6 Multitemporal Remote Sensing of Coastal ...

6

Multitemporal Remote Sensing of Coastal Sediment Dynamics Paul Elsner, Tom Spencer, Iris Möller, and Geoff Smith

Contents 6.1 Introduction................................................................................................... 109 6.2 Field Site........................................................................................................ 110 6.3 Data................................................................................................................ 112 6.4 Data Analysis................................................................................................. 113 6.5 Results............................................................................................................ 115 6.6 Discussion...................................................................................................... 118 6.7 Conclusions.................................................................................................... 119 Acknowledgments................................................................................................... 120 References............................................................................................................... 120

6.1 Introduction

Edit OK or “management” meant?

The need for informed management of the coastal zone is increasingly pressing, considering climate change-related challenges and threats that are anticipated for coasts worldwide (Nicholls et al. 2007). In England and Wales alone, approximately 1.5 million people live in low-lying coastal areas that are protected by sea defenses (Jorissen et al. 2000). It has been estimated that 1.1 million properties in an area of 400,000 ha and a capital value of £137 billion are at risk of flooding or coastal erosion in England (DEFRA 2004). The expected acceleration of sea-level rise, in combination with the already observed loss of natural coastal defenses in the form of extensive areas of intertidal saltmarsh, is hence of significant concern for coastal managers (Turner 1995; Nicholls and de la Vega-Leinert 2008). A central strategy for adapting to both salt marsh loss and an anticipated acceleration in the rate of sea-level rise is a shift from “hard engineering” approaches using rigid defense structures to “soft engineering” measures using beach nourishment and managed realignment to create natural coastal buffers (DEFRA 2002; Hanson et al. 2002). Managed realignment sites with elevations below the threshold for colonization by salt marsh plants will most probably initially convert to intertidal mudflats. Promotion of the rapid sedimentation and accretion of tidally imported mineral sediments is therefore 109

K13587_C006.indd 109

10/12/2011 3:17:21 PM

110

Environmental Remote Sensing and Systems Analysis

a central objective in such management interventions, so that intertidal vegetation can subsequently colonize and stabilize such substrates (French 2006). The sedimentation dynamics of mudflat environments are, compared to beaches and salt marshes, less well understood (Christie et al. 1999; Dyer et al. 2000). A central reason for this is their difficult accessibility, resulting in higher costs and overall research effort of field monitoring campaigns. Furthermore, most field-based methods represent point measurements, making it difficult to estimate the spatial variability of processes associated with a dynamic water body. Remote sensing approaches offer an alternative and supplemental technique in this respect because they collect synoptic and spatially coherent information. A number of studies have demonstrated the potential of such approaches to measure sediment concentration (Liedtke et al. 1995; Shimwell 1998; Froidefond et al. 2004). Most airborne coastal remote sensing applications rely on single imagery to capture sediment loading. Longer deployments of airborne platforms that target temporal sediment dynamics have rarely been implemented. A notable exception was a study of the Humber estuary, U.K. east coast. Here an airborne sensor was repeatedly flown along transects across the mouth of the estuary to derive sediment flux estimates (Robinson et al. 1999). Despite the promising results of this project, few follow-on projects have been implemented (Sterckx et al. 2006). This chapter presents the results of a coastal multitemporal monitoring campaign in which a series of overflights captured the inflow of tidal waters into a managed realignment site in South East England with a multispectral sensor.

6.2 Field Site The research presented here focused on sedimentation processes at the Tollesbury managed realignment project in Essex, South East England. The Tollesbury study area is located on the eastern shore of Tollesbury Fleet, a northern side arm of the Blackwater estuary (Figure 6.1). The Fleet is fronted by extensive estuarine fringing salt marsh (Allen 2000). The mean tidal range lies between 4.7 m at springs and 3.0 m on neap tides. The mean high water reaches 2.6 m above Ordnance Datum Newlyn (ODN, which approximates to mean sea level) at springs (MHWS) and 1.5 m at neaps (MHWN) (Reading et al. 2008). The marshes are backed by seawalls and the marshes themselves are in places reinforced by brushwood groins to halt or slow erosion. Substantial marsh area has been lost over the past 100 years (van der Wal and Pye 2004). The earliest modern U.K. managed realignment site was established at Northey Island on the River Blackwater in 1991 (Leggett et al. 2004). Further interventions in this estuary have taken place at Abbots Hall (1992 and 2002), Tollesbury (1995), and Orplands (1995) (Figure 6.1). The managed realignment site at Tollesbury was reclaimed more than 160 years ago. In August 1995, a 60 m wide gap was created in the outer seawall to reexpose an area of approximately 21 ha to the inflow and outflow of tidal waters. Prior to breaching, a new seawall was constructed along the 3-m (ODN) height contour at the east and southeast side of the site. In addition to this, a 100 m long and 2 m wide channel was cut to connect the old drainage network to the breach.

