Using Remote Sensing to Select and Monitor Wetland ... - BioOne

Journal of Coastal Research

29

4

958–970

Coconut Creek, Florida

July 2013

Using Remote Sensing to Select and Monitor Wetland Restoration Sites: An Overview Victor Klemas School of Marine Science and Policy University of Delaware Newark, DE 19716, U.S.A.

ABSTRACT Klemas, V., 2013. Using remote sensing to select and monitor wetland restoration sites: an overview. Journal of Coastal Research, 29(4), 958–970. Coconut Creek (Florida), ISSN 0749-0208. Coastal and estuarine wetlands represent highly productive and critical habitats for a wide variety of plants and animals and provide protection from storms and wave damage. However, wetland acreage in the continental United States has been steadily decreasing mainly as a result of human activities and sea level rise. Major efforts are being made by federal, state, and local agencies to protect existing wetlands, restore lost wetlands, and improve those stressed by human activities. The restoration process can involve removing exotic plants, removing bulkheads and fill, elevation grading, creating flushing channels, and planting native vegetation. Having developed criteria for selecting wetland sites to be restored or enhanced, wetland managers must prioritize the sites based on ecological and economic considerations. Remote sensing techniques can provide a cost-effective means for selecting restoration sites and observing their progress over time. The objective of this paper is to review airborne and satellite remote sensing techniques for identifying suitable wetland restoration sites and monitoring their progress.

ADDITIONAL INDEX WORDS: Wetland remote sensing, wetland restoration, restoration site selection, wetland restoration monitoring.

INTRODUCTION Coastal and estuarine wetlands represent highly productive habitats for a wide variety of plants, fish, shellfish, reptiles, mammals, birds, and other wildlife (Odum, 1993; Pinet, 2009). Wetlands also provide protection from floods, storm surge, and wave damage; water quality improvement through filtering of agricultural and industrial waste; and recharge of aquifers. Wetlands reduce shoreline erosion by absorbing wave action and protecting shoreline soils with root networks. Over the past 200 years, wetland acreage in the continental United States has steadily decreased from about 200 million acres to an estimated 95 million acres, mainly as a result of human activities and, more recently, sea level rise (Bedford, 1999; Mayer and Hill, 1993). About one-third of the remaining acres are located along the coasts. They include salt marshes, tidal flats, seagrass beds, kelp forests, coral reefs, and other coastal habitats. The losses of coastal and estuarine wetlands are caused by conversion to agriculture, transportation, construction and urbanization, industry, logging, aquaculture, storm surge, and sea level rise (Klemas, 2009).The environmental quality of wetland habitats is degraded by clearing, draining, diking, filling, dredging, and shoreline stabilization (Mayer and Hill, 1993). Furthermore, wetlands are affected by water diversions and other hydrologic changes, nutrient DOI: 10.2112/JCOASTRES-D-12-00170.1 received 30 August 2012; accepted in revision 23 October 2012 corrected proofs received 25 January 2013. Published Pre-print online 27 February 2013. Ó Coastal Education & Research Foundation 2013

enrichment and eutrophication, decreases in water clarity, toxic spills, and pollutants. Major efforts are being made by federal, state, and local agencies to protect existing wetlands, restore some wetlands that have been lost, and improve the ones stressed by human activities (Mitsch and Gosselink, 2007). Large-scale, systematic wetland restoration programs were started only in the early 1970s, when the U.S. Army Corps of Engineers studied the feasibility of creating habitat on dredged material. At present, there are hundreds of created, restored, or enhanced tidal marshes and mangrove forests in the United States that support a wide range of fish and wildlife populations. The degree of success for many of these restoration sites is still being debated, especially since there is no full agreement on criteria used to measure success. The creation, enhancement, or restoration of coastal habitats requires much time and constant attention. Seagrass communities may become fully functional in 3 years, marsh systems may take 15 or more years, and mangrove and coral reefs can take decades to recover (Mayer and Hill, 1993; Mitsch and Gosselink, 2007). Usually, the more complex and highly developed the habitat, the greater the time and care required to ensure a successful restoration (Odum, 1993). The restoration process can involve removing exotic plants, removing bulkheads and fill, specific elevation grading, creating flushing channels, and planting native vegetation. In addition, unconsolidated shorelines may need to be stabilized and enhanced with vegetation and protective barriers. Many states are improving recreational opportunities for wetlands by

Remote Sensing in Monitoring Wetland Restoration

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Table 1. High-resolution satellite parameters and spectral bands (Digital Globe, 2003; Orbimage, 2003; Parkinson, 2003; Space Imaging, 2003).

