Remote Sensing of Coastal Wetland Biomass: An

Journal of Coastal Research

29

5

1016–1028

Coconut Creek, Florida

September 2013

Remote Sensing of Coastal Wetland Biomass: An Overview Victor Klemas School of Marine Science and Policy University of Delaware Newark, DE 19716, U.S.A.

ABSTRACT Klemas, V., 2013. Remote sensing of coastal wetland biomass: an overview. Journal of Coastal Research, 29(5), 1016– 1028. Coconut Creek (Florida), ISSN 0749-0208. Wetlands are highly productive and provide critical habitat to animals and plants. Coastal wetlands have been affected by human-created alterations and, more recently, by losses due to sea-level rise. To protect and restore tidal wetlands, scientists need to monitor the changes in the wetlands as the sea level continues to rise and the coastal population keeps expanding. Advances in sensor design and data-analysis techniques are making remote-sensing systems practical and cost effective for obtaining quantitative biophysical information, such as wetland extent, live aerial biomass, leaf area index, percentage of canopy closure, etc. This article reviews biomass mapping techniques and illustrates their use for wetland-change detection. The results show that analysis of satellite and aircraft data, combined with a small number of on-the-ground observations, allows researchers to map biomass and determine long-term changes in tidal marshes.

ADDITIONAL INDEX WORDS:

Biomass mapping, biomass change detection, coastal remote sensing, wetland

productivity, wetland stress.

INTRODUCTION Wetlands are transitional zones between terrestrial and aquatic ecosystems which experience seasonal or permanent above surface water (Dillabaugh and King, 2008). Their soils are saturated and hydrophytic vegetation predominates. Wetlands and estuaries are highly productive and act as critical habitats for a wide variety of plants, fish, shellfish, and other wildlife. There is a global recognition of coastal wetlands’ critical role as a carbon sink because of extremely high soil Csequestration amounts. Wetlands also provide protection from storm flooding, water quality improvement through filtering of agricultural and industrial waste, and recharge of aquifers (Eleuterius, 1990; Mitch and Gosselink, 2007; Odum, 1993). However, wetlands have been exposed to a wide range of stressinducing alterations, including dredge and fill operations, hydrologic modifications, pollutant runoff, eutrophication, impoundments, and fragmentation by roads and ditches (Williams, 1990). Invasive wetland plants, such as Phragmites on the U.S. East Coast, are also threatening coastal wetlands. One major concern is the ability of wetlands to adapt to sealevel rise (Morris et al., 2002; Purkis and Klemas, 2011; Thorne, Takekava, and Elliott-Fisk, 2012). Vegetated wetlands are stable only when the marsh platform is able to accrete sediment at a rate equal to the prevailing rate of sea-level rise. This ability to accrete is proportional to the biomass density of the plants, concentration of suspended sediment, time of submergence and the depth of the marsh surface, and the tidal range (Kirwan et al., 2010). Many coastal wetlands, such as the tidal salt marshes along the Louisiana coast, are within DOI: 10.2112/JCOASTRES-D-12-00237.1 received 15 November 2012; accepted in revision 28 December 2012; corrected proofs received 12 February 2013. Published Pre-print online 14 March 2013. Ó Coastal Education & Research Foundation 2013

fractions of a meter of sea level and will be lost, especially if the impact of sea-level rise is amplified by coastal storms (Farris, 2005; Klemas, 2009; Lam et al., 2011; McInnes et al., 2003; Morton and Barras, 2011; Ramsey and Rangoowala, 2010; Ramsey et al., 2011). Constructed modifications of wetland hydrology and extensive urban development will further limit the ability of wetlands to survive sea-level rise. For instance, constructed channelization of the Mississippi River flow causes much of the river sediment to be carried into the Gulf, rather than being deposited in the wetlands along the Louisiana coast (Kim et al., 2009; Pinet, 2009). Knowledge of the amount of vegetation biomass is needed for simulating local carbon budgets and is particularly important for estimating autotrophic respiration. The mass of living material is often used to initialize vegetation carbon pools for regional model simulation (Turner, Ollinger, and Kimball, 2004). Biomass changes can be indicative of vegetation stress induced by natural and human-caused disturbances. To plan for wetland protection and sensible coastal development, scientists and managers need to monitor changes in the biochemical and biophysical properties of coastal ecosystems, including wetland biomass. Improved new sensors and data analysis techniques are available that make remote sensing attractive for monitoring natural and constructed coastal ecosystem changes. Highresolution, multispectral and hyperspectral imagers, Light Detection and Ranging (LIDAR) and radar are available for mapping changes to wetland extent, species composition, and biomass. Even Unmanned Aerial Vehicles (UAVs) are being used to map wetlands (Lechner et al., 2012). With the rapid development of new remote sensors, databases, and image analysis techniques, there is a need to help potential users choose remote sensors and data analysis methods that are most appropriate and cost effective for wetland studies (Lang and

Remote Sensing of Coastal Wetland Biomass

McCarty, 2008; Thenkabail, Lyon, and Huete, 2012). The objective of this article is to review wetland remote-sensing techniques for mapping wetland biomass and the use of aboveground biomass (AGB) for determining wetland changes.

COASTAL WETLAND PRODUCTIVITY AND BIOMASS Salt marshes rank among the most productive ecosystems in the world. Their primary production can be as high as 3000 g C/ m2/y, exceeding the productivity of some agricultural crops (Campbell et al., 2000; Kelly and Tuxen, 2009; Odum, 1993). Smooth cordgrass (Spartina alterniflora) is the dominant macrophytic plant in most salt marshes along the U.S. east coast. The stiff, leafy grass can grow in either tall or short forms in different parts of the marsh. The tall form ranges in height from 100 to 250 cm and usually occurs adjacent to tidal creeks and in very low portions of the intertidal marsh. The short form ranges in height from 17 to 80 cm and is found further away from the creeks and usually in the upper marsh (Gallagher et al., 1988; Jensen et al., 2002; Mitch and Gosselink, 1986). The main biochemical constituents in wetland vegetation are nitrogen, plant pigment, and water, whereas the biophysical properties of plants include the leaf-area index (LAI), canopy architecture and density, and biomass. Estimating the biochemical and biophysical properties of wetland vegetation is important for monitoring the dynamics of plant productivity, carbon cycling, and nutrient allocation within wetland ecosystems (Adam, Mutanga, and Rugege, 2010; Asner, 1998; Mutanga and Skidmore, 2004). Biomass concentration is one of the most important biophysical properties that characterizes wetland species and is necessary for studies of plant productivity and stress. In situ, AGB measurements are typically obtained by clipping all of the plants within a certain geographic quadrat (e.g. 1 3 1 m), and then drying and weighing the material to compute biomass in grams per square-meter. Traditional LAI measurement techniques, such as hemispherical photography and quantum sensors, do not work well in a grassland environment. To perform in situ LAI measurements, leaf samples within a quadrat must be collected, taken back to the laboratory, have all leaf perimeters digitized, and the amount of one-sided leaf area in the quadrat must be computed per unit of ground surface area (m2/m2). Such data acquisition is very time consuming, tedious, and expensive (Jensen et al., 2002; McCoy, 2005).

REMOTE SENSING OF COASTAL WETLANDS For more than four decades, remote-sensing techniques have been used successfully to map and monitor wetlands (Dahl, 2006). For instance, the U.S. Fish and Wildlife Service (FWS) has used remote-sensing techniques to determine the biological extent of wetlands for the past 30 years. Through its National Wetlands Inventory, FWS has provided federal and state agencies, the private sector, and citizens with scientific data on wetland location, extent, status, and trends. To accomplish this important task, FWS has used multiple sources of aircraft and satellite imagery and on-the-ground observations (Tiner,

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1996). Most states have also conducted wetland inventories, using both aircraft and satellite imagery. Because wetlands are spatially complex and temporally quite variable, mapping their vegetation often requires high-resolution satellite or aircraft imagery and, in some cases, hyperspectral data. A cost-effective approach is to map large areas with the Landsat Thematic Mapper (TM) and focus in on critical or rapidly changing sites with high-resolution sensors (Jensen, 2007; Klemas, 2011). The Landsat TM has been the traditional sensor for vegetation and land-cover mapping (Cohen and Goward, 2004; Lunetta and Balogh, 1999; Ozesmi and Bauer, 2002). Its 30-m resolution and spectral bands have proven adequate for observing land-cover changes in large coastal watersheds (e.g. Chesapeake Bay). The multispectral bands of SPOT (Systeme Pour L’Observation de la Terre) have also been used to measure the characteristics of various coastal wetlands, including mangroves (Jensen et al., 1991). In a typical, digital-image analysis for classifying coastal wetlands, the Landsat TM multispectral imagery must first be radiometrically and geometrically corrected. After all corrections, supervised and unsupervised classification is usually performed (Jensen, 1996). Training-site spectral clusters and unsupervised spectral classes are then compared and analyzed using cluster analysis to develop an optimum set of spectral signatures. Final image classification is then performed to match the classified themes with the project requirements (Jensen, 1996). Data from other sensors can improve classification accuracy. For instance, synthetic aperture radar (SAR) can help distinguish upland forests from forested wetlands (Ramsey, 1995). In many developing countries, mangrove swamps are being cut down to provide firewood or building material and are being destroyed by the development of shrimp ponds. A good example of mapping the remaining mangroves using Landsat TM is the U.S. Geological Survey/National Aeronautics and Space Administration (USGS/NASA) project to determine mangrove cover on a global scale (Giri et al., 2010). The satellite imagery, with a spatial resolution of 30 m, was used to produce the most comprehensive and exact data on the extent, distribution, and decline of mangrove forests across the world. More than 1000 Landsat scenes were analyzed using hybrid supervised and unsupervised digital image classification techniques. The time series of the imagery revealed that, in the year 2000, only 137,760 km2 of mangroves still existed. This meant that the remaining area of mangrove forest in the world was less than previously thought and 12.3% smaller than the estimate by the United Nations (U.N.) Food and Agriculture Organization. Mangrove forests have also been mapped using hyperspectral, radar, and object-based image analysis (OBIA) techniques (Flores De Santiago, 2013; Kamal and Phinn, 2011) Linear spectral mixture analysis has also been used to unmix TM images into fraction images, which were used for classifying major wetland-related land covers with a thresholding technique (Zhang et al., 2011). The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method. Expert rules were then used to modify the classified image. The results indicated that this hybrid approach employing subpixel information, an analyst’s knowledge, and characteristics of