K13587_C006.indd 110

10/12/2011 3:17:21 PM

111

Multitemporal Remote Sensing of Coastal Sediment Dynamics Riv er

Abberton Reservoir

Co lne

Abbots hall managed realignment site

Mersea Island

Salcott Channel Virley Channel

Tollesbury managed realignment site

Tollesbury Fleet

River

a Bl

er

at

w ck

Orplands managed realignment site

N

0

0.000 0.417 0.833 1.250 1.667

52.917

52.917

52.500

52.500

52.083

52.083

51.667

51.667

51.250

51.250

1

2

3

4

5

6 km

0.000 0.417 0.833 1.250 1.667

Figure 6.1 Overview of the Blackwater estuary and the location of managed realignment schemes. (Based on the Ordnance Survey, © Crown copyright. With permission.)

At the time of breaching, the site had a complex microtopography: its northeastern part was particularly low in the tidal frame with elevations of 1.0 m (ODN), compared to 1.20 m (ODN) toward the northwestern part of the site. There was a general rise in surface elevation in southwesterly direction, leading to heights of more than 2 m (ODN) in southern parts of the site. Most surface elevations within the site were therefore significantly lower compared to mature salt marshes at the seaward site of the seawalls, which typically have elevations in the range of 2.4 to

K13587_C006.indd 111

10/12/2011 3:17:21 PM

112


2.6 m (ODN) (Reading et al. 2008). This height differences can be attributed to the combined effect of dewatering and compaction of soils within the site since reclamation and continued vertical accretion of salt marshes outside the site (Hazeldon and Boorman 2001).

6.3 Data The data for this research project was acquired on July 29, 1999, by a CASI-2 sensor on board a research aircraft that was operated by the Airborne Research and Survey Facility (ARSF) of United Kingdom’s Natural Environment Research Council (NERC). The CASI-2 sensor is a two-dimensional CCD array based pushbroom imaging spectrograph with a theoretical spectral range between 405 and 950 nm and a dynamic range of 12 bit (Wilson 1997; ARSF 2002). The potential theoretical spectral sampling rate is 1.8 nm and the spectral resolution is 2.2 nm full width at half maximum (FWHM). The across-track field of view is 54.4°, making it possible to record a scanline of 512 pixels. For the Tollesbury campaign, the CASI-2 sensor was operated in the spatial mode and 14 channels were programmed in the ARSF default ocean color bandset (Table 6.1). The multitemporal data set consisted of a series of 18 overflights that captured the inflow during a spring tide at the Tollesbury managed realignment site. The first flight started at 10:05 GMT when the site was still in an unflooded state. Subsequently, the inflow of tidal waters was monitored with overflights every 8 to 10 minutes until near high tide at 12:30 GMT. Overflight 15 (12:04 GMT) did not capture the managed realignment site fully and had therefore to be omitted from further analysis. This omission created the biggest step in the time series, constituted by the 15 minutes interval between overflight 14 (11:56 GMT) and overflight 16 (12:11 GMT). The

Table 6.1 CASI Ocean Color Bandset Band 1 2 3 4 5 6 7 8 9 10 11 12 13 14

K13587_C006.indd 112

Start nm

End nm

407.5 437.5 485 505 555 615 660 677.5 700 750 767.5 855 885 895

417.5 447.5 495 515 565 625 670 685 710 757.5 782.5 875 895 905

10/12/2011 3:17:21 PM

Multitemporal Remote Sensing of Coastal Sediment Dynamics

113

weather conditions during the entire flying campaign were clear. The flying height of 1,400 m equated to a spatial resolution of 2.8 m. Concurrent ancillary data sets on the ground were not collected because the survey was scheduled at short notice as a target of opportunity. This constituted significant problems for the analysis of the remotely sensed data because established procedures using empirical models could not be applied.