Sponsor Launched Spatial Resolution (m) Panchromatic Multispectral Spectral Range (nm) Panchromatic Coastal blue Blue Green Yellow Red Red edge Near-infrared Swath width (km) Off nadir pointing Revisit time (days) Orbital altitude (km)

IKONOS

QuickBird

OrbView-3

WorldView-1

GeoEye-1

WorldView-2

Space Imaging September 1999

Digital Globe October 2001

Orbimage June 2003

Digital Globe September 2007

GeoEye September 2008

DigitalGlobe October 2009

1.0 4.0

0.61 2.44

1.0 4.0

0.5 n/a

0.41 1.65

0.5 2

525–928 n/a 450–520 510–600 n/a 630–690 n/a 760–850 11.3 6268 2.3–3.4 681

450–900 n/a 450–520 520–600 n/a 630–690 n/a 760–890 16.5 6308 1–3.5 450

450–900 n/a 450–520 520–600 n/a 625–695 n/a 760–900 8 6458 1.5–3 470

400–900 n/a n/a n/a n/a n/a n/a n/a 17.6 6458 1.7–3.8 496

450–800 n/a 450–510 510–580 n/a 655–690 n/a 780–920 15.2 6308 2.1–8.3 681

450–800 400–450 450–510 510–580 585–625 630–690 705–745 770–1040 16.4 6458 1.1–2.7 770

constructing nature trails and greenways, observation towers, and canoe and boat ramps. After developing criteria for selecting wetland sites to be restored or enhanced, wetland managers must prioritize the sites based on ecological and economic considerations. When the actual restoration work is completed, they must produce a well-designed monitoring plan that remains in place over the life of the restoration, to track progress and take corrective actions if they are necessary (Daiber, 1986; Milano, 1999; Zedler, 2000). Remote sensing offers cost-effective means for selecting sites that are suitable for restoration and monitoring their progress after the first phase of restoration is completed. Yet only in few restoration projects are remote sensing techniques used systematically and to their fullest potential. The objective of this paper is to review cost-effective airborne and satellite remote sensing techniques for identifying suitable wetland restoration sites and monitoring their progress over time.

REMOTE SENSING OF COASTAL WETLANDS Advances in technology and decreases in cost are making remote sensing systems attractive for use in coastal ecosystem research and management, including wetland restoration (Kelly and Tuxen, 2009; Klemas, 2011a; Schmidt and Skidmore, 2003; Yang, 2009). They are also allowing researchers to take a broader view of ecological patterns and processes (Malthus and Mumby, 2003; Ozesmi and Bauer, 2002). Environmental indicators that can be detected by remote sensors are available to provide quantitative estimates of coastal and estuarine habitat conditions and trends. Such indicators include percentage of impervious watershed area, natural vegetation cover, buffer degradation, wetland loss and fragmentation, wetland biomass change, invasive species, water turbidity, chlorophyll concentration, and eutrophication (Lathrop, Cole, and Showalter, 2000; Underwood et al., 2006; Wang, 2010). Advances in the application of geographic information systems (GIS) help to combine remotely sensed images with other georeferenced data layers, such as digital elevation models, providing a convenient means for modeling

ecosystem characteristics. A good example is the predictive modeling of the impact of sea level rise on coastal wetlands (Church and White, 2006; Dahl, 2006; McInnes et al., 2006; Noe and Zedler, 2001). Because wetlands are spatially complex and temporally quite variable, mapping emergent and submerged aquatic vegetation requires high-resolution satellite or aircraft imagery and, in some cases, hyperspectral data (Belluco et al., 2006; Jensen et al., 2007; Shan and Hussain, 2010; Thomson et al., 1998; Yang, 2009). Traditionally aerial color photography has been used to map emergent and submerged wetlands. The recent availability of high spatial and spectral resolution satellite data, shown in Table 1, presents another option for mapping wetlands and general coastal vegetation (Ozesmi and Bauer, 2002). The new satellites, carrying sensors with fine spatial (1–4 m) or spectral (200 narrow bands) resolution are also providing the means for more accurately detecting changes in coastal wetland extent, ecosystem health, biological productivity, and habitat quality (Adam, Mutanga, and Rugege, 2010; Klemas, 2011a; Ozesmi and Bauer, 2002; Wang, 2010; Wang, Christiano, and Traber, 2010; Weatherbee, 2000). When studying small wetland sites one can use airborne or high-resolution satellite systems (Jensen, 2007; Klemas, 2011a). Table 1 shows key features of high-resolution satellite sensors. Accordingly, the high-resolution satellites can provide 0.5 to 1.0 m resolution in panchromatic bands and 2 to 4 m resolution in multispectral bands, covering the visible and near-infrared (IR) regions. Airborne georeferenced digital cameras, providing color and color infrared digital imagery, are particularly suitable for accurate mapping of wetlands or interpreting satellite data. Figure 1 shows a wetland map derived from an airborne digital camera image. At a spatial resolution of 0.5 m, the ADS-40 digital camera was able to identify three key species of marsh vegetation, i.e. Phragmites, Typha, and Spartina. Most digital cameras are capable of recording reflected visible to near-infrared light. A filter is placed over the lens that transmits only selected portions of the wavelength spectrum. For a single camera operation, a filter is chosen

Journal of Coastal Research, Vol. 29, No. 4, 2013

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Klemas

Figure 1. The wetlands map shown on the left was derived from an airborne ADS-40 digital camera image on the right. Credits: NOAA National Ocean Service.