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coastal wetland vegetation distribution can be used to successfully distinguish coastal vegetation classes, which are difficult to separate with a maximum-likelihood classifier (Zhang et al., 2011). When studying small wetland sites, aircraft or highresolution satellite systems are used. Airborne, georeferenced digital cameras, providing color and color-infrared digital imagery are particularly suitable for accurate mapping of wetlands (Ellis and Dodd, 2000). Compared with aerial film cameras, large-format digital cameras offer technical and economic advantages. Digital cameras provide simultaneous true and false color, better radiometry, absence of grain noise, faster image repeat cycles, accurate geometry, better automated procedures, instant quality control during image acquisition, and better stereo quality (Leberl et al., 2002). Usually, savings on film, photograph processing, and scanning cover the cost of the camera. Digital camera imagery can be integrated with global positioning system (GPS) position information and used as layers in a geographical information system (GIS) for a wide range of modeling applications (Lyon and McCarthy, 1995). Small aircraft flown at low altitudes (e.g. 200–500 m) can be used to guide and supplement field data collection. Highresolution imagery (0.6–4 m) can also be obtained from satellites, such as IKONOS and QuickBird. However, cost becomes excessive if the site is larger than a few hundred square-kilometers, and in that case, medium-resolution sensors, such as Landsat TM (30 m) and SPOT (20 m), become more cost effective (Klemas, 2011). The recent availability of high spatial and spectral resolution satellite data has significantly improved the capacity for mapping upstream wetlands and tidal salt marshes (Ustin et al., 2004; Wang, Christiano, and Traber, 2010). QuickBird high-resolution imagery has been used successfully for mapping invasive wetland plants (Ghioca-Robrecht, Johnston, and Tulbure, 2008; Laba et al., 2008). Furthermore, a number of advanced new techniques have been developed for identifying wetland types and plant species (Filippi and Jensen, 2006; Lang and McCarty, 2008; Schmidt and Skidmore, 2003; Schmidt et al., 2004; Yang et al., 2009). For instance, using LIDAR, hyperspectral and radar imagery, and narrow-band vegetation indices, researchers have been able to discriminate some wetland species and make progress on estimating biochemical and biophysical parameters of wetland vegetation, such as water content, biomass, and LAI (Adam, Mutanga, and Rugege, 2010; Gilmore et al., 2010; Lefsky et al., 2002; Li, Ustin, and Lay, 2005; Pengra, Johnston, and Loveland, 2007; Rosso, Ustin, and Hastings, 2005; Simard, Fatoyinbo, and Pinto, 2010; Ustin et al., 2004; Wang, 2010; Zhang et al., 2003). High-resolution imagery is more sensitive to within-class spectral variance, making separation of spectrally mixed land cover types more difficult than when using medium-resolution imagery. Therefore, pixel-based analysis techniques are sometimes replaced by OBIA, which incorporates spatial neighborhood properties, by segmenting/partitioning the image into a series of closed objects that coincide with the actual spatial pattern and then proceeding to classify the image. The choice of OBIA depends on the pixel size as compared with the size of the object of interest. ‘‘Region growing’’ is among the most

commonly used segmentation methods. This procedure starts with the generation of seed points over the whole scene, followed by grouping neighboring pixels into an object under a specific homogeneity criterion. Thus, the object keeps growing until its spectral closeness metric exceeds a predefined breakoff value (Kelly and Tuxen, 2009; Shan and Hussain, 2010; Wang, Sousa, and Gong, 2004). Airborne hyperspectral imagers, such as the Advanced Visible Infrared Imaging Spectrometer (AVIRIS) and the Compact Airborne Spectrographic Imager (CASI), have been used for mapping coastal wetlands (Jensen et al, 2007; Li, Ustin, and Lay, 2005; Ozesmi and Bauer, 2002; Rosso, Ustin, and Hastings, 2005; Schmidt and Skidmore, 2003). The advantages and problems associated with hyperspectral mapping have been clearly demonstrated by Hirano et al. (2003) who used AVIRIS hyperspectral data to map vegetation for a portion of the Everglades National Park in Florida. The AVIRIS provides 224 spectral bands from 0.4 to 2.45 lm, each with 0.01-lm bandwidth, 20 m spatial resolution, and a swath width of 10.5 km. A comparison of the geographic locations of spectrally pure pixels in the AVIRIS image with dominant vegetation polygons of the Everglades Vegetation Database identified spectrally pure pixels as 10 different vegetation classes plus water and mud. An adequate number of pure pixels were identified to permit the selection of training samples used in the automated classification procedure. The spectral signatures from the training samples were then matched to the spectral signatures of each individual pixel. Image classification was undertaken using the ENVI spectral angle mapper (SAM) classifier in conjunction with the spectral library created for the Everglades study area. The SAM classifier examines the digital numbers (DNs) of all bands from each pixel in the AVIRIS data set to determine similarity between the angular direction of the spectral signature (i.e. color) of the image pixel and that of a specific class in the spectral library. A coincident or small spectral angle between the vector for the unknown pixel and that for a vegetation-class training sample indicates that the image pixel likely belongs to that vegetation class. In the case of spectrally mixed pixels, the relative probability of membership (based on the spectral angle) to all vegetation classes is calculated. Mixed pixels are then assigned to the class of the greatest probability of membership (Hirano et al., 2003). The hyperspectral data proved effective in discriminating spectral differences among major Everglades plants, such as red, black, and white mangrove communities, and enabled the detection of exotic invasive species (Hirano et al., 2003). The overall classification accuracy for all vegetation pixels was 65.7%, with different mangrove tree species ranging from 73.5% to 95.7% correct. Limited spatial resolution was a problem, resulting in too many mixed pixels. Another problem was the complexity of the image-processing procedures required before the hyperspectral data can be used for automated classification of wetland vegetation. The integration of hyperspectral imagery and LIDARderived elevation data has also significantly improved the accuracy of mapping salt marsh vegetation. The hyperspectral images help distinguish high marsh from other salt marsh communities because of its high reflectance in the near-



infrared (NIR) region of the spectrum, and the LIDAR data help separate invasive Phragmites from low marsh plants (Artigas and Yang, 2006; Yang and Artigas, 2010). Major plant species within a complex, heterogeneous tidal marsh have been classified using multitemporal high-resolution QuickBird images, field reflectance spectra, and LIDAR height information. Phragmites australis, Typha spp., and Spartina patens were spectrally distinguishable at particular times of the year, likely because of differences in biomass and pigments and the rate at which those change throughout the growing season. Classification accuracies for Phragmites were high because of the uniquely high NIR reflectance and the plant’s height in the early fall (Gilmore et al., 2008, 2010). Submerged aquatic vegetation (SAV) is an important part of wetland and coastal ecosystems, playing a major role in the ecological functions of those habitats (Silva et al., 2008). Seagrass beds provide essential habitat for many aquatic species, stabilize and enrich sediments, dissipate turbulence, reduce current flow, cycle nutrients, and improve water quality. However, in many parts of the world, the health and quantity of seagrass beds have been declining (Hughes et al., 2009; Orth et al., 2006). The declines have been attributed to human activity causing reduction in water clarity, alteration of sediment migration via dredging, and destruction from coastal engineering, boating, and commercial fishing. Coral reef ecosystems usually exist in clear water and can be classified to show different forms of coral reef, dead coral, coral rubble, algal cover, sand, lagoons, and different densities of seagrasses, etc. SAV often grows in somewhat turbid water and, thus, is more difficult to map (Silva et al., 2008). Seagrass habitats have traditionally been mapped with airborne film and digital cameras (Lathrop, Montesano, and Haag, 2006). Large SAV beds and other benthic habitats have been mapped using Landsat TM with limited accuracies ranging from 60% to 74%. (Ferguson and Korfmacher, 1997; Schweitzer, Armstrong, and Posada, 2005). SAV biomass has been mapped with Landsat TM using regression analysis. (Armstrong, 1993; Zhang, 2010). The mapping of SAV, coral reefs, and general bottom characteristics from satellites has become more accurate because high-resolution (0.64 m), multispectral imagery is available (Mumby and Edwards, 2002; Purkis et al., 2002; Purkis and Klemas, 2011). Aerial hyperspectral scanners and high-resolution, multispectral satellites, such as IKONOS and QuickBird, have been used to map SAV with accuracies of about 75% for classes that include high-density seagrass, lowdensity seagrass, and unvegetated bottoms (Dierssen et al., 2003; Maeder et al., 2002; Mishra et al., 2006; Purkis, 2005). Hyperspectral imagers have improved the SAV and coral reef mapping results by being able to identify additional estuarine and intertidal habitat classes (Akins, Wang, and Zhou, 2010; Garono et al., 2004; Louchard et al., 2003; Pu et al., 2012; Underwood et al., 2006). Airborne LIDARs have been used with multispectral and hyperspectral imagers to map coral reefs and SAV (Brock et al., 2006; Brock and Purkis, 2009). Protocols have been developed for hyperspectral remote sensing of submerged aquatic vegetation in shallow waters (Bostater and Santoleri, 2004).

