Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh
Introduction
Coastal wetlands are a critical and dynamic component of the Long Island Sound ecosystem. Over the past century, a significant amount of these wetlands has been lost due to development, filling and dredging, or damaged due to anthropogenic disturbance and modification. Global sea level rise is also likely to have a significant impact on the condition and health of coastal wetlands, particularly if the wetlands cannot migrate due to dense coastal development (e.g., Donnelly & Bertness, 2001). In addition to physical loss of marshes, the species composition of marsh communities is changing. Spartina alterniflora (saltwater cordgrass) and Spartina patens (salt meadow grass), once the dominant species of New England salt marshes, are being replaced by monocultures of the non-native genotype of Phragmites australis (Cav.) Trin. ex Steud (common reed) in Connecticut marshes (Barrett and Prisloe, 1998, Chambers et al., 1999, Orson, 1999, Warren et al., 2001). Phragmites outcompetes other marsh species in areas with increased fresh water, nitrogen and sediment and its presence is positively correlated with marsh fragmentation (Moore et al., 1999, Bertness et al., 2002, Bart et al., 2006). In response to the increase of Phragmites in many marshes, several government agencies, academic institutions, and conservation organizations have instituted efforts (commencing in the 1980s) to restore marsh health, including the eradication of Phragmites in some areas. The response of marshes to eradication includes both an increase of non-Phragmites marsh species and Phragmites reinvasion (Farnsworth and Meyerson, 1999, Meyerson et al., 2000).
With the mounting anthropogenic and climatic pressures on coastal wetland areas, it is becoming increasingly important to identify and inventory the current extent and condition of wetlands located throughout the coastal region of the Long Island Sound estuary, implement a cost effective method to track changes in the condition of wetlands over time, and monitor the effects of habitat restoration and management activities. Because marsh fieldwork is labor intensive, remote sensing is an efficient way to characterize coastal wetlands due to its synoptic coverage and repeatability. For management applications, image data must be of high enough spatial and spectral resolution to effectively identify stands of each species without being cost-prohibitive. This has led many workers to develop classification methods using widely available high spatial resolution, low spectral resolution image data such as aerial photographs (e.g., Shima et al., 1976, Phinn et al., 1999, Shuman and Ambrose, 2003, Maheu-Giroux and de Blois, 2005) or to use coarse-resolution (30m–1km) multispectral data (e.g., Donoghue and Shennan, 1987, Arzandeh and Wang, 2003). These methods are most successful at identifying large-scale stands of dense, monotypical species, but have limited applicability to meter-scale mapping of individual species within a heterogeneous mosaic of marsh plants. Improved vegetation maps have been produced using traditional supervised and unsupervised classifiers on high spatial resolution multispectral and hyperspectral data (e.g., Underwood et al., 2003, Schmidt et al., 2004, Belluco et al., 2006, Wang et al., 2007, Pengra et al., 2007, Sadro et al., 2007, Laba et al., 2008). These classification methodologies are based on image and/or ground reference data measured on a single date, which limits their applicability to images taken at other times. Vegetation phenology has long been recognized to be useful in discriminating species for vegetation mapping (e.g., Reed et al., 1994, Key et al., 2001), as the spectrum of a single species may vary throughout the growing season due to variations in the amount and ratios of plant pigments, leaf water content, plant height, canopy effects, leaf angle distribution and other structural characteristics. Previous work on the classification of marsh vegetation using multi-temporal image data (Dennison and Roberts, 2003, Belluco et al., 2006, Judd et al., 2007) and LiDAR data (Rosso et al., 2006) relies on judicious identification of endmembers, often derived from extensive field measurements. Such field measurements may be impractical if a goal is to inventory vegetation in even a small number of marshes. Endmember selection can be enhanced using image processing algorithms (e.g., Dennison and Roberts, 2003, Judd et al., 2007). In this work, we take a different approach, and test a new method to relate field measurements at a limited number of sites to image classification of an entire marsh.
Several studies demonstrate significant spectral differences between marsh plant species in both field reflectance data (Hardisky et al., 1986, Zhang et al., 1997, Schmidt and Skidmore, 2003, Gao and Zhang, 2006) and hyperspectral (Gross & Klemas, 1986) reflectance data at various times during the growing season. Laba et al. (2005) computed the derivatives of field reflectance spectra of purple loosestrife, Phragmites and cattail in the Hudson River estuary weekly throughout the growing season and determined that these species were best differentiated in late August. Artigas and Yang (2005) measured reflectance spectra of Phragmites in the field throughout the growing season and determined that the spectra were significantly separable, and that characteristics of the field spectra correlated with seasonal patterns of vigor interpreted from classified hyperspectral AISA data of the New Jersey Meadowlands. The results of these studies suggest that phenological variability of the VNIR reflectance of marsh plants can guide image classification, however none of these studies do so.
The purpose of this study was to propose and evaluate a novel approach, where characteristics of field reflectance spectra of marsh vegetation measured over the growing season were used to direct the classification of high spatial resolution multi-temporal QuickBird data (2.4m/pixel) of a Connecticut marsh. This is the first known attempt to use field reflectance characteristics to define rules for the classification of multi-temporal image data. Single date LiDAR data also contributed to the classification. Our goals were to: 1) determine the optimal times during the growing season for the discrimination of individual marsh plant species based on spectral reflectance and structure measured in the field, and 2) assess the utility of these field data to direct the classification of multi-temporal, multispectral images of the entire marsh, with particular attention to the mapping of the invasive species Phragmites. Additionally, we sought to provide mapping protocols that can be used to identify a single species, such as Phragmites, from a single date of multispectral imagery, as these data are likely to be the most accessible to land managers.