that generates natural color (blue–green–red wavelengths) or color–infrared (green–red–near IR wavelengths) imagery. For multiple camera operation, filters that transmit narrower bands are chosen. For example, a four-camera system may be configured so that each camera filter passes a band matching a specific satellite imaging band, e.g. blue, green, red, and nearinfrared bands matching the bands of the IKONOS satellite (Table 1) multispectral sensor (Ellis and Dodd, 2000). Digital camera imagery can be integrated with global positioning system (GPS) position information and used as layers in a GIS for a wide range of modeling applications (Lyon and McCarthy, 1995). Small aircraft flown at low altitudes (e.g. 200–500 m) can also be used to supplement field data (McCoy, 2005). However, as shown in Table 2, cost becomes excessive if the site is larger than a few hundred square kilometers, and in that case, medium-resolution sensors, such as Landsat Thematic Mapper (TM) (30 m) and SPOT (20 m), become more cost effective than the high-resolution systems (Klemas, 2011a). New image analysis techniques using hyperspectral imagery and narrow-band vegetation indices have been able to discriminate some wetland species and estimate biochemical

and biophysical parameters of wetland vegetation, such as water content, biomass, and leaf area index (Adam, Mutanga, and Rugege, 2010; Gilmore et al., 2010; Ozesmi and Bauer, 2002; Pengra, Johnston and Loveland, 2007; Schmidt et al., 2004; Simard, Fatoyinbo, and Pinto, 2010; Wang, 2010). The integration of hyperspectral imagery and light detection and ranging (LIDAR)-derived elevation data has significantly improved the accuracy of mapping salt marsh vegetation (Yang and Artigas, 2009; Yang et al., 2009). Major plant species within a complex, heterogeneous tidal marsh have been classified using multitemporal high-resolution QuickBird satellite images, field reflectance spectra, and LIDAR height information (Hirano, Madden, and Welch, 2003; Ozesmi and Bauer, 2002; Schmidt et al., 2004). Synthetic aperture radar (SAR) technology provides the increased spatial resolution that is necessary in regional wetland mapping, and SAR data have been used extensively for this purpose (Bourgeau-Chavez et al., 2005; Lang and McCarty, 2008; Novo et al., 2002). Furthermore, SAR microwave energy is sensitive to variations in soil moisture and inundation and is only partially attenuated by vegetation canopies, especially in areas of lower biomass (Baghdadi,



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Table 2. Imagery acquisition costs (approximate). Resolution (m) Digital camera imagery (ADS40) Aerial hyperspectral (AISA)b High resolution satellite (IKONOS) Medium resolution satellite (Landsat TM) a b c

Swathwidth (km)

0.3a

Cost ($/km2) 330

2.3 1–4

0.6 13

175 30

30

180

0.02c

Cell area ¼ 2.3 3 2.3 km. 35 spectral channels (0.44–0.87 lm). $600 per scene (now free of charge).

Gaultier, and King, 2001; Kasischke and Bourgeau-Chavez, 1997; Kasischke et al. 1997; Kasischke, Melack, and Dobson, 1997; Lang and Kasischke, 2008; Rosenqvist et al., 2007; Townsend, 2000, 2002; Townsend and Walsh, 1998). The sensitivity of microwave energy to water and its ability to penetrate vegetative canopies make SAR ideal for the detection of hydrologic features below the vegetation (Hall, 1996; Kasischke, Melack, and Dobson, 1997; Kasischke and Bourgeau-Chavez, 1997; Phinn, Stow, and Van Mouwerik, 1999; Rao et al., 1999; Wilson and Rashid, 2005). For instance, highresolution SARs allow one to distinguish between forested wetlands and upland forests. Seagrass beds provide essential habitat for many aquatic species, stabilize and enrich sediments, dissipate turbulence, reduce current flow, cycle nutrients, and improve water quality (Hughes et al., 2009; Macleod and Congalton, 1998; Wolter, Johnston and Niemi, 2005). However, in many parts of the world, the health and quantity of seagrass beds has been declining (Green and Short, 2003; Hemminga and Duarte, 2000; Orth and Moore, 1983; Orth et al., 2006). The main challenge for remote sensing of submerged aquatic plants is to isolate the plant signal from the interference of the water column, the bottom, and the atmosphere. In addition to atmospheric effects and bottom reflectance, optically active materials, such as plankton, suspended sediment, and dissolved organics, affect the scattering and absorption of radiation. The green region of the spectrum is considered to be the best for sensing submerged macrophytes, followed by the red regions. The mapping of submerged aquatic vegetation (SAV), coral reefs, intertidal habitats, and general bottom characteristics has benefited from the newly available high-resolution (0.6–4 m) satellite and aerial hyperspectral imagery (Mishra et al., 2006; Mumby and Edwards, 2002; Philpot et al., 2004; Purkis, 2005; Purkis et al., 2002; Trembanis, Hiller, and Patterson, 2008). High-resolution multispectral data provided by satellites, such as IKONOS and QuickBird, have been used to map SAV with accuracies of about 75% for classes including highdensity seagrass, low-density seagrass, and unvegetated bottom (Table 1). Airborne hyperspectral imagers have improved the SAV and coral reef mapping results by being able to identify more estuarine and intertidal habitat classes (Fyfe, 2003; Han and Rundquist, 2003; Midwood and ChowFraser, 2010; Pinnel, Heege, and Zimmermann, 2004; Thomson et al. 1998; Underwood et al., 2006; Williams et al., 2003).