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Figure 1. U.S. biomass map produced from the National Biomass and Carbon Dataset (NBCD) and released in 2011. The darkest greens indicate areas with the densest, tallest, and most-robust forest growth (Grieser et al., 2011). Reprinted with permission from the Woods Hole Research Center.

REMOTE SENSING OF FOREST AND WETLAND BIOMASS Because of the global and regional importance of wetlands, additional quantitative, biophysical information is required by scientists, including data on changes in biomass, LAI, percentage of canopy closure, etc. (Campbell et al., 2000; Gower, Kucharik, and Norman, 1999; Jensen et al., 1998; Running et al., 2004). Biomass of selected plant species, especially grasses, has been shown to be a good indicator of species richness and can be obtained from remotely sensed imagery (Anderson, Hanson, and Haas, 1993; Gough, Grace, and Taylor, 1994; Jensen et al., 1998; Moore and Keddy, 1989; Zhang et al., 1997) . Visible, infrared, and microwave wavelengths have varying sensitivities to aboveground vegetation biomass. Optical and radar remote-sensing methods, empirical and statistical regression models, some containing the Normalized Difference Vegetation Index (NDVI), have been used to estimate the amount and temporal variability of AGB (Dong et al., 2003; Gower, Kucharik, and Norman, 1999; Ku et al., 2012; Peregon et al., 2008; Riegel, 2012). Remote sensing has been applied to collect large amounts of biomass data on a global scale for forested areas, such as upland forests, forested wetlands, and mangroves (Running et al., 2004). Extensive work is underway with L-band SAR to develop operational biomass programs across a range of countries, using an approach that is effective over a range of 0–200 t/ha (Lucas et al., 2010). The Woods Hole Research Center, working with the U.S. Forest Service and the U.S. Geological Survey, has created a National Biomass and Carbon Dataset (NBCD), released in 2011. Figure 1, obtained from the NBCD, depicts the concentration of biomass stored in the trunks, limbs, and leaves of trees across the continental United States. The darkest greens reveal the areas of the densest, tallest, and most-robust forest growth (Kellndorfer et al., 2012). For 6 years, researchers assembled the national forest map from space-based radar (SRTM), satellite multispectral sensors (Landsat Enhanced Thematic Mapper Plus [ETMþ]), computer


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models, and extensive ground-based data. The forests in the United States were mapped at scales of 30 m or about 4 pixels per acre. Global maps of net primary production of biomass have been published by UN/FAO (Grieser et al., 2006). Baccini et al. (2008) used the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) in combination with a large number of field measurements to map woody AGB across tropical Africa. A regression-tree model was used to predict AGB at 1-km resolution. Results show that the model explained 82% of the variance in AGB, with a root mean square error (RMSE) of 50.5 Mg/ha for a range of biomass between 0 and 454 Mg/ha. The results also showed a strong positive correlation of R2 ¼ 0.90 between airborne LIDAR height metrics and predicted AGB (Baccini et al., 2008). When mapping and monitoring forest and wetland ecosystems, imaging radars have some advantages over sensors that operate in the visible and infrared portions of the electromagnetic spectrum. SAR is sensitive to vegetation structure and to the amount of biomass (Baghdadi et al., 2001; Kasischke et al., 1994; Rignot et al., 1994; Townsend, 2002). Microwave wavelengths penetrate to greater depths in plant canopies than do optical sensors and show more promise for assessing standing woody biomass, such as found in forests or forested wetlands. SAR is not attenuated by atmospheric effects and, depending on frequency, polarization, and viewing angle, SAR backscatter responds to multiple structural elements of forests and forested wetlands (Hess and Melack, 1994; Hess, Melack, and Simonett, 1990; Lang and McCarty, 2008; Townsend, 2001, 2002). Radar sensitivity to vegetation biomass depends strongly on wavelength, with longer wavelengths (L-band) able to detect greater vegetation volumes and biomass levels than shorter wavelengths (C-band) can (Dobson et al., 1992; Turner, Ollinger, and Kimball, 2004). SAR has also been used to map inundation conditions, hydrologic networks, and to estimate vegetation biomass, especially in areas of frequent cloud cover (Hess et al., 2003; Lu et al., 2005; Kasischke, Melack, and Dobson, 1997). Thus, SAR imagery has offered a valuable supplement to multispectral image data in studies of wetland ecology (Pope, Rey-Benayas, and Paris, 1994; Ramsey, 1995; Ye et al., 2010). As shown in Figure 2, forest height and biomass have been mapped using LIDAR and SAR by Sun et al. (2011) in Maine. LIDAR can measure the height of the scatterers in its footprint and can yield accurate information on the vertical profile of the canopy within the footprint samples (Ku et al., 2012). SAR senses the canopy volume and provides image data. Sun et al. (2011) used a stepwise regression to select height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with an R2 of 0.71 and an RMSE of 31.33 Mg/ha. The biomass map generated from the regression model was used as a reference map from which random samples were taken and the correlation between the sampled biomass and co-located SAR signature was studied. The models were used to extend the biomass from LIDAR samples into all forested areas. The SAR data can predict the LIDAR biomass samples with an R2 of 0.63–0.71 and an RMSE of 32.0–28.2 Mg/ha up to biomass levels of 200–250 Mg/ha. The mean biomass calculated from the biomass maps generated by

Figure 2. Biomass map using LVIS height indices from the regression model developed using field biomass data. The image was smoothed using a 5 by 5 window, but the pixel size remains as 15 m (Sun et al., 2011). Reprinted with permission from Elsevier Ltd. (RightsLink).

LIDAR–SAR synergy was within 10% of the reference biomass map derived from LVIS data. These results show the potential of the combined use of LIDAR samples and radar imagery for forest biomass mapping (Sun et al., 2011). As shown in Figure 3, maps of mean mangrove height and biomass in the Everglades National Park have been produced by Simard et al. (2006) using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data were calibrated using airborne LIDAR data and a highresolution USGS Digital Elevation Model (DEM). The mangrove height map had a mean tree height error of 2.0 m (RMSE) over a single pixel of 30 m. Field data were used to derive a relationship between mean forest stand height and biomass to map the spatial distribution of the standing biomass of mangroves for the entire national park (Simard et al., 2006). Several excellent review articles on mangrove remote sensing are available in the literature (Heumann, 2011; Jensen et al., 1991; Kuenzer et al., 2011; Li et al., 2007). In tidal salt marshes, Hardisky, Smart, and Klemas (1983a) found that vegetation and infrared indices were significantly correlated with live leaf biomass, total AGB, live biomass (%), leaf moisture (%), and canopy moisture. Hardisky, Smart, and Klemas (1983b) assessed the feasibility of using spectral measurements to detect and quantify changes in salt marsh plants because of environmental perturbations. Leaf biomass was the major plant parameter controlling spectral delineation among freshwater, sewage effluent, and control plots of



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Figure 3. Mangrove forest biomass map near Everglades National Park in Florida produced using the elevation data from the Shuttle Radar Topography Mission (SRTM). (Simard et al., 2006). Reprinted with permission from ASPRS, Bethesda, Maryland.