6.4 Data Analysis Due to the absence of concurrent ground reference data, it was necessary to establish a temporally robust hydro-optical model for the estuarine waters at Tollesbury that could then be employed for the analysis of the CASI images. This was done with the aid of the Water Colour Simulator (WASI), developed by Peter Gege from the Remote Sensing Technology Institute of the German Aerospace Center (DLR) (Gege 2001, 2003, 2005). WASI is a physically based analytical model that uses nonlinear optimization procedures to analyze and simulate a wide range of hydro-optical parameters, including the concentration of suspended particulate matter. WASI links a series of models that describe the physical process of light traveling through: (a) the atmosphere, (b) the hydrosphere, and (c) the air–water interface. Although originally developed for freshwater applications, WASI offers the opportunity to parameterize the model for coastal Case-2 water environments such as those at Tollesbury. “Case-2” as meant, or should To aid model parameterization and adaptation, extensive boat-based field specthis be “CASI2”? troscopy was undertaken between August 2001 and May 2002, using a Geophysical and Environmental Research Corporation GER 1500 field spectrometer. Sampling was carried out in Eulerian mode from a moored position in the central part of the site. The sensor height above the water level was 1 m, resulting in a circular field of view of approximately 16 cm. The overall sampling procedure followed an approach similar to that of Doxaran et al. (2002) and included the extraction of water samples concurrent to spectral measurements. The water samples were subsequently analyzed for the concentration of its optically active ingredients (phytoplankton, yellow substance, and suspended particulate matter) using standard laboratory procedures (Edwards and Glysson 1999; Parsons et al. 1989). The field measurements resulted in a set of 100 spectra and concurrent water samples that covered a wide range of tidal stages and suspended sediment concentrations (SSCs) at Tollesbury. This set was randomly split to provide independent data for model parameterization and validation, respectively. In addition to boat-based spectroscopy, a number of terrestrial pseudoinvariant feature (PIF) spectra were collected that facilitated atmospheric correction and conversion of the CASI data to units of reflectance, using the empirical line method (Smith and Milton 1999). The following WASI submodels were parameterized, based on the parameterization data set of in situ water spectra and concurrent water samples (n = 50): downwelling irradiance Ed(λ), specular reflectance at the water surface Ld *(λ ), and the subsurface irradiance reflectance spectrum R(λ). R(λ) was of particular interest for the eventual model inversion to determine suspended sediment concentration and

K13587_C006.indd 113

10/12/2011 3:17:22 PM

114


was parameterized as a function of the absorption coefficient a and the backscattering coefficient bb of the water body (Gordon et al. 1975; Sathyendranath and Platt 1997): R( 0 ) = f

bbw + Cbbc + Sbbs aw + Cac + Ya y + Sas + bbw + Cbbc + Sbbs

(6.1)

Modelled SSC (mg·L−l)

180 160 140 120 100 80 60 40 20

R2 = 0.49

0

50 100 150 Measured SSC (mg·L−l)

SSC (mg·L−l)

where f is a proportionality factor that is a function of the mean cosines for the downwelling and upwelling irradiances and the ratio of the upward-scattering coefficient and the backscattering coefficient, and the subscripts w, c, y, and s stand for water, phytoplankton, yellow substance, and suspended particulate matter, respectively. C, Y, and S stand for the corresponding concentrations. Both a and bb exhibit some wavelength dependency but this property has been omitted here for display convenience. The accuracy of the parameterized WASI model was determined with an absolute root-mean-square error (RMSE) of 22 mg∙L –1 and a normalized RMSE of 26% (Figure 6.2). Measured and modeled SSC had a coefficient of determination R2 of 0.49 (p < .01). Further statistical analysis showed that neither mean difference nor relative error had a significant bias (Smith and Smith 2007). Additional validation was carried out with actual CASI airborne data collected during additional flights at Tollesbury in 2002. During these flights, concurrent ground reference data could be collected. The validation with this data had a much higher accuracy than results using field spectroscopy data. However, the size of the airborne CASI validation data set was very small (n = 3), which limited the statistical significance of the accuracy measures. Model sensitivity was tested against parameter uncertainty for chlorophyll-a, yellow substance, suspended particulate matter, sunlight reflected at the water surface, anisotropy of the underwater light field, and water temperature. The analysis showed that the model was particularly sensitive to variations in reflection of direct sunlight at the water surface and changes in the underwater light field. Both parameters are a function of wave geometry that changes dynamically due to the local wave climate. Strong sensitivity existed also for negative variations of the backscattering coefficient for suspended particulate matter, bbs. Little sensitivity was present to variations in the 180 160 140 120 100 80 60 40 20 0