Figure 2. Top panel is a true-color EO-1 satellite image of the Blackwater National Wildlife Refuge in the Chesapeake Bay. The inset is a detailed USGS LIDAR topo-map. Credit: USGS-NASA.

LIDAR techniques, combined with GPS, can provide accurate topographical and bathymetric maps, including shoreline positions (Ackermann, 1999; Guenther et al., 1996; Krabill et al., 2000; Lillycrop, Pope, and Wozencraft, 2002). LIDAR surveys can produce a 10- to 15-cm vertical accuracy at a high spatial resolution. The LIDAR data are used in many research and management applications, including flood zone delineation, monitoring beach nourishment projects, and mapping changes along sandy coasts and shallow benthic environments due to storms or long-term sedimentary processes. Typically, a LIDAR sensor may collect data down to depths of about three times the Secchi depth. If the depth or the water turbidity is too great, acoustic echo-sounding is used for bathymetry and qualitatively characterizing seagrass beds (Hundley, Zabloudil, and Norall, 1994; Klemas, 2011b; Miner, 1993; Moreno, Siljestrom, and Rey, 1998; Morton and Miller, 2005; Sabol et al., 2002; Schmidt et al., 2011; Spratt, 1989). Figure 2 shows a successful application of LIDAR in a restoration project at the Blackwater National Wildlife Refuge. The refuge has been featured prominently in studies of the impact of sea level rise on coastal wetlands. The marsh is less than 1 m above sea level, and most of it has been breached and is being drowned. The severe loss of about 130 acres per year is caused by sea level rise, damage by geese and nutria, severely altered hydrology and salinity, and an increase in wave energy due to greater stretches of open water (Larsen et al., 2004).


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Without intervention, by the next century the entire refuge will be submerged. The U.S. Geological Survey (USGS) has developed an inundation model centered on the refuge. LIDAR mapping of land and shallow water surfaces has provided the model with the detailed topographical maps upon which future sea level positions can be superposed. Recent runs of the model suggest that through a combination of public and private conservation efforts, the wetland habitat could be sustained at least for the next 50 years (Larsen et al., 2004). Limited spatial resolution has been a problem in wetland studies, resulting in too many mixed pixels. Another problem has been the complexity of image-processing procedures that are required before hyperspectral data can be used for automated classification of wetland vegetation. Furthermore, the tremendous volume of hyperspectral image data necessitates the use of specific software packages, large data storage capacity, and extended processing time (Hirano, Madden, and Welch, 2003). A step-by-step guide for remotely sensing coastal wetlands and adjacent land cover change is provided in reports by the National Oceanic and Atmospheric Administration (NOAA) Coastwatch Project (Dobson et al., 1995).

IDENTIFYING RESTORATION SITES Wetland losses are mainly due to dredging, filling, impoundments, drainage, and other natural or man-made processes. Wetlands may be degraded by pollution, hydrologic alteration, and removal of vegetation buffers. Identifying potential restoration sites and prioritizing them using ecological and economic criteria is by no means a simple task (Russell, Hawkins, and O’Neill, 2004; Thayer, 1992; White and Fennessy, 2005). The specific objectives of every restoration project can be different. The selection criteria for restoring a critical animal habitat may differ significantly from a project to attract more tourists to a site. However, broadly speaking, most of the potential sites fall into one of these categories: (1) Drained former wetlands (e.g. farmed wetlands, mosquito ditching) (2) Impoundments (former vegetated wetlands) (3) Tidally restricted wetlands (4) Impounded wetlands (former vegetated wetlands) (5) Ditched palustrine wetlands (6) Excavated wetlands (7) Wetlands fragmented by roads and developments The selection of sites and planning their restoration involves the review of historical documents (aerial photos and literature), field investigations, and aerial or satellite remote sensing data. Field investigations include topographic, biological, geotechnical, hydrological, and archeological reviews of the prospective sites (Milano, 1999). This information also helps to protect existing natural and cultural resources and to identify the limits and details of restoration activities. All potential wetland restoration sites are prioritized by public ownership (to ensure long-term protection), habitat benefit considerations to the surrounding natural areas, site accessibility for heavy equipment, and cost effectiveness (Llewellyn et al., 1996). The biological assessment includes documenting existing onsite and surrounding biological communities, including exotic