Spartina alterniflora. Hardisky, Klemas, and Smart (1983) found that an infrared index was most effective for biomass estimation of short form S. alterniflora, whereas a vegetation index (VI) was the most effective for biomass estimation of the tall form of S. alterniflora. Hardisky et al. (1984) determined that biomass estimates predicted from in situ VI and infrared index models compared favorably with biomass estimates obtained from traditional harvest techniques. Gross et al. (1987) used Landsat TM imagery to quantify and map the distribution of live AGB of the dominant smooth cordgrass (Spartina alterniflora) in a Delaware salt marsh. The total S. alterniflora live AGB for the marsh was estimated from satellite-gathered radiance data to be 1.70 3 109 g dry weight

(gdw) distributed over 580 ha, for a mean of 294 gdw/m2, with 1 standard deviation (SD) from the mean being 76 gdw/m2. A large collection of 784 field measurements of radiance from S. alterniflora was used to compare with satellite measurements. The remotely sensed biomass estimates were within 13% of those derived from ground-gathered radiance and harvest data. This study clearly demonstrated that satellite imagery can be used to derive cost-effective, accurate estimates of the live aerial biomass of S. alterniflora throughout an entire salt marsh. The technique has since been used in other areas where monospecific vegetation dominates areas at least several times as large as the image pixels (Gross, Hardisky, and Klemas,


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1990; Gross, Klemas, and Levasseur, 1986; Gross et al., 1987; Jensen et al., 2002). Gross et al. (1991) studied the relationship between aboveground and belowground biomass in S. alterniflora–dominated marshes. This enabled the authors to estimate the total biomass, including the belowground biomass, of the marshes from the satellite-derived values of the AGB. Belowground biomass production and its relationship to AGB has been studied by various authors (Gallagher and Plumley, 1979; Schubauer and Hopkinson, 1984; Seliskar, 1983; Smith, Good, and Good, 1979; Whigham and Simpson, 1978). Jensen et al. (2002) investigated the use of high spatial resolution (0.7 3 0.7 m), metric, multiband aerial photography to measure biophysical parameters in a smooth cordgrass (S. alterniflora) wetland in the ACE Basin Natural Estuarine Research Reserve in South Carolina. In situ biophysical ground-reference information was collected at approximately the same time as the remotely sensed data acquisition. The NIR band, in addition to selected vegetation indices (simple ratio, NDVI, soil-adjusted VI), was shown to be highly correlated with total aboveground dry biomass, LAI, and chlorophyll a and b content. The derivation of significant models for predicting S. alterniflora biophysical parameters made it possible to create maps of total AGB, LAI, and chlorophyll a and b content. These were the first maps to be created for S. alterniflora wetlands in South Carolina at submeter resolution and represented a significant benchmark against which future biophysical inventories can be compared. Dillabaugh and King (2008) used IKONOS imagery to map vegetation composition and biomass in three riparian marshes in Ontario, Canada. For vegetation-composition mapping, a separability and correlation analysis aided the selection of an optimum set of spectral and texture input variables. Several maximum-classification tests for sets of terrestrial and aquatic vegetation classes gave best accuracies from 61% for seven classes to 88% for five classes. For biomass mapping, dried green and senescent biomass collected at 75 locations was modeled using stepwise, forward multiple regression. The best model produced was the logarithm of green biomass against a combination of texture and spectral variables at R2 ¼ 0.61. The model was applied to the image data to map green biomass with an absolute error of 213 g/m2, or approximately 40% of the mean field-measured biomass. Because of the magnitude of that error, the output map was aggregated into three simple classes of biomass (high, medium, and low), which also showed a strong visual correspondence with the spatial distribution observed in the field (Dillabaugh and King, 2008). Seagrass species, cover, and biomass have been mapped with multispectral scanners on satellites, airborne hyperspectral imagers, and digital cameras (Armstrong, 1993; Gullstrom et al., 2006; Schweitzer, Armstrong, and Posada, 2005; Zhang, 2010). For instance, Phinn et al. (2008) assessed the accuracy of airborne, hyperspectral CASI-2, Quickbird-2 multispectral, and Landsat-5 TM multispectral data sets for mapping seagrass species composition, horizontally projected foliage cover and aboveground dry-weight biomass in Moreton Bay, Australia. The work was carried out in an area that was shallow and contained clear coastal waters with a range of seagrass species, cover, and biomass levels. The mapping was

constrained to depths shallower than 3 m, based on past modeling of the separability of seagrass reflectance signatures at increasing water depths. At the 4-m spatial resolution, the airborne hyperspectral data produced higher accuracies (46%) than did the Quickbird-2 and Landsat-5. However, the results demonstrated that accurate mapping (.80%) of seagrass cover, species composition, and biomass requires further work using high spatial resolution (,5 m), multispectral or hyperspectral image data (Phinn et al., 2008). The question of how differing levels of tidal inundation affect the reflectance characteristics of emergent marsh vegetation still needs to be better documented. Kearney et al. (2009) found that several major species of marsh vegetation showed significant reductions in near-infrared reflectance with progressive substrate inundation. The Leaf-Area –Index (LAI) decreased greatly when water depths on the substrate reached 15 cm. Increasing inundation produced particularly significant changes in the 700–825 nm band of the spectrum. Simulations of Landsat TM bands showed that the NDVI is highly correlated with the LAI and suggests that NDVI-based estimates for marsh biomass can be strongly influenced by the effects of marsh canopy submergence on LAI.

WETLAND CHANGE DETECTION USING BIOMASS To identify long-term trends and short-term variations, such as the impact of rising sea levels and hurricanes on coastal wetlands, one needs to analyze time-series of remotely sensed imagery. For studying changes in wetlands, there are now various multidate data sets available, such as the Landsat archive and multisensor data sets. Some of these archives contain thousands of images. For instance, Google now has the entire global Landsat and MODIS archives sitting on its cloud server and its ‘‘Earth Engine’’ enables users to define algorithms that can be applied to the entire archive anywhere in the world. When absolute comparisons between different dates are to be carried out, the preprocessing of multidate sensor imagery is much more demanding than it is for the single-date cases (Jensen, 1996; Ramsey and Laine, 1997). Multidate imagery requires a sequence of operations, including calibration to radiance or at-satellite reflectance, atmospheric correction, image registration, geometric correction, mosaicking, subsetting, and the masking out of clouds. In the preprocessing of multidate images, the most critical steps are the registration of the multidate images and their radiometric rectification. To minimize errors, registration accuracies of a fraction of a pixel must be attained. The second critical requirement for change detection is attaining a common radiometric response for the quantitative analysis for one or more of the image pairs acquired on different dates. This means that variations in solar illumination, atmospheric scattering and absorption, and detector performance must be normalized, i.e. the radiometric properties of each image must be adjusted to those of a reference image (Coppin and Bauer, 1994; Lunetta and Balogh, 1999; Lunetta and Elvidge, 1998). Detecting changes between two registered and radiometrically corrected images from different dates can be accomplished by employing one of several techniques, including postclassi-



fication comparison and spectral image differencing. In change detection by postclassification comparison, two images from different dates are independently classified. The two classified maps are then compared on a pixel-by-pixel basis. This avoids the difficulties in change detection associated with the analysis of images acquired at different times of the year or day or by different sensors, thereby minimizing the problem of radiometric calibration between dates. One disadvantage is that every error in the individual date classification maps will also be present in the final change-detection map (Dobson et al., 1995; Jensen, 1996; Lunetta and Elvidge, 1998). Spectral image differencing is the most widely applied change-detection algorithm. This technique requires the transformation of two original images to a new single-band or multiband image in which the areas of spectral change are highlighted. This is accomplished by subtracting one date of raw or transformed (e.g. vegetation indices, albedo, etc.) imagery from a second date, which has been precisely registered to the image of the first date. Pixel difference values exceeding a selected threshold are considered as changed. A change/no change binary mask is overlaid onto the second date image, and only the pixels labeled as having changed are classified in the second date imagery. Although the unchanged pixels remain in the same classes as in the first date imagery, the spectrally changed pixels must be further processed by other methods, such as by use of a classifier, to produce a labeled land-cover change map. The band difference or ‘‘change’’ image is clustered and then aggregated into ‘‘change’’ and ‘‘no-change’’ spectral classes. This approach eliminates the need to identify land cover changes in areas where no significant spectral change has occurred between the two dates of imagery (Coppin and Bauer, 1994; Yuan, Elvidge, and Lunetta, 1998). To obtain accurate results, radiometric normalization must be applied to one date of imagery to match the radiometric condition of the two dates of data before image subtraction. A comparison of the spectral image differencing and the postclassification comparison algorithms is provided by Macleod and Congalton (1998). The acquisition and analysis of the time series of multispectral imagery is a difficult task because, ideally, the imagery should be acquired under similar environmental conditions (e.g. same time of year, sun angle, etc.) and in the same or similar spectral bands. There will be changes in both time and spectral content. One way to approach this problem is to reduce the spectral information to a single index, reducing the multispectral imagery into one single field of the index for each time step. In this way, the problem is simplified to the analysis of the time series of a single variable, one for each pixel of the images. The most common index used is the NDVI, which is expressed as the difference between the red and NIR reflectances, divided by their sum (Cihlar, St.-Laurent, and Dyer, 1991; Goward et al., 1991). These two spectral bands represent the most-detectable spectral characteristic of green plants because the red (and blue) radiation is absorbed by the chlorophyll in the surface layers of the plant (the palisade parenchyma), and the NIR is reflected from the inner leaf-cell structure (the spongy mesophyll) as it penetrates several leaf layers in a canopy. Thus, the NDVI can be related to plant

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biomass or stress because the NIR reflectance depends on the abundance of plant tissue and the red reflectance indicates the surface condition of the plant. Researchers have shown that time series of remote-sensing data can be used effectively to identify long-term trends and subtle changes of NDVI by means of principal component analysis (Jensen, 2007; Young and Wang, 2001; Yuan, Elvidge, and Lunetta, 1998). The NDVI must be used with caution in areas with less than 20% vegetation cover, especially when the soil is covered by water or algae, and in areas where the vegetation fraction is higher than 50%, causing saturation. Another successful approach for identifying changed wetland pixels in digital imagery by detecting changes in their AGB has been developed by Weatherbee (2000). To detect biomass changes the Modified Soil-Adjusted Vegetation Index (MSAVI) was chosen with red and NIR reflectances. The soil-adjusted VI had been developed to minimize soil influences on canopy spectra (Qi et al., 1994). The MSAVI was applied to a time series of Landsat/TM images and used with selected thresholds to detect biomass changes. To minimize natural variations between images in the time series (e.g. atmospheric, annual, seasonal, etc.), it was assumed that the relative distribution of biomass in each subbasin would remain essentially constant over time. Any wetland pixel whose MSAVI deviation from the subbasin mean changed from its previous deviation by more than the selected threshold was considered to have changed. To minimize costs, only the changed sites ‘‘flagged’’ by Landsat/ TM were studied in more detail with high-resolution systems (Weatherbee, 2000).