Measured

1

8

Modelled

15 22 29 36 43 50 Sample

Figure 6.2 Accuracy of the calibrated hydro-optical model for the Tollesbury CASI data set.

K13587_C006.indd 114

10/12/2011 3:17:22 PM


115

concentration of chlorophyll-a, yellow substance, and suspended particulate matter (SPM) and to differences in water temperature. All vary substantially throughout the year. There are, therefore, no limitations to the use of the WASI model with spectral data from different seasons. More care, however, is needed when analyzing data collected under windy conditions and a correspondingly more dynamic wave climate. The calibrated model was eventually applied to invert the spectral water reflectance measured by the CASI sensor to estimates of SSC.

6.5 Results The inversion of the calibrated WASI model resulted in a series of maps that show estimates of suspended sediment concentration of the incoming tidal waters with a temporal resolution of between 7 and 15 minutes (Figure 6.3). The time series

10:47

10:56

11:05

11:13

11:21

11:30

11:39

11:47

11:56

12:11

12:20

12:30

Suspended sediment concentration (mg·L ) −l

40 50 60 70 80

or less

90 100 110 120 130 140

150 160 170 180 190 200 or more

Figure 6.3 Times series of SSC maps at Tollesbury, depicting tidally driven sediment dynamics. Times (GMT) indicate timing of respective overflights.

K13587_C006.indd 115

10/12/2011 3:17:24 PM

116


3000

Inflow since previous overflight Tidal velocity in breach

Volume (m3·min−1)

2500 2000 1500 1000 500 0

6

7

8

9

10 11 12 13 14 15 16 17 18 Overflight number

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

Velocity (m·s−1)

illustrates the spatiotemporal sediment patterns within the Tollesbury managed realignment project during a summer spring tide. Three main stages can be identified: (1) tidal onset with increasing infilling of the external creek network and former drainage channels inside the managed realignment area until the bankfull stage was reached at approximately 11:00 GMT, with sediment loads under 100 mg∙L –1, (2) large-scale across-mudflat inflow into the site until 12:00 GMT with heterogeneous SSC patterns and loads of up to 200 mg∙L and more, indicating considerable resuspension of sediment within the site, and (3) near-slack water phase until 12:31 GMT with more homogeneous SSC patterns in the range of 120 to 160 mg∙L –1. The water height at respective overflights was determined by the waterline method (Lohani 1999; Foody et al. 2005) where a lidar digital elevation model (DEM), provided by the United Kingdom’s Environment Agency, was combined with the measured near-infrared reflectance (CASI channels 12–14). This resulted in the estimation of the water levels at respective overflights with an RMSE of approximately 13 cm and made it possible to estimate tidal inflow and current velocity in the seawall breach for each interval. Figure 6.4 illustrates that the inflow in terms of water volume increased strongly until overflight 12 and remained at a comparatively high level thereafter. The tidal current velocity in the breach also peaked in the period between overflights 11 and 12, but decreased by more than 70% between overflights 12 and 18. Assuming a fully mixed water column, the combination of these data sets with the modeled sediment concentration at the breach and within the site allowed imported sediment mass and total within-site suspended sediment to be inferred for all stages (Figure 6.5). This then served as the basis for interoverflight sediment balances that quantified the differences between total (cumulative) imported sediment and gain of overall suspended sediment within the site for each interval. If the increase in sediment mass in the site’s water column is larger than the sediment mass imported through the breach, then this indicates a resuspension-dominated interval. The opposite case describes a deposition-dominated phase.

Figure 6.4 Tidal hydrodynamics estimated from water height measurements from successive images using the waterline method, normalized to m3∙min–1. Current velocity in the breach calculated by assuming breach width of 60 m and bed elevation of 1 m. Error bars denote one RMSE.