species; identifying environmental concerns; and defining biological goals and construction activities. The hydrographic evaluation may include surveys of bathymetry, tidal regime, current velocities, and wave energy (Milano, 1999; Parkinson, 2003). Nearshore restoration projects frequently lack the required hydrodynamic information necessary for making informed decisions on modifications to the topography and hydrology. The geotechnical evaluation may use a network of fixed stations along transects to determine subsurface soil characteristics by such techniques as excavation of test pits, soil borings, ground penetrating radar, and electronic conductivity. The detailed soil characteristics (type, grain size, distribution, color, etc.) not only help define the wetland characteristics, but are also useful in developing a soil disposal strategy, since the reestablishment of altered historical wetlands typically involves the excavation, removal, and disposal of large amounts of fill. A topographic survey involves transferring elevation information from fixed benchmarks to a network of on-site stations, or by aerial photogrammetric or LIDAR mapping. In a photogrammetric survey an area is mapped by viewing overlapping aerial photos through stereoscopes and digitizing contours over the three-dimensional image. This technique is particularly cost effective at large sites that have only sparse vegetation. Sites that may contain historical archeological artifacts must usually be inspected by an archeologist prior to clearing activities. Federal, state, and local permits are required for all restoration work (Milano, 1999). Despite the growing use of remote sensing for wetland inventory and monitoring, there has been limited use of this technology for restoring wetlands (Phinn, Stow, and Van Mouwerik, 1999; Hinkle and Mitsch, 2005). Furthermore, there is a growing consensus about the need to examine restoration projects at the landscape scale and to develop landscape-based tools for monitoring restoration progress (Simenstad, Reed, and Ford, 2006; Tuxen et al., 2008). Remote sensing is ideal for monitoring restored wetlands because it can provide high spatial and temporal resolution at landscape scales. It also allows for measurements in inaccessible and sensitive sites, without the potential invasiveness that traditional field methods present to delicate habitat conditions, bird nesting areas, or endangered species habitat (Shuman and Ambrose, 2003). Thus remote sensing allows for broad-scale estimation of many parameters valuable to ecologists, including land cover, vegetation structure, biophysical characteristics, and habitat areas (Higinbotham, Alber, and Chalmers, 2004; Thomson et al., 2003; Wulder et al., 2004). As described in the previous section, medium-resolution satellite sensors, such as Landsat TM, can provide land cover information for large coastal watersheds. At 20–30 m resolution they can map entire watersheds, including plant cover and hydrology, and provide a broader view of ecological patterns and processes (Ozesmi and Bauer, 2002; Ramsey, 1995; Ramsey and Rangoonwala, 2010). Within this broad view one can switch to high-resolution airborne or satellite imagery to study specific, critical wetland sites and, with additional layers of data (e.g. topography, hydrology, soil type) in a GIS, select the sites most suitable for restoration or enhancement. High-



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Figure 3. Images showing vegetation, inundation, and hydrologic changes at the MNCA site between 1999 and 2001. On the left is an AISA hyperspectral image of 1-m resolution obtained on 18 September 1999. On the right is an IKONOS satellite image of merged 1-m resolution captured on 24 August 2001. Modified and reproduced with permission from Field and Philipp (2000a).

resolution multispectral or hyperspectral imagery can identify plants and other land cover types. Airborne LIDAR can map the topography/bathymetry, while SAR can help outline the hydrologic network (Kelly and Tuxen, 2009; Klemas, 2011b; Lillycrop, Pope, and Wozencraft, 2002). An interesting example of using remotely sensed imagery to identify a potential restoration site is the study of the Milford Neck Conservation Area (MNCA), which is located along the southwestern shore of Delaware Bay (Field and Philipp, 2011a, 2011b). Historical aircraft photographs and more recent aerial and satellite images, some obtained at different seasons, were used to examine environmental changes that have taken place for over 60 years at this dynamic site. The complex, dynamic landscape of this site is characterized by a transgressing shoreline, extensive tidal wetlands, island hammocks, and upland forests. A canal (Greco’s Canal) separates the site from a narrow barrier beach along Delaware Bay. The barrier beach of the Milford Neck Conservation Area was breached during the winter of 1985–86, making a direct connection between Delaware Bay and Greco’s Canal. The breach through the barrier beach resulted in a much shorter and direct linkage of the marsh to the tidal forcing of Delaware Bay. This has

changed the tide regimes experienced in the various sections of the marsh and the resulting patterns of tide marsh vegetation. These changes in the shoreline and tidal marsh have produced a dramatic habitat conversion and loss that may have significant immediate and long-term impacts on the biological resources and ecological integrity of the MNCA (Field and Philipp, 2000a). In Figure 3, airborne hyperspectral (AISA) imagery and IKONOS satellite imagery is used to illustrate that in just 2 years, from 1999 to 2001, the areas of open water plus scoured mud banks increased by about 50% as a result of the increased tidal flushing after the canal breach. Since the canal breach allowed tidal waters to flow directly into the marshes, the average width of some major creeks changed from 5.1 to 7.3 m, and the bank widths affected by tidal scouring increased from about 9.1 to 16.2 m (Field and Philipp, 2000a). The improved understanding of the processes occurring at this rapidly changing site is helping wetland managers decide whether to intervene in the hydraulic regime by channel modification in order to accelerate or delay marsh development in a particular direction.