SUMMARY AND CONCLUSIONS Wetland biomass changes can be indicative of vegetation stress induced by natural and human-caused disturbances. Improved new sensors and data analysis techniques have become available that make remote-sensing techniques attractive for monitoring natural and constructed coastal-ecosystem changes, including wetland biomass. Because wetlands are spatially complex and temporally quite variable, mapping their vegetation often requires high-resolution satellite or aircraft imagery. A cost-effective approach is to map large areas with the Landsat TM and focus on critical or rapidly changing wetland sites with high-resolution sensors. Airborne georeferenced digital cameras are particularly suitable for accurate wetland mapping. Satellites with high-resolution multispectral and hyperspectral imagers have significantly improved the mapping of the extent, species composition, and biomass of upstream wetlands, salt marshes, and mangroves. Using LIDAR, hyperspectral and radar imagery, and narrow-band vegetation indices, researchers have been able to discriminate wetland species and make progress on estimating biochemical and biophysical parameters of vegetation, such as water content, biomass, and Leaf Area Index. The integration of hyperspectral imagery and LIDAR-derived elevation has further improved the accuracy of the mapping of salt marsh vegetation. The mapping of SAV, coral reefs, and general bottom characteristics from satellites has also become more accurate because high-resolution (0.64 m) imagery is available. Aerial hyperspectral scanners and high-resolution multispectral


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satellites, such as IKONOS and QuickBird, have been used to map SAV with accuracies of about 75% for classes that include high-density seagrass, low-density seagrass, and unvegetated bottoms. Optical and radar remote-sensing methods and empirical and statistical regression models have been used with NDVI to estimate the amount and temporal variability of AGB. Multispectral and hyperspectral imagers have been applied to collect large amounts of biomass data on a global scale for forested areas, such as upland forests, forested wetlands, and mangroves. Forest height and biomass have been mapped using LIDAR and SAR. LIDAR can measure the height of the trees and the vertical profile of the canopy, whereas SAR senses the canopy volume and provides image data. In salt marshes, vegetation and infrared indices have been shown to be correlated with live leaf biomass, total aboveground biomass, live biomass (%), leaf moisture (%), and canopy moisture. Leaf biomass was the major plant parameter controlling spectral delineation among freshwater, stressed plots, and control plots of Spartina alterniflora. Biomass estimates predicted from in situ vegetation index and infrared index models compared favorably with biomass estimates obtained from traditional harvest techniques. The NIR band and selected vegetation indices (simple ratio, NDVI, soiladjusted VI), have been shown to be highly correlated with total aboveground dry biomass, LAI, and chlorophyll a and b content of salt marshes. Landsat TM imagery has been used to quantify and map the distribution of live AGB of the dominant smooth cordgrass (Spartina alterniflora). The total S. alterniflora, live AGB has been estimated for large marsh areas using satellite-gathered radiance data. The remotely sensed biomass estimates were within 13% of those derived from ground-gathered radiance and harvest data. Time series of remote-sensing data have been used with the NDVI to identify long-term trends and subtle changes in wetlands. One successful approach for identifying changed wetland pixels is based on detecting changes in their AGB. To detect the biomass changes, the MSAVI was chosen with red and NIR reflectances. This index minimizes soil influences on canopy spectra. The MSAVI was then applied to a time series of Landsat/TM images and used with selected thresholds to detect biomass changes in tidal salt marshes. The research results described in this article clearly demonstrate that some key biophysical characteristics of wetlands, including their AGB, can be measured with highresolution remote sensors and extrapolated over large coastal areas.

ACKNOWLEDGMENTS This research was supported in part by the NOAA National Sea Grant College Program and by the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) at the University of Delaware.

LITERATURE CITED Adam, E.; Mutanga, O., and Rugege, D., 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management, 18(3), 281–296.

Akins, E.R.; Wang, Y., and Zhou, Y., 2010. EO-1 Advanced Land Imager data in submerged aquatic vegetation mapping. In: Wang, J. (ed.). Remote Sensing of Coastal Environment. Boca Raton, Florida: CRC, 297–312. Anderson, G.L.; Hanson, J.D., and Haas, R.H., 1993. Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semi-arid rangelands. Remote Sensing of Environment, 45(2), 165–175. Armstrong, R.A., 1993. Remote sensing of submerged vegetation canopies for biomass estimation. International Journal of Remote Sensing, 14(3), 621–627. Artigas, F.J. and Yang, J., 2006. Spectral discrimination of marsh vegetation types in the New Jersey meadowlands, USA. Wetlands, 26(1), 271–277. Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment, 64(3), 234– 253. Baccini, A.; Laporte, N.; Goetz, S.J.; Sun, M., and Dong, H., 2008. A first map of Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters, 3(4), 045011. doi: 10. 1088/1748–9326/3/4/045011. Baghdadi, N.; Bernier, M.; Gauthier, R., and Neeson, I., 2001. Evaluation of C-band SAR data for wetland mapping. International Journal of Remote Sensing, 22(1), 71–88. Bostater, C.R., Jr.; Ghir, T.; Bassetti, L.; Hall, C.; Reyeier E.; Lowers, R.; Holloway-Adkins, K., and Vernstein, R., 2004. Hyperspectral remote sensing protocol development for submerged aquatic vegetation in shallow waters. In: Bostater, C.R., Jr., and Santoleri, R. (eds.), Remote Sensing of the Ocean and Sea Ice—SPIE Proceedings (Barcelona, Spain) Volume 5233, pp. 199–602. doi:10. 1117/12.541191. Brock, J.; Wright, C.W.; Kuffner, I.B.; Hernandez, R., and Thompson, P., 2006. Airborne LIDAR sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract. Remote Sensing of Environment, 104(1), 31–42. Brock, J.C. and Purkis, S.J., 2009. The emerging role of LIDAR remote sensing in coastal research and resource management. In: Brock, J.C. and Purkis, S.J. (eds.), Coastal Applications of Airborne LIDAR. Journal of Coastal Research, Special Issue No. 53, pp. 1–5. Campbell, C., Vitt, D.H., Halsey, L.A., Campbell, I.D., Thormann, M.N., and Bayley, S.E., 2000. Net Primary Production and Standing Biomass in Northern Continental Wetlands. Edmonton, AB, Canada: Canadian Forest Service, Northern Forestry Centre, Information Report NOR-X-369, 63p. Cihlar, J.; St.-Laurent, A., and Dyer, J.A., 1991. Relation between the Normalized Difference Vegetation Index and ecological variables. Remote Sensing of Environment, 35(2–3), 279–298. Cohen, W.B. and Goward, S.N., 2004. Landsat’s role in ecological applications of remote sensing. BioScience, 54(6), 535–545. Coppin, P.R. and Bauer, M.E., 1994. Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features. IEEE Transactions on Geoscience and Remote Sensing, 32(4), 918–927. Dahl, T.E., 2006. Status and Trends of Wetlands in the Conterminous United States 1998 to 2004. Washington, DC: U.S. Department of the Interior, Fish and Wildlife Service Publication, 112p. Dierssen, H.M.; Zimmermann, R.C.; Leathers, R.A.; Downes, V., and Davis, C.O., 2003. Ocean color remote sensing of seagrass and bathymetry in the Bahamas banks by high resolution airborne imagery. Limnology and Oceanography, 48(1), 444–455. Dillabaugh, L.A. and King, D.J., 2008. Riparian marshland composition and biomass mapping using Ikonos imagery. Canadian Journal of Remote Sensing, 34(2), 143–158. Dobson, J.E.; Bright, E.A.; Ferguson, R.L.; Field, D.W.; Wood, L.L.; Haddad, K.D.; Iredale, H., III; Jensen, J.R.; Klemas, V.; Orth, R.J., and Thomas, J.P., 1995. NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation. Washington, DC: U.S. Department of Commerce. NOAA Technical Report NMFS123, 92p. Dobson, M.C.; Ulaby, F.T.; LeToan, T.; Beaudoin, T.; Kasischke, E.S., and Christensen, N., 1992. Dependence of radar backscatter on



coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 412–415. Dong, J.; Kaufmann, R.K.; Myneni, R.B.; Tucker, C.J.; Kauppi, P.E.; Liski, J.; Buermann, W.; Alexeyev, V., and Hughes, M.K., 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84(3), 393–410. Eleuterius, L.N., 1990. Tidal Marsh Plants. Gretna, Louisiana: Pelican, 168p. Ellis, J.M. and Dodd, H.S., 2000. Applications and lessons learned with airborne multispectral imaging. In: Applied Geologic Remote Sensing: Proceedings of the Fourteenth International Conference and Workshops on Applied Geologic Remote Sensing (Las Vegas, Nevada). Ann Arbor, Michigan: Veridian ERIM Intl., pp. 123–131. Farris, G.S., 2005. USGS Reports New Wetland Loss from Hurricane Katrina in Southeastern Louisiana. http://www.usgs.gov/ newsroom/article.asp?ID¼997. Ferguson, R.L. and Korfmacher, K.F., 1997. Remote sensing and GIS analysis of seagrass meadows in North Carolina, USA. Aquatic Botany 58(3–4), 241–258. Filippi, A.M. and Jensen, J.R., 2006. Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment, 100(4), 512–530. Gallagher, J.L. and Plumley, F.G., 1979. Underground biomass profiles and productivity in Atlantic coastal marshes. American Journal of Botany, 66(2), 156–161. Gallagher, J.L.; Somers, G.F.; Grant, D.M., and Seliskar, D.M., 1988. Persistent differences in two forms of Spartina alterniflora: a common garden experiment. Ecology, 69(4), 1005–1008. Garono, R.J.; Simenstad, C.A.; Robinson, R., and Ripley, H., 2004. Using high spatial resolution hyperspectral imagery to map intertidal habitat structure in Hood Canal Washington, USA. Canadian Journal of Remote Sensing, 30(1), 54–63. Ghioca-Robrecht, D.M.; Johnston, C.A., and Tulbure, M.G., 2008. Assessing the use of multiseason QuickBird imagery for mapping invasive species in a Lake Erie coastal marsh. Wetlands, 28(4), 1028–1039. Gilmore, M.S.; Civco, D.L.; Wilson, E.H.; Barrett, N.; Prisloe, S.; Hurd, J.D., and Chadwick, C., 2010. Remote sensing and in situ measurements for delineation and assessment of coastal marshes and their constituent species. In: Wang, J. (ed.). Remote Sensing of Coastal Environment. Boca Raton, Florida: CRC, pp. 261–280. Gilmore, M.S.; Wilson, E.H.; Barrett, N.; Civco, D.L.; Prisloe, S.; Hurd, J.D., and Chadwick, C., 2008. Integrating multitemporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sensing of Environment, 112(11), 4048–4060. Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J., and Duke, N., 2010. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159, doi: 10.1111/j. 1466– 8238.2010.00584.x. Gough, L.; Grace, J.B., and Taylor, K.L., 1994. The relationship between species richness and community biomass: the importance of environmental variables. Oikos, 70(2), 271–279. Goward, S.N.; Markham, B.; Dye, D.G.; Dulaney, W., and Yang, J., 1991. Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sensing of Environment, 35(2–3), 257–277. Gower, S.T.; Kucharik, C.J., and Norman, J.M., 1999. Direct and indirect estimation of leaf area index, f(APAR), and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1), 29–51. Grieser, J; Gommes, R; Cofield, S., and Bernardi, M., 2006. World Maps of Climatological Net Primary Production of Biomass, NPP. Rome, Italy: UN/FAO, Environment, Climate Change and Bioenergy Division. FAO–Climpag, 35p. Gross, M.F.; Hardisky, M.A., and Klemas, V., 1990. Inter-annual spatial variability in the response of Spartina alterniflora biomass to amount of precipitation. Journal of Coastal Research, 6(4), 949– 960.

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Gross, M.F.; Hardisky, M.A.; Klemas, V., and Wolf, P.L., 1987. Quantification of biomass of the marsh grass Spartina alterniflora Loisel using Landsat Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing, 53(11), 1577–1583. Gross, M.F.; Hardisky, M.A.; Wolf, P.L., and Klemas, V., 1991. Relationship between aboveground and belowground biomass of Spartina alterniflora (Smooth Cordgrass). Estuaries, 14(2), 180– 191. Gross, M.F.; Klemas, V., and Levasseur, J.E., 1986. Remote sensing of Spartina anglica biomass in five French salt marshes. International Journal of Remote Sensing, 7(5), 657–664. Gullstrom, M.; Lunden, B.; Bodin, M.; Kangwe, J.; Ohman, M.C.; Mtolera, S.P., and Bjork, M., 2006. Assessment of changes in the seagrass-dominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing. Estuarine, Coastal and Shelf Science, 67(3), 399–408. Hardisky, M.A.; Daiber, F.C.; Roman, C.T., and Klemas, V., 1984. Remote sensing of biomass and annual net aerial productivity of a salt marsh. Remote Sensing of Environment, 16(2), 91–106. Hardisky, M.S.; Klemas, V., and Smart, R.M., 1983. The influence of soil salinity, growth form and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing, 49(1), 77–83. Hardisky, M.A.; Smart, R.M., and Klemas, V., 1983a. Seasonal spectral characteristics and aboveground biomass of the tidal marsh plant, Spartina alterniflora. Photogrammetric Engineering and Remote Sensing, 49(1), 85–92. Hardisky, M.A.; Smart, R.M., and Klemas, V., 1983b. Growth response and spectral characteristics of a short Spartina alterniflora salt marsh irrigated with freshwater and sewage effluent. Remote Sensing of Environment, 13(1), 57–67. Hess, M.M. and Melack, J.M., 1994. Mapping wetland hydrology and vegetation with synthetic aperture radar. International Journal of Ecology and Environmental Sciences, 20(SI), 197–205. Hess, L.L.; Melack, J.M.; Novo, E.M.L.M.; Barbosa, C.C.F. and Gastil, M., 2003. Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sensing of Environment, 87(4), 404–428. Hess, M.M.; Melack, J.M., and Simonett, D.S., 1990. Radar detection of flooding beneath the forest canopy: a review. International Journal of Remote Sensing, 11(7), 1313–1325. Heumann, B.W., 2011. Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Progress in Physical Geography, 35(1), 87–108. Hughes, A.R.; Williams, S.L.; Duarte, C.M.; Heck, K.L., Jr., and Waycott, M., 2009. Associations of concern: declining seagrasses and threatened dependent species. Frontiers in Ecology and the Environment, 7(5), 242–246. Jensen, J.R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd edition. Upper Saddle River, New Jersey: Prentice-Hall. Jensen, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, New Jersey: PrenticeHall. Jensen, J.R.; Coombs, C.; Porter, D.; Jones, B.; Schill, S., and White, D., 1998. Extraction of smooth cordgrass (Spartina alterniflora) biomass and leaf area index parameters from high resolution imagery. Geocarto International, 13(4), 25–34. Jensen, R.R.; Mausel, P.; Dias, N.; Gonser, R.; Yang, C.; Everitt, J., and Fletcher, R., 2007. Spectral analysis of coastal vegetation and land cover using AISAþ hyperspectral data. Geocarto International, 22(1), 17–28. Jensen, J.R.; Olson, G.; Schill, S.R.; Porter, D.F., and Morris, J., 2002. Remote sensing of biomass, leaf-area-index, and chlorophyll-a and b content in the ACE Basin National Estuarine Research Reserve using sub-meter digital camera imagery. Geocarto International, 17(3), 27–36. Jensen, J.R.; Lin, H.; Yang, X.; Ramsey, E.; Davis, B.A., and Thoemke, C.W., 1991. The measurement of mangrove characteristics in southwest Florida using SPOT multispectral data. Geocarto International, 6(2), 13–21.


