern US has not yet into the future due to the paired effects of climate change and been attempted. human development. Sea levels are expected to rise substan- Due to the large spatial scale at which northeastern tidal tially between 2013 and 2100 (IPCC 2014), and continuing marshes occur, publicly-available and low-cost imagery storm events affecting coastal regions are also predicted. The datasets offer the most promising option for repeatedly delin- future distribution of high- and low-marsh habitat therefore eating large swaths of marsh along the coast. Landsat satellite remains uncertain (Chu-Agor et al. 2011;Kirwanetal. imagery provided by the National Aeronautic and Space 2016). It is essential to develop tools to identify coastal marsh Administration (NASA) is publicly available multispectral plant communities, particularly high marsh, on a biologically imagery including RGB and IR bands provided at a 30 × relevant timescale to protect existing ecosystem services and 30 m resolution and is often used for classifying coarse cover to inform the adaptive management of coastal wetlands as types across large landscapes. In the case of tidal marshes, habitat for high-marsh specialist species. however, the heterogeneity in tidal marsh vegetation often Wetlands occurs at scales smaller than 30 m pixels, and large-scale rocky or highly sloped shorelines in the north limits marshes classifications of tidal marsh plant communities using this to small (~10–100 ha) patches, while southern marshes form imagery have so far been unfruitful (e.g. Correll 2015). larger patches of marsh along the coast. Across our study area, There is thus a clear need for an alternative path to create a however, tidal marsh ecosystems can be reliably separated regional classification of tidal marsh vegetation. into six distinct cover types, plus two bordering cover types, Recent advances in high-resolution airborne imagery pro- which we included in our marsh mapping effort: vide new opportunities to develop large-scale classifications of coastal plant communities in the northeast, including 1. High marsh: Area flooded during spring tides related to Phragmites (e.g. Xie et al. 2015). The National Agricultural the lunar cycle and dominated by Spartina patens, Imagery Program (NAIP) from the US Department of Distichlis spicata, Juncus gerardii, and short form Agriculture (USDA 2016) captures 3-band, high-resolution Spartina alterniflora. Other species include Juncus RGB orthophotos during the growing season. Since 2007 roemerianus, Scirpus pungens, Scirpus robustus, most states have added a Near-InfraRed (NIR) band to the Limonium nashii, Aster tenuifolius,andTriglochin image requirements to aid in the accurate classification of maritima. vegetative cover. The image resolution is 1 m with 6-m hori- 2. Low marsh: Area flooded regularly by daily tides and zontal accuracy and a maximum of 10% cloud cover. The dominated by tall form Spartina alterniflora. imagery, freely available for governmental agencies and the 3. Salt pools/pannes: Depressed, bare areas with sparse public, is an affordable alternative to commercial aerial and vegetation cover and extremely high soil salinities. high-resolution multispectral satellite imagery. Recent appli- Generally, pools retain water between high tides while cations of NAIP imagery include mapping of tree cover pannes do not. (Davies et al. 2010), forest clearings (Baker et al. 2013), iso- 4. Terrestrial border: Area infrequently flooded by storm lated trees (Meneguzzo et al. 2013), land cover classification and spring tides and can include areas of marsh with fresh/ (Baker et al. 2013) and mining activity (Maxwell et al. 2014). brackish water due to a high water table and/or runoff In this study we compare several remote sensing tech- from impervious surfaces. Typical plant species include niques applied to NAIP imagery, elevation data from the Typha angustifolia, Iva frutescens, Baccharis halimifolia, National Elevation Dataset (NED) provided by the US Solidago sempervirens, Scirpus robustus, and Spartina Geological Survey (USGS 2015), and local tidal information pectinata. records from the National Oceanic and Atmospheric 5. Phragmites: The exotic invasive form of Phragmites Administration (NOAA 2016) to develop an affordable tool australis (subspecies australis). This subspecies is of con- capable of repeated classification of high-marsh zones in tidal siderable management interest (Saltonstall 2002), espe- marshes in the northeastern United States. We then use the cially in marshes with freshwater input, upland develop- best-performing classifier to categorize marsh vegetation ment, and/or increased nutrients (Dreyer and Niering communities with a 3-m resolution from coastal Maine to 1995;Bertnessetal.2002;SillimanandBertness2004). Virginia, USA. 6. Mudflat: Exposed muddy areas free of vegetation. 7. Open water (bordering cover type): Channels and bays leading to open ocean included within the 500-m buffer. Methods 8. Upland (bordering cover type): All non-marsh terrestrial cover included within the 500-m buffer. Study Site and Community Types