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In 2004 NOAA’s National Estuarine Research Reserve System (NERRS) Program funded a team of remote sensing experts to compare the cost, accuracy, reliability, and userfriendliness of four remote sensing approaches for mapping land cover, emergent wetlands, and submerged aquatic vegetation (Porter et al., 2006). Four NERRS test sites were selected for the project, including the Ashapoo, Combahee, Edisto (ACE) basin, South Carolina; Grand Bay, Michigan; St. Jones River and Blackbird Creek, Delaware; and Padilla Bay, Washington. The four remote sensing systems evaluated include the hyperspectral Airborne Imaging Spectrometer for Applications (AISA), aerial multispectral (ADS-40) Digital Modular Camera, IKONOS (or QuickBird) high-resolution satellite, and Landsat TM. In the NERRS study, the highest accuracy for mapping clusters of different plant species over small critical areas was obtained by visually analyzing orthophotos produced by airborne digital cameras. The visual interpretation was performed after image segmentation and with the help of field training sites visited before and after the interpretation process. For larger sites, combining IKONOS and Landsat TM proved cost effective and user friendly, when the Landsat TM imagery was used to map land cover for the large site or entire watershed and the IKONOS high-resolution imagery was used for detailed mapping of critical NERRS areas or those identified by Landsat TM as having changed. A particularly effective technique developed by the team is based on using biomass change as a habitat change indicator (Klemas, 2011a; Porter et al., 2006). Some practical recommendations based on the NOAA/ NERRS are as follows: (1) The cost per square kilometer of imagery and its analysis rises very rapidly as one goes from medium-resolution to high-resolution imagery. Therefore, large wetland areas or entire watersheds should be mapped using mediumresolution sensors (e.g. Landsat TM at 30 m), and only small, critical areas should be examined with highresolution sensors (e.g. IKONOS at 1–4 m or airborne digital cameras). (2) Multispectral imagery should be used for most applications, with hyperspectral imagery reserved for difficult species identification cases, larger budgets, and highly experienced image analysts. (3) Airborne digital camera imagery is not only useful for mapping wetlands, but is also helpful in interpreting satellite images. (4) The combined use of LIDAR, radar, and multispectral/ hyperspectral imagery can improve the accuracy of emergent and submerged aquatic species discrimination and provide a better understanding of the topography/ bathymetry and hydrologic conditions. (5) High-resolution imagery is more sensitive to within-class spectral variance, making separation of spectrally mixed land cover types more difficult. Therefore, pixel-based techniques are sometimes replaced by object-based methods, which incorporate spatial neighborhood properties. In the object-based approach the image is segmented/partitioned into a series of closed objects that

coincide with the actual spatial pattern, and only then classified (Wang, Sousa, and Gong, 2004).

MONITORING RESTORATION SUCCESS It is critical to design and implement a monitoring approach that remains in place over the entire restoration, not just the early stages, to track progress and suggest corrective actions. Constant feedback on restoration performance is very important, if success is to be attained. In order to select the most costefficient remote sensing and field techniques, scientists must be familiar with the changes in plant cover, plant stress, hydrology, etc. that need to be monitored as the restoration progresses (Hinkle and Mitsch, 2005; Zedler, 1996). Success criteria for restoration projects are often based on ongoing planting survivability and habitat use by relevant animal species. Animal assessments at restored sites can be conducted by volunteer wildlife experts and even local school groups (Delphey and Dinsmore, 1993; Fletcher and Koford, 2003; Ratti et al., 2001). Planting survivability is often determined qualitatively using photo-stations and quantitatively using the fixed-quadrat and line-intercept methods within the restored wetlands. Even though success criteria differ for nearly every type of site, planting sites have been considered successful if they have overall planting survival rates in the range of 65% to 80%. Wetland restoration is designed to restore the functions and values of wetland ecosystems that have been altered or impacted through removal of vegetation, cropping, construction, filling, grading, and changes in water levels and drainage patterns. Processes occurring outside the wetland such as influx of sediments, fragmentation, loss of recharge area, or changes in local drainage patterns can also alter functions of wetlands. Thus the main goal of a wetland restoration is to attempt to restore the hydrology and vegetation back to their original condition and to ensure ecological integrity (Melesse et al., 2007; Zedler, 2000). However, in many cases original conditions may not be achievable. The actual restoration process may have involved removing exotic plants, removing bulkheads and fill, specific elevation grading, creating flushing channels, and planting native vegetation. In addition, unconsolidated shorelines may have been stabilized and enhanced with vegetation and protective barriers. For instance, the Northern Delaware Wetlands Rehabilitation Program seeks to achieve the following goals: to improve water quality, increase wildlife populations, control nuisance plants, control mosquitoes, control flooding, reduce shoreline erosion, and improve educational and recreational opportunities (DNREC, 1994). To improve water quality requires reestablishing marsh hydrology, including daily tidal exchange between marsh and river. Water control structures can be installed, permitting the tides to flush nutrients and organisms into and out of the marsh as well as increase the volume of water that can be cleansed by the wetland (Artigas and Yang, 2004). Pollution inputs to the wetland, conveyed during storms, must be controlled by implementing non–point source control plans. Wildlife populations can be increased by increasing the diversity of shallow water habitats (e.g. ponds, ditches,