1026

Klemas

Kamal, M. and Phinn, S.R., 2011. Hyper-spectral data for mangrove species mapping: A comparison of pixel-based and object-based approach. Journal of Applied Remote Sensing, 3(10), 2222–2242, doi:10.3390/rs3102222. Kasischke, E.S.; Bourgeau-Chavez, L.L.; Christensen, N.L., and Haney, E., 1994. Observations on the sensitivity of ERS-1 SAR image intensity to changes in aboveground biomass in young loblolly-pine forest. International Journal of Remote Sensing, 15(1), 3–16. Kasischke, E.S.; Melack, J.M., and Dobson, M.C., 1997. The use of imaging radars for ecological applications-a review. Remote Sensing of Environment, 59(2), 141–156. Kearney, M.S.; Stutzer, D.; Turpie, K., and Stevenson, J.C., 2009. The effects of tidal inundation on the reflectance characteristics of coastal marsh vegetation. Journal of Coastal Research, 25, 1177– 1186. Kellndorfer, J.; Walker, W.; LaPoint, E.; Bishop, J.; Cormier, T.; Fiske, G.; Hoppus, K., and Westfall, J., 2012. NACP Aboveground Biomass and Carbon Baseline Data Set (NBCD2000), U.S.A., 2000. Oak Ridge, Tennessee: Oak Ridge National Laboratory, Distributed Active Archive Center for Biogeochemical Dynamics, doi:10. 3334/ORNLDAAC/1081. Kelly, M. and Tuxen, K., 2009. Remote sensing support for tidal wetland vegetation Research and management. In: Yang, X. (ed.). Remote Sensing and Geospatial Technologies for Coastal Ecosystem Assessment and Management. Berlin: Springer-Verlag. Kim, W.; Mohrig, D.; Twilley, R., and Parker, G., 2009. Is it feasible to build new land in the Mississippi River Delta? EOS Transactions, 90(42), 373–374. Kirwan, M.L.; Guntenspergen, G.R.; D’Alpaos, A.; Morris, J.T.; Mudd, S.M., and Temmermen, S., 2010. Limits on the adaptability of coastal marshes to rising sea level. Geophysical Research Letters, 37(23), L23401, doi: 10.1029/2010GL045489. Klemas, V., 2009. The role of remote sensing in predicting and determining coastal storm impacts. Journal of Coastal Research, 25(6), 1264–1275. Klemas, V., 2011. Remote sensing of wetlands: case studies comparing practical techniques. Journal of Coastal Research, 27(3), 418–427. Ku, N.-W.; Popescu, N.C.; Ansley, R.J.; Perotto-Baldivieso, H.L., and Fillippi, A.N., 2012. Assessment of available rangeland woody plant biomass with a terrestrial LIDAR system. Photogrammetric Engineering and Remote Sensing, 78(4), 349–361. Kuenzer, C.; Bluemel, A.; Gebhardt, S.; Vo Quoc, T., and Dech, S., 2011. Remote sensing of mangrove ecosystems. Remote Sensing, 3(5), 878–928. Laba, M.; Downs, R.; Smith, S.; Welsh, S.; Neider, C.; White, S.; Richmond, M.; Philpot, W., and Baveye, P., 2008. Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using QuickBird satellite imagery. Remote Sensing of Environment, 112(1), 286–300. Lam, N.S.N.; Liu, K.-B.; Liang, W.; Bianchette, T.A., and Platt, W.J., 2011. Effects of hurricanes on the Gulf Coast Ecosystems: a remote sensing study of land cover change around Weeks Bay, Alabama. In: Frumanczyk, K.; Giza, A, and Terefenko, P. (eds.). ICS 2011 Proceedings (Szczecin, Poland). Journal of Coastal Research, Special Issue No. 64, pp. 1707–1711. Lang, M.W. and McCarty, G.W., 2008. Remote sensing data for regional wetland mapping in the United States: Trends and future prospects. In: Russo, R.E. (ed.). Wetlands: Ecology, Conservation and Restoration. Hauppauge, New York: Nova. Lathrop, R.G.; Montesano, P., and Haag, S., 2006. A multi-scale segmentation approach to mapping seagrass habitats using airborne digital camera imagery. Photogrammetric Engineering and Remote Sensing, 72(6), 665–675. Leberl, F.; Perko, R.; Gruber, M., and Ponticelli, M., 2002. Novel concepts for aerial digital cameras. In: Symposium of Commission I of the International Society for Photogrammetry and Remote Sensing (ISPRS) (Denver, Colorado). ISPRS Archives, 34(1). Lechner, A.M.; Fletcher, A.; Johansen, K., and Erskine, P., 2012. Characterising upland swamps using object-based classification methods and hyper-spectral resolution imagery derived from an unmanned aerial vehicle. ISPRS Annals of Photogrammetry,

Remote Sensing and Spatial Information Sciences, I-4, 101–106. doi:10.5194/isprsannals-I-4-101-2012. Lefsky, M.A.; Cohen, W.B.; Parker, G.G., and Harding, D.J., 2002. LIDAR remote sensing for ecosystem studies. BioScience, 52(1), 19–30. Li, X.; Gar-On Yeh, A.; Wang, S.; Liu, K.; Qian, J., and Chen, X., 2007. Regression analysis for estimating mangrove wetland biomass in southern China using Radarsat images. International Journal of Remote Sensing, 28(24), 5567–5582. Li, L.; Ustin, S.L., and Lay, M., 2005. Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA, USA. International Journal of Remote Sensing, 26(23), 5193–5207. Louchard, E.M.; Reid, R.P.; Stephens, F.C.; Davis, C.O.; Leathers, R.A., and Downes, T.E., 2003. Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: a comparative spectral classification approach. Limnology and Oceanography, 48(1, pt 2), 511–521. Lu, Z.; Crane, M.; Kwoun, O.I.; Wells, C.; Swarzenski, C., and Rykhus, R., 2005. C-band radar observes water level change in swamp forests. EOS Transactions, 86(14), 141–144. Lucas, R.; Armston, J.; Fairfax, J.; Fensham, R.; Dwyer, J.; Bowen, M.; Eyre, T.; Laidlaw, M., and Shimada, M., 2010. An evaluation of the ALOS PALSAR L-band backscatter- above ground biomass relationship over Queensland, Australia. IEEE Journal of Selected Topics in Earth Observations and Remote Sensing, 3(4), 576–593. Lunetta, R.S. and Balogh, M.E., 1999. Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photogrammetric Engineering and Remote Sensing, 65(11), 1303–1310. Lunetta, R.S. and Elvidge, C.D., 1998. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Ann Arbor, Michigan: CRC, 318p. Lyon, J.G. and McCarthy, J., 1995. Wetland and Environmental Applications of GIS. New York: Lewis, 400p. Macleod, R.D. and Congalton, R.G., 1998. A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 64(3), 207–216. McCoy, R., 2005. Field Methods in Remote Sensing. New York: Guilford, 159p. Maeder, J.; Narumalani, S.; Rundquist, D.; Perk, R., Schalles, J.; Hutchins, K., and Keck, J., 2002. Classifying and mapping general coral-reef structure using Ikonos data. Photogrammetric Engineering and Remote Sensing, 68(12), 1297–1305. McInnes, K.L.; Walsh, K.J.E.; Hubbert, G.D., and Beer, T., 2003. Impact of sea-level rise and storm surges on a coastal community. Natural Hazards, 30(2), 187–207. Mishra, D.; Narumalani, S.; Rundquist, D., and Lawson, M., 2006. Benthic habitat mapping in tropical marine environments using QuickBird multispectral data. Photogrammetric Engineering and Remote Sensing, 72(9), 1037–1048. Mitch, W.J. and Gosselink, J.G., 2007. Wetlands. Hoboken, New Jersey: Wiley, 574p. Moore, D.R.J. and Keddy, P.A., 1989. The relationship between species richness and standing crop in wetlands: the importance of scale. Vegetatio, 79(1–2), 99–106. Morris, J.T.; Sundareshwar, P.V.; Nietch, C.T.; Kjerfve, B., and Cahoon, D.R., 2002. Responses of coastal wetlands to rising sea level. Ecology, 83(10), 2869–2877. Morton, R.A. and Barras, J.A., 2011. Hurricane impacts on coastal wetlands: A half-century record of storm-generated features from Southern Louisiana. Journal of Coastal Research, 27(6A), 27–43. Mumby, P.J., and Edwards, A.J., 2002. Mapping marine environments with IKONOS imagery: Enhanced spatial resolution can deliver greater thematic accuracy. Remote Sensing of Environment, 82(2–3), 248–257. Mutanga, O. and Skidmore, A.K., 2004. Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19), 3999–4014. Odum, E.P., 1993. Ecology and Our Endangered Life-Support Systems, 2nd edition. Sunderland, Massachusetts: Sinauer, 320p.



Orth, R.J.; Carruthers, T.J.B.; Dennison, W.C.; Duarte, C.M.; Fourqurean, J.W.; Heck, K.L. Jr.; Hughes, A.R.; Kendrick, G.A.; Kenworthy, W.J.; Olyarnik, S.; Short, F.T.; Waycott, M., and Williams, S.L., 2006. A Global Crisis for Seagrass Ecosystems. BioScience, 56(12), 987–996. Ozesmi, S.L. and Bauer, M.E., 2002. Satellite remote sensing of wetlands. Wetland Ecology and Management, 10(5), 381–402. Pengra, B.W.; Johnston, C.A., and Loveland, T.R., 2007. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sensing of Environment, 108(1), 74–81. Peregon, A.; Maksyutov, S.; Kosykh, N.P., and Mironycheva-Tokareva, N.P., 2008. Map-based inventory of wetland biomass and net primary production in Western Siberia. AGU Journal of Geophysical Research, Biogeosciences, 113(G1), 1–12. doi:10.1029/ 2007JG000441. Phinn, S.; Roelfsema, C.; Decker, A.; Brando, V., and Anstee, J., 2008. Mapping seagrass species, cover and biomass in shallow waters: an assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sensing of Environment, 112(15), 3413–3425. Pinet, P.R., 2009. Invitation to Oceanography, 5th edition. Sudbury, Massachusetts: Jones and Bartlett, 620p. Pope, K.O.; Rey-Benayas, J.M., and Paris, J.F., 1994. Radar remote sensing of forest and wetland ecosystems in the Central American tropics. Remote Sensing of Environment, 48(2), 205–219. Pu, R.; Bell, S.; Baggett, L.; Meyer, C., and Zhao, Y., 2012. Discrimination of seagrass species and cover classes with in situ hyperspectral data. Journal of Coastal Research, 28(6), 1330–1344. Purkis, S.J., 2005. A ‘reef-up’ approach to classifying coral habitats from IKONOS imagery. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1375–1390. Purkis, S. and Klemas, V., 2011. Remote Sensing and Global Environmental Change. Oxford, United Kingdom: Wiley-Blackwell, 384p. Purkis, S.J.; Kenter, J.A.M.; Oikonomou, E.K., and Robinson, I.S., 2002. High-resolution ground verification, cluster analysis and optical model of reef substrate coverage on Landsat TM imagery (Red Sea, Egypt). International Journal of Remote Sensing, 23(8), 1677–1698. Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H., and Sorooshian, S., 1994. A modified soils adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126. Ramsey, E., 1995. Monitoring flooding in coastal wetlands by using radar imagery and ground-based measurements. International Journal of Remote Sensing, 16(13), 2495–2502. Ramsey, E.W. and Laine, S.C., 1997. Comparison of Landsat Thematic Mapper and high resolution photography to identify change in complex coastal wetlands. Journal of Coastal Research, 13(3), 281–292. Ramsey, E. and Rangoonwala, A., 2010. Mapping the onset and progression of marsh dieback. In: Wang, J. (ed.), Remote Sensing of Coastal Environment. Boca Raton, Florida: CRC, pp. 123–149. Ramsey, E.; Werle, D.; Suzuoki, Y.; Rangoonwala, A., and Lu, Z., 2011. Limitations and potential of satellite imagery to monitor environmental response to coastal flooding. Journal of Coastal Research, 28(2), 457–476. Riegel, B., 2012. A comparison of Remote Sensing Methods for Estimating Above-Ground Carbon Biomass at a Wetland Restoration Area in the Southeastern Coastal Plain. http://dukespace.lib. duke.edu/dspace/handle/10161/5164. Rignot, E.; Way, J.B.; Williams, C., and Viereck, L., 1994. Radar estimates of aboveground biomass in boreal forests of interior Alaska. IEEE Transactions on Geoscience and Remote Sensing, 32(5), 1117–1124. Rosso, P.H.; Ustin, S.L., and Hastings, A., 2005. Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data. International Journal of Remote Sensing, 26(23), 5169–5191. Running, S.W.; Nemani, R.R.; Heinsch, F.A.; Zhao, M.; Reeves, M., and Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. BioScience, 54(6), 547– 560.