Our marsh-mapping effort encompasses all salt marshes of the Data Sources Northeast Atlantic coast of the USA, from northern Maine to Virginia. To define our classification extent we applied a 500- We collected training data for marsh vegetation classes both m buffer to all coastal, tidal marsh as delineated by the in the field and remotely using aerial imagery, depending on National Wetland Inventory (NWI, USFWS 2010) estuarine the cover type. We collected training polygons for high emergent wetland (E2EM) layer. The study site is further split marsh, low marsh, and Phragmites between May and into 8 subzones (Fig. 1) to accommodate data management August of 2015 and 2016. Technicians collecting polygon and processing. data were collectively trained at the beginning of the season These coastal marshes vary substantially from north to in salt marsh vegetation identification. Phragmites poly- south. Due to local bathymetric structure, tidal amplitudes in gons mapped were of the invasive Phragmites australis the Gulf of Maine are among the highest in the world (Garrett australis and not of the native North American form 1972), while those farther south experience much less varia- Phragmites australis americanus. Tecnnicians used a tion between high and low tides. Similarly, a preponderance of GEO7XTrimbleGPS(Trimble2015) without an external Wetlands

Fig. 1 Geographic extent of our study site from Maine to Virginia, USA, divided into 8 N biogeographic regions used in the remote sensing classification of tidal marsh cover types

training polygonsSources: Esri, DeLorme, USGS, NPS, Sources: Esri, for classifierUSGS, NOAA comparison 41°N 42°N 43°N 44°N 45°

Analysis Zones

Zone 1 Zone 2 Zone 3 Region used for classifier comparison Zone 4 Zone 5 Zone 6 Zone 7

37 °N 38°N0100200 39°N50 40°N 41°N Kilometers 42°N Zone 8

Sources: Esri, USGS, NOAA antenna for all community delineation. Horizontal accuracy classes are easily identifiable in visible wavelength of this unit without the external antenna is estimated at <1 m imagery. by the manufacturer. We used the most recent digital ortho-photography (RGB We used a generalized random tessellation stratified and NIR) available from the NAIP collected during the (GRTS) sampling framework designed to sample tidal growing season from 2014 or 2015 as imagery predictor marsh bird communities across all ownership types data (see Appendix A for acquisition year by state). We (Wiest et al. 2016) to select randomly-located delineation resampledraw1mNAIPimageryto3mresolutionto sites for training data across our study area. Technicians match the spatial scale of the NED, which was used as the navigated to bird survey points and located contiguous digital elevation model (DEM) for this analysis. We also patches of high marsh, low marsh, and Phragmites larger calculated NAIP imagery derivatives using ArcMap 10.3 than 10 × 10 m as they traversed to each bird survey lo- usingtherawbandvalues.Werefertothemas‘pseudo’- cation. At each patch, they placed a stake flag or other vegetation indices because we used the raw band values highly visible marker on the ground to indicate the begin- instead of reflectance values. In total, we used the following ning of polygon delineation and then delineated the outer data inputs as predictor variables: DEM, Raw NAIP Band 1, boundary of the patch on foot by walking the outer pe- Raw NAIP Band 2, Raw NAIP Band 3, Raw NAIP band 4, rimeter with the GEO 7X. We collected training data for NDVI, the Normalized Difference Water Index (McFeeters open water, pools and pannes, and mudflat cover classes 1996), the Difference Vegetation Index (Richardson and using manual digitization of 2014–2015 1-m NAIP imag- Wiegand 1977), and the first three principle components ery using ArcGIS 10.3 (ESRI 2016) since these cover from a principal components analysis (Fung and Ledrew Wetlands