islands), constructing duck and songbird boxes, establishing preferred food and cover plants, and adjusting water levels to accommodate the needs of aquatic mammals, water birds, and endangered species. Nuisance plants, such as Phragmites, can overtake a wetland, forming dense stands of little value to wildlife. However, since even Phragmites can provide suitable habitat for some species, especially if it is interspersed among other plants, the goal might be to control its spread rather than totally eradicate it (DNREC, 1994; Pengra, Johnston, and Loveland, 2007). Historically wetlands were drained using ditches to control mosquitoes. However, when the marsh was flooded by heavy rains or unusual tides, the wetland surface created a prime egglaying site for floodwater mosquitoes, permitting eggs to hatch and larvae to develop. Removal of ditches by filling or other methods is being considered as a restoration alternative in many wetlands. Since the average time for such ditches to naturally fill can take centuries, active filling of ditches is being attempted (Corman et al., 2012). To decrease the use of insecticides, the abundance of mosquito-eating fish and insects can be increased or their access to mosquito-breeding areas improved (Lathrop, Cole, and Showalter, 2000). Since wetlands soak up water during heavy rains, some of Delaware’s wetland rehabilitation efforts have focused on installing new water control structures to expedite floodwater removal without flooding adjacent areas (DNREC, 1994). Water levels were manipulated to facilitate vegetation changes in coastal lagoons undergoing partial tidal restoration. Oneway tide gates were implemented that let high tides into the lagoons, while blocking their escape. The resulting increased flooding of the marsh raised porewater salinities and resulted in decreases in the cover of freshwater and brackish-water plants, helping establish and expand native halophytes (Artigas and Young, 2004; DNREC, 1994; Smith and Medeiros, 2012). Vegetation cover and groundwater changes over the period of restoration are perhaps the two most important indicators of the level of success in wetland eco-hydrological restoration (Phinn, 1998; Tuxen et al., 2000). For more detailed monitoring, Natural Habitat Integrity Indices have been developed, including (1) (2) (3) (4) (5) (6) (7) (8) (9)

Natural Cover Index Stream Corridor Integrity Index Wetland and Other Water Body Buffer Index Wetland Extent Index Standing Water Body Extent Index Dammed Stream Flowage Index Channelized Stream Length Index Wetland Disturbance Index Index of Remotely Sensed Natural Habitat Integrity

Detailed definitions of these indices are provided in Tiner et al. (2001) and Lopez and Fennessy (2002).The indices can range from zero to one, with a pristine watershed having an index of 1.0 for natural habitat integrity. Even though remote sensing is mentioned only in some of these indices, it can provide valuable information on most of them, as discussed in

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an earlier section of this paper. For instance, planting sites, plant survival rates, and spread of nuisance plants can be monitored using high-resolution airborne or satellite imagers (Pengra, Johnston and Loveland, 2007; Phinn, Stow, and Zedler, 2006; Tiner et al., 2001). Restoration success assessments often target the quantity and quality of vegetation communities (Boyd and Davies, 2012). An Index of Ecosystem Integrity was used by Staszak and Armitage (2012) to determine whether estuarine emergent marsh restoration projects (Galveston, Texas) that had successfully achieved permit-mandated plant coverage were comparable to reference sites at an ecosystem level. They used a Rapid Assessment Method developed specifically for this habitat to compare restored (ages 5–15 years) and reference marshes. Thirteen biotic and abiotic characteristics were used to calculate an ecosystem index score, where a pristine habitat would score 100%. Restored marshes scored 75% as compared with 81% for reference marshes, which is typical for urbanized estuaries. Reference sites also had more epifauna, while restored sites had practically none. Furthermore, older restored sites had higher plant diversity and belowground plant biomass than younger restored sites (Staszak and Armitage, 2012). Some of the more common restoration assessment products include wetland trend studies, landscape-level functional assessments, and natural habitat indicators. For instance, changes in functions may include surface water detention, streamflow maintenance, nutrient transformation, sediment retention, shoreline stabilization, storm surge detention, fish/ shellfish habitat, waterfowl/waterbird habitat, and other wildlife habitat (Tiner, 2004). A wide range of remote sensors is available for monitoring restoration progress, including changes in wetlands extent and quality, wetland function, wetland and water body buffers, land use and land cover in watershed, extent of ditching, and water quality, such as turbidity and eutrophication (Phinn, Stow, and Van Mouwerik, 1999; Phinn, Stow, and Zedler, 2006; Selvam et al., 2003; Shuman and Ambrose, 2003; Tiner, 1996). To monitor long-term trends and short-term variations in restored wetlands, one needs to analyze time-series of remotely sensed imagery (Coppin et al., 2004; Klemas, 2011a; Morris et al., 2002; Purkis and Klemas, 2011). A traditional way has been to visually compare multidate images to identify improvements or losses in the restored wetlands. Nowadays computer-based change detection techniques are often being used. Performing computer-based change analysis between digital images is a difficult task, since the imagery must be acquired under similar environmental conditions (e.g. same time of year, sun angle, etc.) and in similar spectral bands. In the preprocessing of multidate images the most critical steps are the registration of the multidate images and their radiometric rectification. Registration accuracies of a fraction of a pixel must be attained. Detecting the actual changes between two corrected images from different dates can be accomplished by employing one of several techniques, including postclassification comparison and spectral image differencing (Houhoulis and Michener, 2000; Jensen, 1996). More research is needed to compare and improve the various change detection techniques, especially for complex coastal