K13587_C006.indd 116

10/12/2011 3:17:24 PM


Sediment (kg·min−1)

600

117

Gain of total suspended sediment since previous overflight Imported sediment since previous overflight

500 400 300 200 100 0

–100

7

8

9

10 11 12 13 14 15 16 17 18

Figure 6.5 Relationship between gain of total suspended sediment in the water column and imported sediment between overflights. Error bars denote one RMSE.

The results illustrated in Figure 6.5 showed that nearly all intervals prior to overflight 14 were resuspension dominated and that the phase between overflights 14 to 17 was deposition dominated. This correlates reasonably well with the estimated flow velocity in the breach and associated shear stress (Figure 6.4). There are two interesting exceptions to this trend. The interval between overflight 11 to 12 was deposition dominated. The last interval between overflights 17 and 18 was again resuspension dominated, despite experiencing a low tidal current velocity near slack water. A possible explanation for these exceptions might include the impact of windgenerated waves that are known to alter short-term tidal hydrodynamics and sediment processes (Christie et al. 1999) and have been reported to be significant across the Tollesbury site (Reading et al. 2008). It should be noted, however, that the error bars indicate a considerable uncertainty. When modeling such derivatives, it is important to account for the uncertainty that is introduced by propagating errors of respective base data layers. In the framework of this project, these were the errors of the lidar DEM (RMSE 13 cm) and the relative error of the hydro-optical model (26% of SPM). The errors of the presented derivative data sets were estimated using standard error propagation theory for bivariate models (Burrough and McDonnell 1998). This included Monte Carlo analysis where, for respective overflights, 100 simulations of water height and 100 simulations of Edit OK or differ- sediment load where constructed, resulting in 10,000 pairwise combinations. ent number? In addition to analyzing total within-site bulk sediment budgets in the temporal domain, the modeled SSC maps were also analyzed spatially. An example parameter is the amount of sediment suspended in the water column of individual pixels. The choice for the specific locations was guided by the position of 20 sediment erosion bridges (SEBs) at which long-term accretion has been monitored since the breach in 1995 (Reading et al. 2008). We may display the tidal patterns for the three SEB locations together with the highest and lowest accretion, respectively (Figure 6.6). It is apparent that the water columns at high-accretion locations tended to contain more sediment at later stages of the tide, compared to low-accretion locations. A subsequent linear regression between both parameters resulted in coefficients of determination (R2) of 0.72 (p < .05) for the three highest and lowest locations.

K13587_C006.indd 117

10/12/2011 3:17:24 PM

118 Sediment in water column (g)

Environmental Remote Sensing and Systems Analysis 1200 1000 800 600 400 200 0

7

8

9 10 11 12 13 14 15 16 17 18 Overflight number

Figure 6.6 Time series of suspended sediment in water column at six example locations for which long-term accretion data was available. Solid lines represent locations with high accretion, and dashed lines indicate low-accretion locations.

When the same analysis was performed for all 20 transect locations where long-term accretion has been measured, the R2 decreased to 0.33 but remained highly significant (p < .01). It was thus possible to establish a statistically significant relationship between single tide dynamics observed from the July 1999 CASI images and longterm accretion measurements on the ground between 1995 and 2000.

6.6 Discussion The accuracy of the WASI model demonstrates that it is possible to calibrate a temporally robust analytical hydro-optical model with good predictive power. It should be noted in this respect that estimations of SSC by traditional in situ methods are also not error free. For instance, Binding et al. (2005) found that laboratory-based gravimetric measurements of suspended sediment concentration had an average standard error of 12.4%. Measurements by optical beam transmissometer can also be sensitive to short-term variations of particle size and composition, resulting in errors of more than 20% (Jago and Bull 2000). It thus appears realistic to collect and analyze suspended sediment data from future airborne data of locations such as Tollesbury without the need for concurrent ground measurements. The successful calibration of the WASI model also indicates that the Water Colour Simulator proved to be a versatile and powerful modeling and analysis tool that can be adjusted to aquatic conditions other than clear freshwater environments for which it originally was developed (Gege 2001, 2003). However, the sensitivity analysis demonstrated that the model is sensitive to changes in the geometric light field that is a function of surface waves. Thus the transferability of the calibrated model should be limited to data collected during relatively calm conditions. The sensitivity analysis further showed that the established hydro-optical model lacks robustness against changes in the backscattering coefficient of inorganic particulate matter bbs. This parameter is a function of particle size distribution, particle shape, and mineralogy (Bukata et al. 1995; Novo et al. 1989). Considering the tidal and seasonal dynamics of an environment such as the Blackwater estuary, where