1987) of the four NAIP bands, which collectively explained Maximum Flooding (MAX) Upland Terrestrial border >95% of the variance. Highest Astronomical Tide (HAT) High Marsh Marsh habitats are often influenced by their elevation and Mean Highest High Tide (MHHT) topographic context and therefore can often be successfully Mean High Tide (MHT) Low Marsh mapped using elevation data (e.g. Hladik et al. 2013,Maxwell et al. 2016). Elevational data is particularly helpful in tidal Mean Sea Level (MSL) marshes, where topography can drive tidal flooding frequency Open Water and thus can influence plant species zonation (Silvestri et al. Fig. 2 Graphic representation of the tidal elevation limits for tidal marsh 2005). We used the NED for all elevation predictor data. The community classification effort in the northeastern USA based on NED is derived from different contributed datasets and then flooding history are represented by the National Oceanic and processed by the USGS into a near-continuous DEM at vari- Atmospheric Administration (NOAA) vertical datums ous resolutions across the US. We used 1/9-s (~3 m resolution) data when available for our classification. When no 1/9 arc- there was no NED DEM layer available, we classified the second imagery was available, we used 1/3 arc-second data marsh communities without the DEM data input. In these (~10 m resolution). To account for the large differences in tidal cases, no terrestrial border or upland was defined. inundation across our study area, we collected tidal data for the study area from the closest NOAA tidal gauge station, Data Analysis creating 29 different tidal zones (NOAA 2016). For each of the stations we collected the following tidal datums: HAT, We compared three classification methods using training data MHHW, MHW, MSL, NAVD88 and MAX (Table 1). We collected in 2015 to delineate tidal marsh cover types. We resampled the NED data to an exact 3 m resolution to match conducted this comparison of classifiers on NAIP imagery, the upscaled NAIP imagery and clipped the resulting imagery imagery derivatives, and NED elevation data from the center with the 500 m buffer around all coastal tidal marsh in the of our study area (Delaware Bay, subregion 6, Fig. 1)tomax- NWI. We further clipped the NED by the 29 tidal gauge zones imize utility of the resulting method to both the north and of the study area, and rescaled each zone to the NAVD88 south. First, we used classification and regression trees datum using the NOAA tidal amplitude data. To calibrate (CART), a fast and flexible rule-based classifier where no the DEM across the entire study site we used the Mean High statistical data distribution is required (Otukei and Blaschke Tide (MHT) divided by Mean Highest High Tide (MHHT) 2010). CART methods are particularly useful when integrat- value for each tidal gauge zone as a basis for elevational dif- ing environmental variables with different measurement ferences between marsh zone types. scales and are robust for large datasets. Post-hoc pruning We classified the NED of each tidal gauge zone based on removes nodes with low explanatory power to reduce the tidal amplitude data for that zone. To do this, we rescaled overfitting. We used the R package rpart (Therneau et al. NOAA tidal amplitude data to match the NAVD88 datum 2015) which implements the CART methods described by used in the elevation dataset, and defined elevation limits for (Breiman et al. 1984). Secondly, we used random forests open water, high marsh, low marsh, terrestrial border, and (RF), which are collections of decision trees that improve upland class based on flooding history (Fig. 2). We then used the accuracy and stability of a single decision tree (Breiman these thresholds in conjunction with NAIP imagery reflec- 2001). RFs perform well with small training sets, uses a ran- tance values and derivatives to identify water, high marsh, dom subset of the training data to calculate the variable im- low marsh, and Phragmites cover types. We used only eleva- portance, and similar to cross-validation the out-of-bag (OOB) tion thresholds to define the terrestrial border and upland cov- error estimates delivers a measure of classification accuracy er types. In rare cases in small areas along the coast where (Breiman 2001). We used the R package RandomForest (Liaw