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landscapes containing wetlands and submerged aquatic vegetation (Baker et al., 2007; Lunetta and Elvidge, 1998; Shalabi and Tateishi, 2007; Yuan, Elvidge, and Lunetta, 1998). Vegetation indices have often been used to monitor the extent and changes in wetland vegetation cover (Eastwood et al., 1997; Lyon et al., 1998). A study illustrating practical monitoring of a restored site by remote sensing has been described by Tuxen et al. (2008). In that study a semiautomated technique using color infrared aerial photography and the normalized difference vegetation index (NDVI) were used to document vegetation colonization in a restoring salt marsh. Changes in vegetation over a period of 10 years was analyzed using a postclassification comparison change detection technique where each image year was classified individually into vegetated and nonvegetated areas using NDVI thresholds and then differenced between years to identify areas of vegetation change. Periods of vegetation change were also identified. A comparison of the classified NDVI imagery determined that 90% of the study site was vegetated 10 years after restoration. The study demonstrated that high-resolution remotely sensed data can be analyzed with common geospatial software to monitor change in a rapidly vegetating wetland and that long time frames with yearly image acquisition are needed to quantify plant colonization rates (Tuxen et al., 2008). This method was effective even using imagery that had inconsistent specifications and quality across years. Inconsistencies included interannual climate variations, phenology, and presence of algae. Differences in pixel size and image brightness had to be corrected and adjusted. Nonetheless, the results clearly show that even relatively simple remote sensing techniques can be used for postrestoration monitoring of tidal marsh ecosystems. NOAA has compiled a comprehensive manual on how to plan and conduct the monitoring of coastal habitat restoration projects. The manual provides means for detecting early warnings that the restoration is not ‘‘on track,’’ to gauge how well a restoration site is functioning, to evaluate ecological status before and after project completion, and to coordinate projects and efforts for consistent, successful restoration (NOAA, 2010).

SUMMARY AND CONCLUSIONS Coastal and estuarine wetlands represent highly productive and critical habitats for a wide variety of plants and animals. Wetlands also provide protection from floods, storm surge, and wave damage; water quality improvement through filtering of agricultural and industrial waste; and recharge of aquifers. However, over the past 200 years, wetland acreage in the continental United States has steadily decreased from about 200 million acres to an estimated 95 million acres, mainly as a result of human activities and, more recently, sea level rise. Major efforts are being made by federal, state, and local agencies to protect existing wetlands, restore lost wetlands, and improve those stressed by human activities. Wetland restoration is designed to restore the functions and values of wetland ecosystems that have been altered or impacted through removal of vegetation, cropping, construction, filling, grading, and changes in water levels and drainage patterns.

Remote sensing offers cost-effective means for selecting sites that are suitable for restoration and for monitoring their progress after the first phase of restoration is completed. A wide range of remote sensors is available for detecting changes in wetland extent and quality, wetland function, wetland and water body buffers, land use and land cover in watersheds, extent of ditching, and water quality (turbidity, eutrophication). Environmental indicators that can be detected by remote sensors are available to provide quantitative estimates of coastal and estuarine habitat conditions and trends. Such indicators include percentage of impervious watershed area, natural vegetation cover, buffer degradation, wetland loss and fragmentation, wetland biomass change, invasive species, water turbidity, chlorophyll concentration, and eutrophication. Some of the changes that are difficult to detect by remote sensing include hydrologic alteration from groundwater withdrawal, diversions, and tile drainage. Furthermore, remote sensors have difficulty detecting chemical contamination, certain water pollutants, and some invasive species. Some of the more common restoration assessment products include wetland trend studies, landscape-level functional assessments, and natural habitat indicators. For instance, changes in wetland functions may include surface water detention, stream-flow maintenance, nutrient transformation, sediment retention, shoreline stabilization, storm surge detention, fish/shellfish habitat, waterfowl/water bird habitat, and other wildlife habitat. Limited spatial resolution has been a problem in wetland studies, resulting in too many mixed pixels. Another problem has been the complexity of image-processing procedures that are required before hyperspectral data can be used for automated classification of wetland vegetation. The tremendous volume of hyperspectral image data necessitates the use of specific software packages, large data storage capacity, and extended processing time. Future research priorities should include better understanding and description of ecosystem characteristics, the functional interpretation of wetland maps, and the radiative properties of coastal environments. Additional knowledge is required about the spatial and temporal variations of water column optical properties and their constituents. Best approaches for processing hyperspectral data need to be further investigated, and hyperspectral sensors need to be tested for bottom type discrimination using data obtained from satellites. Finally there is a need to investigate improvements to be gained from synergistic use of multiwavelength remote sensing approaches, change detection techniques, multitemporal comparisons, and knowledge-based approaches for improving classification accuracy (Malthus and Mumby, 2003). In sum, we can conclude that when remote sensing techniques are used wisely, including complementary combinations of different satellite and airborne sensors, they can provide data that enhance the research, management, and restoration of coastal ecosystems. Remote sensors used in wetland restoration projects can monitor and assess long-term trends and short-term changes of vegetation and hydrology faster, more completely, and at lower cost per unit area than field surveys alone.



ACKNOWLEDGMENTS This research was supported in part by the NOAA Sea Grant and by the NASA EPSCoR Programs at the University of Delaware.

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