1027

Schmidt, K.S. and Skidmore, K.A., 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85(1), 92–108. Schmidt, K.S.; Skidmore, A.K.; Kloosterman, E.H.; Van Oosten, H.; Kumar, L., and Janssen, J.A.M., 2004. Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogrammetric Engineering and Remote Sensing, 70(6), 703–716. Schubauer, J.P. and Hopkinson, C.S., 1984. Above- and belowground emergent macrophyte production and turnover in a coastal marsh ecosystem, Georgia. Limnology and Oceanography, 29(5), 1052–1065. Schweitzer, D.; Armstrong, R.A., and Posada, J., 2005. Remote sensing characterization of benthic habitats and submerged vegetation biomass in Los Roques Archipelago National Park, Venezuela. International Journal of Remote Sensing, 26(12), 2657– 2667. Seliskar, D.M., 1983. Root and rhizome distribution as an indicator of upper salt marsh wetland limits. Hydrobiologia, 107(3), 231–236. Shan, J. and Hussain, E., 2010. Object-based data integration and classification for high-resolution coastal mapping. In: Wang, J. (ed.). Remote Sensing of Coastal Environment. Boca Raton, Florida: CRC, pp. 210–234. Silva, T.S.F.; Costa, M.P.F.; Melack, J.M., and Novo, E.M.L.M., 2008. Remote sensing of aquatic vegetation: theory and applications. Environmental Monitoring and Assessment, 140(1–3), 131–145. Simard, M.; Fatoyinbo, L.E., and Pinto, N., 2010. Mangrove canopy 3D structure and ecosystem productivity using active remote sensing. In: Wang, Y. (ed.). Remote Sensing of Coastal Environments. Boca Raton, Florida: CRC, Taylor and Francis Group, LLC, pp. 61–78. Simard, M.; Zhang, K.; Rivera-Monroy, V.H.; Ross, M.S.; Ruiz, P.S.; Castaneda-Moya, E.; Twilley, R.R., and Rodriguez, E., 2006. Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data. Photogrammetric Engineering and Remote Sensing, 72(3), 299–311. Smith, K.K.; Good, R.E., and Good, N.F., 1979. Production dynamics for above and belowground componentsof a New Jersey Spartina alterniflora tidal marsh. Estuarine and Coastal Marine Science, 9(2), 189–201. Sun, G.; Ranson, K.J.; Guo, Z.; Zhang, Z.; Montesano, P., and Kimes, D., 2011. Forest biomass mapping from lidar and radar synergies. Remote Sensing of Environment, 115(11), 2906–2916. Thenkabail, P.S.; Lyon, J.G., and Huete, A., 2012. Hyperspectral Remote Sensing of Vegetation. Boca Raton, Florida: CRC. 705p. Thorne, K.M.; Takekawa, J.Y., and Elliott-Fisk, D.L., 2012. Ecological effects of climate change on salt marsh wildlife: a case study from a highly urbanized estuary. Journal of Coastal Research, 28(6), 1477–1487. Tiner, R.W., 199. Wetlands. In: Manual of Photographic Interpretation, 2nd ed. Falls Church, Virginia: American Society for Photogrammetry and Remote Sensing, 2440p. Townsend, P.A., 2001. Mapping seasonal flooding in forested wetlands using multi-temporal RADARSAT SAR. Photogrammetric Engineering and Remote Sensing, 67(7), 857–864. Townsend, P.A., 2002. Estimating forest structure in wetlands using multitemporal SAR. Remote Sensing of Environment, 79(2–3), 288– 304. Turner, D.P.; Ollinger, S.V., and Kimball, J.S., 2004. Integrating remote sensing and ecosystem process models for landscape- to regional-scale analysis of the carbon cycle. BioScience, 54(6), 573– 584. Underwood, E.C.; Mulitsch, M.J.; Greenberg, J.A.; Whiting, M.L.; Ustin, S.L., and Kefauver, S.C., 2006. Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery. Environmental Monitoring and Assessment, 121(1–3), 47–64. Ustin, S.L.; Roberts, D.A.; Gamon, J.A.; Asner, G.P., and Green, R.O., 2004. Using imaging spectroscopy to study ecosystem processes and properties. BioScience, 54(6), 523–534. Wang, Y., 2010. Remote sensing of coastal environments: an overview. In: Wang, J. (ed.). Remote Sensing of Coastal Environments. Boca Raton, Florida: CRC, pp. 1–21.


1028

Klemas

Wang, L.; Sousa, W.P., and Gong, P., 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing, 25(24), 5655–5668. Wang, Y.; Christiano, M., and Traber, M., 2010. Mapping salt marshes in Jamaica Bay and terrestrial vegetation in Fire Island National Seashore using QuickBird satellite data. In: Wang, J. (ed.). Remote Sensing of Coastal Environments. Boca Raton, Florida: CRC, pp. 191–208. Weatherbee, O.P., 2000. Application of satellite remote sensing for monitoring and management of coastal wetland health. In: Gutierrez, J. (ed.). Improving the Management of Coastal Ecosystems through Management Analysis and Remote Sensing/GIS Applications. Newark, Delaware: University of Delaware Sea Grant Report, pp. 122–141. Whigham, D.F. and Simpson, R.L., 1978. The relationship between aboveground and belowground biomass of freshwater tidal wetland macrophytes. Aquatic Botany, 5, 355–364. doi:10.1016/ 0304–3770(78)90076-1. Williams, M. (ed.), 1990. Wetlands: A Threatened Landscape. Oxford, United Kingdom: Basil Blackwell, 419p. Yang, J. and Artigas, F.J., 2010. Mapping salt marsh vegetation by integrating hyperspectral and LIDAR remote sensing. In: Wang, J. (ed.). Remote Sensing of Coastal Environment. Boca Raton, Florida: CRC, pp. 173–187. Yang, C.; Everitt, J.H.; Fletcher, R.S.; Jensen, J.R., and Mausel, P.W., 2009. Mapping black mangrove along the south Texas gulf coast using AISAþ hyperspectral imagery. Photogrammetric Engineering and Remote Sensing, 75(4), 425–436.

Ye, Y., Zhou, C., Sun, Y., and Zhou, D., 2010. Estimation of wetland aboveground biomass based on SAR image: a case study of Honghe National Natural Reserve in Heilongjiang, China. In: Liu, Y. and Chen A. (eds.), Proceedings of the 18th International Conference on Geoinformatics, (Beijing, China), pp. 992–927. Young, S.S. and Wang, C.Y., 2001. Land-cover change analysis of China using global-scale Pathfinder AVHRR Landcover (PAL) data, 1982–92. International Journal of Remote Sensing, 22(8), 1457–1477. Yuan, D.; Elvidge, C.D., and Lunetta, R.S., 1998. Survey of multispectral methods for land cover change analysis. In: Lunetta, R.S. and Elvidge, C.D. (eds.). Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Chelsea, Michigan: Ann Arbor, pp. 21–39. Zhang, X., 2010. On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: a case study of the Honghu Lake, PR China. International Journal of Remote Sensing, 19(1), 11–20. Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C., and Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471–475. Zhang, Y.; Lu, D.; Yang, B.; Sun, C., and Sun, M., 2011. Coastal wetland vegetation classification with Landsat Thematic Mapper image. International Journal of Remote Sensing, 32(2), 545–561. Zhang, M.; Ustin, S.L.; Rejmankova, E., and Sanderson, E.W., 1997. Monitoring Pacific Coast salt marshes using remote sensing. Ecological Applications, 7(3), 1039–1053.