Table 1 Summary and definitions of datums collected by Datum NOAA explanation the National Oceanic and Atmospheric Administration MAX Maximum: Highest observed water level. (No datum) (NOAA) and used to classify HAT Highest Astronomical Tide: The elevation of the highest marsh cover type from Virginia to predicted astronomical tide expected to occur. northern Maine, USA MHHW Mean Higher High Water: Average of the higher high water height of each tidal day. MHW Mean High Water: Average of all the high water heights. MSL Mean Sea Level: The arithmetic mean of hourly heights observed NAVD88 North American Vertical Datum of 1988 Wetlands and Wiener 2002) with Ntree and mtry default values for all low marsh cover types returned accuracy rates ranging be- RF analyses. Finally, we used support vector machines tween 73% and 88% across all classifiers, with RF producing (SVM), which are non-parametric classifiers that use risk min- substantially lower error rates for high marsh and all three imization to separate classes defined by the ‘support vectors’, classifiers producing similar levels of accuracy for low marsh or points that occur closest to the splitting threshold. In gen- (Table 3). All classifiers produced low accuracy rates for the eral, SVM offers high training performance versus low gener- Phragmites cover type, ranging from 32 to 55%, but SVM alizing errors, but is sensitive to over-fitting, especially with methods were substantially worse when compared with the noisy and unbalanced data. We used the svm function in the R other two classifiers. package e1071 (Dimitriadou et al. 2006) with the default pa- Our final data layer produced through RF methods classi- rameter values and polynomial kernel. fied a total of 16,014 km2 of tidal marsh and bordering com- To select the best remote sensing classifier for the final munities within our defined 500-m buffer at a 3-m resolution marsh layer, we classified and validated a randomly-selected (Fig. 3). This layer is now publicly available for public down- independent training and validation subset (66% training, load at https://nalcc.databasin.org/galleries/46d6e771dd6f4 33% validation) of the first year of polygon training data fdb8aa5eb46efffffa7. (2015, n = 36 training polygons) from zone 6 and compared Mean classification accuracies varied among cover types, classification accuracies across CART, RF, and SVM ranging from >99% for open water and mudflat to 25% for methods. We also considered specifications and known Phragmites (Table 2). Within geographic zones, open water, strengths of the of the classifiers including robustness, sensi- mudflat, and pools/pannes were classified with high (>95%) tivity to over fitting, and performance with relatively small accuracy in almost all cases. Classification of the three vege- training datasets. tation types varied among cover classes. High marsh was clas- We applied the best performing classifier, RF, to all biogeo- sified with a mean accuracy of 94%, but with clear regional graphic zones from Maine to Virginia using NAIP imagery, variation. In the regions from New Jersey north and in the imagery derivatives, NED elevation data, and NOAA tidal inner portions of Chesapeake Bay, accuracy was generally gauge information. For the final classification, we used the greater than 95%. In contrast, accuracy from Delaware Bay out-of-bag (OOB) error estimates produced by the RF algo- to Virginia was lower with 83–87% of high marsh correctly rithm to measure accuracy of our classification by zone. We classified. High marsh was most commonly misclassified as then clipped the resulting marsh classification by the DEM- low marsh. Classification of low marsh was generally less based cover types (upland, terrestrial border), wherever the accurate than high marsh, with an overall accuracy of 77%. NED layer was available. Due to variable image quality and/ Low marsh was regularly confounded with open water and or due to high tide during image acquisition, we sometimes classification accuracy tended to be higher in the southern encountered artefacts in the imagery that affected the accuracy zones, especially the inner portions of Delaware and of our classification, particularly in Zones 1 and 6. Sun glitter Chesapeake Bays. or high mud content in open water sometimes caused misclas- Classification of Phragmites showed the greatest variation sification of this cover type as low marsh. We used the RF across regions, and was often confounded with terrestrial bor- probability scores for the open water class to better represent der. Overall, classification accuracy for this cover type was the actual water cover and then updated the open water clas- 75%, and in coastal Massachusetts accuracy approached sification in Zones 1 and 6 to improve overall accuracy. All 95%. By contrast, overall Phragmites accuracy in coastal methods and datasets involved in our study are freely available New Jersey was 67%, and in our northernmost region (coastal to the public, and our analyses are limited to tools available New Hampshire and Maine) accuracy was 20%. through ArcGIS, a geographic information system commonly- Finally, almost 4000 km2 of the data layer is covered by used by federal, state, and private conservation organizations, tidal marsh (i.e., excluding open water or upland, Table 4), or simple open-source Program R code (Appendix B). with the majority classified as high marsh (36%) followed by low marsh (21%), mudflats (7%), Phragmites (7%), pools and pannes (5%), and terrestrial border (24%). The distribution of Results cover types varies from north to south with an overall increase in low marsh area and Phragmites to the south. Conversely, We collected a total of 2655 field training polygons across all the percentage of mudflat cover increases to the north. cover types from Maine to Virginia (Table 2). Of these training polygons, 36 were used in our methods comparison in Zone 6 for high marsh (n = 18), low marsh (n =14),andPhragmites Discussion (n = 4). In this comparison we found that RF methods generally outperformed the other two tools in classification of tidal Tidal marshes of the northeastern USA are critical pieces marsh plant communities (Table 3). Classification of high and of the coastal landscape, providing key wildlife habitat Wetlands

Table 2 Percent classification error for cover classes of tidal marsh vegetation communities from Virginia to Maine, USA

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7 Zone 8 Mean (μ)

OOB # polygons OOB # polygons OOB # polygons OOB # polygons OOB # polygons OOB # polygons OOB # polygons OOB # polygons OOB # polygons