K13587_C006.indd 118

10/12/2011 3:17:24 PM


119

particle flocculation is significant and highly spatiotemporally variable, the assumption of unchanged sediment scattering properties appears unrealistic (Benson and French 2007; Doxaran et al. 2002; Manning and Bass 2006). Binding et al. (2005) found that scattering coefficients of inorganic sediments in the Irish Sea could vary to up to one order of magnitude. This was particularly significant as scattering of sediment particles dominates the spectral signal of such midturbid waters. Careful case-to-case parameterization of sediment scattering led to a substantial reduction of the errors of hydro-optical models (Binding et al. 2003, 2005). Similar observations have been made for the calibration of optical beam transmissometers when deployed for long-term monitoring projects (Jago and Bull 2000). The model calibration identified for this research project appears to be temporally robust. This may be because all data sets were collected between May and September. It is not known how far the summer calibration of WASI as used in this project would perform for the analysis of data collected in the winter period. Apart from seasonal effects, tidal waters are also known for their short-term variability in suspended sediment concentrations. Observations for mudflats of The Wash, U.K. east coast have identified a strong quarter-diurnal signal, corresponding to changes in flow velocity and sediment resuspension and settlement processes (Jago and Bull 2000). For the Tollesbury setting where data with a temporal resolution of annual) accretion processes, if the spatial heterogeneity of such processes can be resolved. This study also confirms the added value that can be gained by utilizing and linking data sets from different research projects and approaches.

6.7 Conclusions Multitemporal remote sensing offers significant potential to capture and quantify coastal processes such as intertidal suspended sediment dynamics that realistically cannot be obtained by traditional field-based sampling methods. Significant practical challenges have to be overcome, however, before such data collection campaigns can be realized. A central obstacle for implementing remote sensing as an operational research tool for monitoring dynamic coastal environments is logistical problems.

K13587_C006.indd 119

10/12/2011 3:17:24 PM

120


To make remote sensing a more integral part of the methodological suite employed in coastal research, it would therefore be necessary to have unrestricted access to the monitoring equipment when weather and tidal conditions open rare windows of opportunity. This appears to be unlikely in situations where sophisticated airborne platforms have to be shared with the wider remote sensing community. An alternative platform for many applications might be unmanned aerial vehicles (UAVs), which are equipped with relatively simple, low-cost digital multispectral sensors. This would provide a system that could be independently deployed in the field and thus makes remote sensing a more operational monitoring tool for environmental research.

Acknowledgments We wish to acknowledge the support of the Airborne Research and Survey Facility and the Field Spectroscopy Facility, U.K. Natural Environment Research Council. We thank the developer of the Water Colour Simulator, Dr. Peter Gege, German Aerospace Centre (DLR), for making this excellent software available to the scientific community. Adrian Hayes and his team from the Cambridge University Physical Geography Laboratories provided invaluable assistance by developing bespoke field equipment. Paul Elsner is grateful for financial support from the German Academic Exchange Service (DAAD) and for a Cambridge University Domestic Research Studentship.

References Allen, J. R. L. (2000). Morphodynamics of Holocene salt marshes: A review sketch from the Atlantic and Southern North Sea coasts of Europe. Quaternary Science Reviews, 19(12), 1155–1231. ARSF (2002). CASI-2 sensor. User Manual of the Airborne Research and Survey Facility of the National Environment Research Council. Natural Environment Research Council, Oxford, United Kingdom. Benson, T. and French, J. R. (2007). InSiPID: A new low cost instrument for in situ particle size measurements in estuaries. Journal of Sea Research, 58, 167–188. Binding, C. E., Bowers, D. G., and Mitchelson-Jacob, E. G. (2003). An algorithm for the retrieval of suspended sediment concentrations in the Irish Sea from SeaWiFS ocean colour satellite imagery. International Journal of Remote Sensing, 24(19), 3791–3806. Binding, C. E., Bowers, D. G., and Mitchelson-Jacob, E. G. (2005). Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters; the impact of variable particle scattering properties. Remote Sensing of Environment, 94, 373–383. Bukata, R. P., Jerome, J. H., Kondratyev, K. Y., and Pozdnyakov, D. V. (1995). Optical Properties and Remote Sensing of Inland and Coastal Waters. CRC Press, Boca Raton, FL. Burrough, P. A. and McDonnell, R. A. (1998). Principles of Geographic Information Systems. Oxford University Press, Oxford, United Kingdom. Cahoon, D. R., French, J. R., Spencer, T., Reed, D., and Moeller, I. (2000). Vertical accretion versus elevational adjustment in UK saltmarshes: An evaluation of alternative methodologies. In Coastal and Estuarine Environments: Sedimentology, Geomorphology and Geoarchaeology, K. Pye and J. R. L. Allen (eds.), Special Publication. The Geological Society, London, pp. 223–238.