Section snippets
Study site
Ragged Rock Creek marsh is a 142-hectare brackish tidal marsh located on the western bank of the Connecticut River, approximately 2.5km north of its confluence with Long Island Sound (Fig. 1). The vegetation at Ragged Rock Creek marsh is typical of Connecticut's estuarine tidal marshes, where the pattern of growth is generally controlled by salinity, a function of tidal inundation and therefore elevation. The appearance of the vegetation at Ragged Rock Creek marsh is a mosaic, ranging from
Spectral data collection and processing
Reflectance spectra were obtained using an ASD Fieldspec FR® spectroradiometer (Analytical Spectral Devices, Boulder, CO) with a wavelength range of 350–2500nm, a sampling interval of 1.4nm between 350–1000nm and 2nm between 1000–2500nm, and a spectral resolution of 3nm between 350–1000nm and 10nm between 1000–2500nm. Individual spectral measurements were an average of 5–10 scans and each canopy was generally sampled 10 or more times. These samples were then averaged to provide a single
Phenology and structure of major species
The canopy reflectance spectra of each plant community were broadly similar, including absorptions typical of healthy photosynthesizing vascular plants (Fig. 3). At the beginning of the growing season, each spectrum showed expected increases in the strength of the absorptions at approximately 450nm and 680nm due to chlorophylls and carotenoids within the leaves and an increase in NIR reflectance due to leaf biomass (e.g., Wooley, 1971). This trend continued in each species until the onset of
Spectral characteristics of marsh species across the growing season
The spectral characteristics of vegetation are due to leaf pigments, plant structure (e.g., biomass and canopy architecture and cover) and plant health throughout the phenological cycle. Much of the spectral variability in the field data can be attributed to expected increases in plant pigments and biomass during the green-up phase of plant growth, and the decline of these variables and the increased contribution of background materials during senescence. The magnitude and rate of these changes
Conclusion
This study presents a new technique for the classification of major marsh plant species within a complex, heterogeneous tidal marsh using multi-temporal QuickBird images, field reflectance spectra and LiDAR height information. Analyses of the phenological variation of spectral and structural characteristics of marsh species measured in the field throughout the growing season were necessary to select the best dates to discriminate Phragmites, Typha spp. and S. patens. These three species are
Acknowledgements
This study was funded by the EPA Long Island Sound Research Program, grant LI97100901. A portion of the data was provided by the NOAA Coastal Services Center Coastal Remote Sensing Project and the Institute for the Application of Geospatial Technology. The 2006 field work was funded by the CT DEP Long Island Sound License Plate Program. We very much appreciate the contributions of Bill Moorhead and Joel Labella in the field and the careful reviews provided by the referees and the editors of
References (52)
- et al.
Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing
Remote Sensing of Environment
(2006) - et al.
Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information
Photogrammetry and Remote Sensing
(2004) - et al.
Expansion of Phragmites australis into tidal wetlands of North America
Aquatic Botany
(1999) - et al.
The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral
Remote Sensing of Environment
(2003) - et al.
Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China
Estuarine, Coastal and Shelf Science
(2006) - et al.
The use of Airborne Imaging Spectrometer (AIS) data to differentiate marsh vegetation
Remote Sensing of Environment
(1986) - et al.
Remote sensing of biomass and annual net primary productivity of a salt marsh
Remote Sensing of Environment
(1984) - et al.
A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest
Remote Sensing of Environment
(2001) - et al.
Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using QuickBird satellite imagery
Remote Sensing of Environment
(2008) - et al.
Mapping the invasive species Phragmites australis in linear wetland corridors
Aquatic Botany
(2005)
Mapping and invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor
Remote Sensing of Environment
Use of LiDAR to study changes associated with Spartina invasion in San Francisco Bay marshes
Remote Sensing of Environment
Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations
Remote Sensing of Environment
Spectral discrimination of vegetation types in a coastal wetland
Remote Sensing of Environment
Salt marsh vegetation radiometry data analysis and scaling
Remote Sensing of Environment
Mapping nonnative plants using hyperspectral imagery
Remote Sensing of Environment
Mapping mixed vegetation communities in salt marshes using airborne spectral data
Remote Sensing of Environment
Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands
International Journal of Remote Sensing
Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA
Wetlands
Monitoring the change of Phragmites distribution using satellite data
Canadian Journal of Remote Sensing
Spatial patterns of expansion by Phragmites australis (Cav.) Trin. ex Steud. within the tidelands of the Connecticut River from 1968 to 1994
Environmental determinants of Phragmites australis expansion in a New Jersey salt marsh: An experimental approach
OIKOS
Human facilitation of Phragmites australis invasions in tidal marshes: A review and synthesis
Wetlands Ecology and Management
Anthropogenic modification of New England salt marsh landscapes
Proceedings of the National Academy of Sciences
Assessing the Accuracy of Remotely Sensed Data: Principles and Practices
Cited by (138)
A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data
2023, International Journal of Applied Earth Observation and GeoinformationRandom forest classifications for landuse mapping to assess rapid flood damage using Sentinel-1 and Sentinel-2 data
2023, Remote Sensing Applications: Society and EnvironmentDetection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series
2022, Remote Sensing of EnvironmentDetection of seasonal changes in vegetation and morphology on coastal salt marshes using terrestrial laser scanning
2021, GeomorphologyCitation Excerpt :However, evaluating salt marshes with more complex vegetation species and little variation in plant height, is a continued challenge due to the limited topographic information provided by the TLS. Therefore, the combination of topographic and spectral information can greatly improve the eco-morphology mapping accuracy of salt marshes (Gilmore et al., 2008; Fernandez-Nunez et al., 2017). Further research will focus on combining high spatial resolution photography and high-resolution DCM derived from TLS, which will greatly improve vegetation habitat classification of salt marshes.