High Marsh 1.97% 223 1.35% 37 4.89% 70 1.67% 74 5.99% 103 12.23% 65 16.28% 81 2.29% 16 5.83% 669 Low Marsh 26.07% 136 23.99% 16 21.35% 32 27.63% 28 29.61% 39 19.01% 31 23.56% 41 13.65% 10 23.11% 333 Mudflat 0.09% 57 0.05% 46 0.09% 63 2.83% 9 0.80% 29 1.30% 64 0.53% 24 0.32% 2 0.75% 294 Phragmites 80.20% 36 5.82% 8 16.02% 29 13.66% 12 33.09% 14 19.68% 14 17.22% 22 14.79% 13 25.06% 148 Pool/Panne 4.54% 67 3.91% 45 5.88% 35 12.92% 40 4.49% 37 3.45% 74 0.82% 43 0.00% 19 4.50% 360 Open Water 0.09% 91 0.44% 62 0.03% 148 0.31% 66 0.26% 119 0.07% 190 0.23% 134 0.08% 41 0.19% 851

Coverage created through random forest classification, with estimates organized by regional zones that extend from the Gulf of Maine (zone 1) to the eastern Chesapeake Bay (zone 8, Fig. 1). Error percentages were derived from out-of-bag (OOB) errors Wetlands

Table 3 Average classification error (%) for three different remote to Virginia; the resulting data layer is the first of its kind sensing techniques for classifying tidal marsh vegetation communities to classify this marsh ecosystem at such a high spatial in Delaware Bay, USA using National Agricultural Imagery Program (NAIP) imagery from 2015 resolution (3 m) regionally, with a mean map classification accuracy of 90% and a classification accuracy for High marsh Low marsh Phragmites high marsh vegetation of 94%. Random forests (RF) 11.86% 15.86% 48.73% The high classification accuracies in the high marsh zone make this data layer particularly helpful for use by Classification and 27.42% 18.78% 45.61% regression trees (CART) marsh managers, researchers, and planners. Tracking Support vector 26.51% 13.81% 68.29% change in marsh vegetation can be used to understand machines (SVM) which marshes are most impacted by sea-level rise, predict changes in flooding risk potential for coastal properties (Arkema et al. 2013),andtoidentifymarsheswith and ecosystem services to humans (Barbier et al. 2011). low rates of change to protect as important habitat for Our effort applies well-established methods and data marsh-obligate species. As sea levels continue to rise sources for remote sensing of wetlands to classify plant and human development continues to alter marsh hydrol- communities within this important ecosystem from Maine ogy and accretion (Day et al. 2008), a method to

A43 42

41 0 . .525 1 Kilometers 40

B B 39

High Marsh Low Marsh Mudflat Phragmites Pool/Panne Open water Terrestrial border Upland

Fig. 3 Visualization of a detailed random forest classification of northern Massachusetts and (b) Delaware Bay, New Jersey; entire vegetation communities within tidal marsh habitat in the Northeastern coverage is available at https://nalcc.databasin.org/galleries/ USA. Maps shows example portions of the entire coverage in (a) 46d6e771dd6f4fdb8aa5eb46efffffa7 Wetlands

Table 4 Total marsh area (km2) 2 and percentage of total marsh for Total Marsh (km )ZoneZone Zone Zone Zone Zone Zone Zone each cover class throughout the 1 2 3 4 5 6 7 8Total northeastern USA, from Virginia 398 177 214 243 653 515 542 914 3656 to Maine High Marsh (%) 38 36 33 36 54 26 40 27 36 Low Marsh (%) 17 9 6 8 21 44 32 13 21 Mudflat (%) 28 21 10 5 3 4 1 1 7 Phragmites (%)1749578127 Pool/Panne (%) 8 3 3 3 6 5 3 6 5 Terrestrial Border 7 2444381015164124 (%)

Coverage created through random forest classification, with estimates organized by regional zones that extend from the Gulf of Maine (zone 1) to the eastern Chesapeake Bay (zone 8, Fig. 1)