K13587_C006.indd 120

10/12/2011 3:17:24 PM


121

Christie, M. C., Dyer, K. R., and Turner, P. (1999). Sediment flux and bed level measurements from a macro tidal mudflat. Estuarine, Coastal and Shelf Science, 49, 667–688. DEFRA. (2002). Managed Realignment Review: Project Report (Policy Research Project FD 2008). Department of Environment, Food, and Rural Affairs, London. DEFRA. (2004). Making Space for Water. Developing a New Government Strategy for Flood and Coastal Erosion Risk Management in England. A Consultation Exercise. Department of Environment, Food, and Rural Affairs, London. Doxaran, D., Froidefond, J. M., Lavender, S., and Castaing, P. (2002). Spectral signature of highly turbid waters. Application with SPOT data to quantify suspended particulate matter concentrations. Remote Sensing of Environment, 81, 149–161. Dyer, K. R., Christie, M. C., Feates, N., Fennessy, M. J., Pejrup, M., and van der Lee, W. (2000). An investigation into processes influencing the morphodynamics of an intertidal mudflat, the Dollart Estuary, the Netherlands: I. Hydrodynamics and suspended sediment. Estuarine, Coastal and Shelf Science, 50, 607–625. Edwards, T. K. and Glysson, G. D. (1999). Field Methods for Measurement of Fluvial Sediment. U.S. Geological Survey, Reston, VA. Foody, G., Muslim, A. M., and Atkinson, P. M. (2005). Super-resolution mapping of the waterline from remotely sensed data. International Journal of Remote Sensing, 26(24), 5381–5392. French, P. (2006). Managed realignment: The developing story of a comparatively new approach to soft engineering. Estuarine, Coastal and Shelf Science, 67, 409–423. Froidefond, J. M., Lahet, F., Hu, C., Doxaran, D., Guiral, D., Prost, M. T., and Ternon, J.-F. (2004). Mudflats and mud suspension observed from satellite data in French Guiana. Remote Sensing of Environment, 208(2–4), 153–168. Garbutt, R. A., Reading, C. J., Wolters, M., Gray, A. J., and Rothery, P. (2006). Monitoring the development of intertidal habitats on former agricultural land after the managed realignment of coastal defences at Tollesbury, Essex, UK. Marine Pollution Bulletin, 53, 155–164. Gege, P. (2001). The Water Colour Simulator WASI: A Software Tool for Forward and Inverse Modeling of Optical in situ Spectra, IGARSS’01 Proceedings of IEEE Geoscience and Remote Sensing Symposium, Sydney, Australia, pp. 2743–2745. Gege, P. (2005). The Water Colour Simulator WASI. User manual for version 3, DLR-IB 564– 1/2005, http://www.opairs.aero/publications_en.html. Gege, P. (2003). WASI—A software tool for water spectra. Backscatter, 14(1), 22–24. Gordon, H. R., Brown, O. B., and Jacobs, M. M. (1975). Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Applied Optics, 14, 417–427. Hanson, H., Brampton, A., Capobianco, M., Dette, H. H., Laustrup, C., Lechuga, A., and Spanhoff, R. (2002). Beach nourishment projects, practices, and objectives—A European overview. Coastal Engineering Journal, 47(2), 81–111. Hazeldon, J. and Boorman, L. A. (2001). Soils and “managed retreat” in South-East England. Soil Use and Management, 17, 150–154. Jago, C. F. and Bull, C. F. J. (2000). Quantifications of errors in transmissometer-derived concentration of suspended matter in the coastal zone: implications for flux determinations. Marine Geology, 169, 273–286. Jorissen, R., Litjens, J., and Menedez Lorenzo, A. (2000). Flooding Risk in Coastal Areas: Risks, Safety Levels and Probabilistic Techniques in Five Countries along the North Sea. Ministry of Transport, Public Works and Water Management, Koningskade 4, The Hague, the Netherlands. Leggett, D. J., Cooper, N., and Harvey, R. (2004). Coastal and Estuarine Managed Realignment—Design Issues. DEFRA/Environment Agency Report FD2413/TR. CIRIA, London.

K13587_C006.indd 121

10/12/2011 3:17:24 PM

122


Liedtke, J., Roberts, A., and Luternauer, J. (1995). Practical remote sensing of suspended sediment concentration. Photogrammetric Engineering & Remote Sensing, 61, 167–175. Lohani, B. (1999). Construction of a digital elevation model of the Holderness coast using the waterline method and Airborne Thematic Mapper data. International Journal of Remote Sensing, 20(3), 593–607. Manning, A. J. and Bass, S. (2006). Variability in cohesive sediment settling fluxes: Observations under different estuarine tidal conditions. Marine Geology, 235, 177–192. Martin, S. (2004). Introduction to Ocean Remote Sensing. Cambridge University Press, Cambridge, United Kingdom. Mobley, C. D. (1994). Light and Water. Radiative Transfer in Natural Waters. Academic Press, San Diego, CA. Nicholls, R. J., Wong, P. P., Burkett, V. R., Codignotto, J. O., Hay, J. E., McLean, R. F., Ragoonaden, S., and Woodroffe, C. D. (2007). Coastal systems and low-lying areas. In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson (eds.), Cambridge University Press, Cambridge, United Kingdom, pp. 315–356. Nicholls, R. J. and de la Vega-Leinert, A. (2008). Implication of sea-level rise for Europe’s coasts: An introduction. Journal of Coastal Research, 24(2), 285–287. Novo, E. M. M., Hansom, K. D., and Curran, P. J. (1989). The effect of sediment type on the relationship between reflectance and suspended sediment concentration. International Journal of Remote Sensing, 10(7), 1283–1289. Parsons, P., Mita, Y., and Lalli, L. M. (1989). A Manual of Chemical and Biological Methods for Seawater Analysis. Pergamon Press, Oxford, United Kingdom. Reading, C. J., Garbutt, A., Watts, C. W., Rothery, P., Turk, A., Yates, M., Boffey, C., Saunders, J., and Wolters, M. (2008). Managed Realignment at Tollesbury. CEH: Project Report Number C00356, R&D Technical Report FD 1922/TR. Department for Environment, Food, and Rural Affairs, London. Robinson, M.-C., Morris, K. P., and Dyer, K. R. (1999). Deriving fluxes of suspended particulate matter in the Humber Estuary, UK, using airborne remote sensing. Marine Pollution Bulletin, 37(3–7), 155–163. Sathyendranath, S. and Platt, T. (1997). Analytic model of ocean color. Applied Optics, 36(12), 2620–2629. Shimwell, S. J. (1998). Remote Sensing of Suspended Sediment off the Holderness Coast. Proudman Oceanographic Laboratory, Plymouth, United Kingdom. Smith, G. M. and Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20, 2653–2662. Smith, J. and Smith, P. (2007). Environmental Modelling. Oxford University Press, Oxford, United Kingdom. Sterckx, S., Knaeps, E., Bollen, M., Trouw, K., and Houthuys, R. (2006). Operational Remote Sensing Mapping of Estuarine Suspended Sediment Concentrations (ORMES), Hydro06—Evolutions in Hydrography, Antwerp, Belgium. Turner, K. M. (1995). Managed realignment as a coastal defense option. Biological Journal of the Linnean Society, 56, 217–219. van der Wal, D. and Pye, K. (2004). Patterns, rates and possible causes of saltmarsh erosion in the Greater Thames area (UK). Geomorphology, 61, 373–391. Wilson, A. K. (1997). An integrated system for airborne remote sensing. International Journal of Remote Sensing, 18, 1889–1901.

K13587_C006.indd 122

10/12/2011 3:17:24 PM