repeatedly classify cover types of coastal marsh will be collected the imagery included in each analysis zone integral to measuring the amount and distribution of avail- across different times of tidal inundation, making a quan- able habitat and change in habitat over time. The small titative comparison of the combined effects of these short- spatial resolution and high horizontal accuracy of our data comings difficult. Conveniently, the RF methods used in layer also allows it to be used across varying spatial ex- our final classification workwellwiththesedescribed tents, from local municipalities to multi-state regions. sources of variation with low threat of over-fitting (see The NAIP dataset used in our classification offers a comparison of data sources and RF classification in high-resolution, low-cost set of multispectral imagery Fig. 4). Classification success (Table 2) assessed through with a refresh rate of 3 years. This dataset, however, has OOB error estimate shows high average accuracies for limitations when used over large areas (see also most cover types, although there is variation depending Meneguzzo et al. 2013), and potential users should care- on biogeographic zone. Additional work to collect inde- fully consider the pros and cons of this dataset before pendent data and compare it to this layer’s predictions setting out on a similar classification effort. Variation in would be a valuable next step to identify ways to improve the time and day of image acquisition across the NAIP current methods; while OOB error estimates are a com- dataset results in different tidal stages, plant phenology, monly used measure of RF accuracy (Breiman 2001), they and illumination across images. The NAIP post-hoc color do not systematically evaluate classification accuracy out- balancing applied to these images by each contractor (7 side the training polygons (e.g. Fry et al. 2011). Future contractors across our study area) does not correct for studies to independently validate our classification outside differences in illumination and atmosphere in a standard- of OOB error estimation the will directly strengthen sup- ized way. This results in a radiometric imbalance across port for the long-term use of this tool in monitoring com- the spatial extent of the dataset, limiting the use of the munity change in tidal marshes over time. In particular, NAIP dataset in large-scale classification efforts to vege- additional tests to assess on-the-ground accuracy should tative communities with either large differences in reflec- involve data collected across the entire region due to the tance values across the RGB and NIR bands (as is the OOB differences we find across our analysis zones. case with tidal marsh) or those varying across other char- Although the primary focus of our study was to distin- acteristics measurable across large areas (e.g. elevation in guish between high and low marsh, the importance of the case of tidal marshes). Further, the temporal resolution invasive Phragmites to many management decisions led of the NAIP dataset (one set of images per year, flown us to also consider this cover type. Unfortunately, during Bgreenup^ between June and August) is ideal for Phragmites proved particularly difficult to classify, how- marsh classification but limits the use of NAIP data to ever we have chosen to keep this cover type in the overall analyses specific to this time of the year. Since the timing classification because accuracy is relatively high (>70%) of high and low tide changes daily, this temporal resolu- in the majority of our analysis zones (zones 2–4,6–8). The tion also results in imagery taken at different parts of the nature and amount of training data available for this cover daily tidal cycle, and low marsh or mudflat areas were type (n = 148 polygons, Table 2) compared to the other likely partially flooded when most imagery was collected. cover classes likely contributed to error in our While these sources of error likely contributed to some Phragmites classification; previous work using RF noise within our classification effort, multiple contractors methods suggest that training sample balance in Wetlands

A B C 0500250 Meters

High Marsh Mudflat Pool/Panne Terrestrial border High : 61 m

Low Marsh Phragmites Open water Upland Low : -7 m Fig. 4 Comparison of (a) a random forest classification of tidal marsh vegetation communities in Scarborough Marsh, Maine with source data from (b) the National Agriculture Imagery Program and (c) the National Elevation Dataset (1/9 arc-second layer) imperative between classes for optimal classification re- 2) as a layer for local and regional analyses of this bio- sults (Belgiu and Dragut 2016). Our results may have also logical community, and 3) as a base layer against which been influenced by the size of our Phragmites training future comparisons can measure land cover change over polygons, as this rapidly-growing cover type can change time. These actions are all integral for the long-term pres- patch size quickly, influencing classifier accuracy ervation of tidal marshes and the species they support, (Kettenring et al. 2016) and make predicting across large especially as climate change and other human influences landscapes particularly difficult. Additionally, upon as- continue to affect this ecosystem. sessment of known marshes, the classification algorithm for Phragmites was regularly confounded with terrestrial Acknowledgements This work was made possible through financial sup- border species not included in the training data, particu- port from the North Atlantic Landscape Conservation Cooperative and the United States Fish and Wildlife Service (USFWS) Northeast Region larly with stands of Typha spp., which is similar in struc- Science Applications (#24), and the National Institute of Food and ture to Phragmites. This problem combined with the var- Agriculture, Hatch Project Number ME0-21710 through the Maine iable ground elevations at which Phragmites can be found Agricultural & Forest Experiment Station. This is Maine State likely further confounded the classification. A strong need Agricultural and Forest Experimentation Station Publication # 3590. We would like to thank all Saltmarsh Habitat and Avian Research remains for development of a method for large-scale clas- Program (SHARP) field technicians who collected field training data sification of this invasive species across latitudes, for this effort, and all participating landowners that allowed access to their flooding regimes, and imagery sources for use in moni- properties for surveying. We also thank Janet Leese for countless hours toring and management along the coast. spent digitizing training polygons in the lab. Comments from Erin and Kasey Legaard, D. Rosco, N. Hanson, and the Olsen Lab substantially improved the methods described here. Conclusion

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