Chapter 9 sediment characterization of intertidal mudflats using remote sensing

Sediment and Ecohydraulics: INTERCOH 2005 T. Kusuda, H. Yamanishi, J. Spearman and J.Z. Gailani (Editors) 9 2008 Elsevier B.V All fights reserved.

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Sediment characterization of intertidal mudflats using remote sensing Stefanie Adam a,*, Annelies De Backer b, Steven Degraer b, Jaak Monbaliu a, Erik A. Toorman a and Magda Vincx b aKatholieke UniversiteitLeuven, Department of Civil Engineering, Hydraulics Laboratory, Kasteelpark Arenberg 40, 3001-Heverlee, Belgium bUniversiteit Gent, Department of Biology, Marine Biology Section, Krijgslaan 281 $8, 9000-Ghent, Belgium ABSTRACT

In this paper an automated method for hyperspectral image classification is proposed. The method is based on a linear transformation of each spectrum in the hyperspectral cube. Different sediment types and land covers were classified using two dimensions of the transformed data space. The methodology is applied to hyperspectral images of the IJzermonding mudflat, acquired by the Compact Airborne Spectrographic Imager (CASI) in 2001 and 2003. Comparable classification results were obtained using a standard classification method employed in hyperspectral image processing. The similarity between classification results was 82 and 65% for the images of 2001 and 2003, respectively. The superiority of the proposed user-friendly method lies in its autonomy, reliability and objectivity. The proposed method uses the underlying statistical information of the data set, while the standard method is mainly based on expert knowledge.

1. I N T R O D U C T I O N Coastal regions are important from an ecological, coastal defence and economic point of view. These coastal areas and especially intertidal zones are increasingly at risk due to pressure from human development and climate change. Rising sea level and increasing storm frequency and intensity are likely to accelerate mudflat erosion, threatening the hinterland and its economic value. The process of sediment entrainment, transport and deposition depends on sediment characteristics. Cohesive sediments show a different erosion behaviour than non-cohesive sediments. Whereas bioturbation by macrofaunal species increases the erosion rate (Nowell et al., 1981; Hall, 1994), cohesive sediments with a biofilm of microphytobenthic algae are less susceptible to erosion (Bryant et al., 1996; Mitchener and Torfs, 1996; Austen et al., 1999; Andersen and Peirup, 2002; Carr6re, 2003). Bio-physical characteristics of intertidal zones are usually estimated or derived from field measurements by the application of interpolation techniques on point measurements. However, field measurements are spatially unrepresentative, especially for dynamic and heterogeneous intertidal mudflats. Furthermore, field observations are seriously restricted by the limited accessibility and the short exposure time between tides. Remote sensing * Corresponding author: E-mail address: [email protected]

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offers a means for the collection of area-covering data. In particular, hyperspectral airborne remotely sensed images are promising for the study of intertidal zones, because of their superior spectral and spatial resolution, and operational flexibility. Researchers have tried to use remotely sensed images to characterize intertidal sediments. A supervised and unsupervised classification method of sediment and vegetation types of the Humber estuary gave qualitative results. An accuracy assessment could not be performed due to poor field data on the study site (Thomson et al., 1998). However, other researchers have developed an empirical model for the quantitative estimation of sand and mud content (Rainey et al., 2003). In this study a spectral linear unmixing procedure, using collected endmembers which are spectra of pure materials, was applied on hyperspectral airborne imagery after which an empirical model was calibrated and validated using a significant amount of field data. The accuracy and prediction potential of the model were expressed as a correlation coefficient. The clay distribution could be mapped based on the mud abundance image with a correlation coefficient of 0.79. In the sand distribution model the correlation was lower (r 2 = 0.60). Disadvantages of this kind of studies are the need for field data and the site and image dependency of the empirical model. Therefore, a more automated method that requires little field knowledge would be helpful in exploring intertidal mudflats. This paper describes the extraction and interpretation of information from hyperspectral images of mudflats. In order to achieve this objective, two classification methods are used. The first classification procedure is based on empirical orthogonal functions. As a reference, a standard classification procedure is also applied.

2. STUDY AREA The IJzer is a relatively small stream in the western part of Belgium with a short intertidal zone at the North Sea. Although human influence is considerable, the IJzer estuary is of high ecological value. The dunes extend far into the hinterland, and a small part of the fight shore of the IJzer has never been artificially protected by constructions, assuring the presence of an intertidal flat with continuous transitions between beach and marsh, and marsh and dunes. The nature reserve 'De IJzermonding' (Fig. 1) consists of the marshes and dunes protected since 1961, the former base of the army, the beach and surrounding areas, making a total of 103 ha. The old naval base of Lombardzijde became a property of the Flemish government in 1998. It consists of heightened terrain, a slipway, docks and some buildings and roads. A nature restoration project was developed by the University of Gent, by the order of AMINAL (Hoffman et al., 1996). The general aim of the initiative was to restore or create beach-dune-salt marsh ecotones with salt-fresh, dynamic-stable, wet-dry and mud-sand ecotones (Hoffman et al., 2005). The restoration works started in 1999. During the monitoring phase between 2000 and 2004, high stability was observed in the southern parts of the nature reserve where no significant changes occurred. The areas near the outlet of the river that were created by the restoration works have been eroding and seem to be evolving towards a new state of equilibrium (Hoffman et al., 2005). Some parts of the intertidal zone are covered by a biofilm of microphytobenthos, suggesting cohesive properties or the presence of a large amount of silt (>10-15%), as

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3. METHODS 3.1. Image data set Hyperspectral images of the IJzermonding were acquired in August 2001 and June 2003 by the Compact Airbome Spectrographic Imager (CASI). The images will be called CAS12001 and CASI 2003. CASI is a hyperspectral optical sensor measuring the reflectance in visible (VIS) light and near infra red (NIR) region in many very narrow contiguous bands (Fig. 2). The details of the images are shown in Table 1. The spectral resolution is expressed as the

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2001/8/27/13/55 UTC time low tide, after a considerable time of air exposure 2 m pixel size 3300 ft 430-971 nm 96 bands (FWHM = 6 nm) 8 bit IJzermonding not complete; only one flight line

2003/6/16/11/38 UTC time 2 h after low tide 2 m pixel size 3300 ft 408-944 nm 48 bands (FWHM = 11.7 nm) 8 bit IJzermonding complete, but partly flooded, due to late overpass of airplane

number of bands and the full width at half maximum (FWHM), which is the full width at 50% of the peak height of the spectral response of the sensor to a monochromatic source. The images were radiometrically, atmospherically and geometrically corrected by VITO (Vlaamse Instelling voor Technologisch Onderzoek; Flemish Institute for Technological Research). The quality of images is usually expressed as the signal-to-noise ratio (S/N). This value is dependent on the wavelength and on the amount of radiation coming from the surface, so each band should have several S/Ns for different surface types. A range of S/N for each band is usually given by the image provider, but this was not the case for these images. Since the two sensors are both CASI-2 sensors, the image quality should not be too different. However, CAS12003 has got broader bands, and thus longer integration times and higer S/Ns. The S/N of each band of CASI 2001 was estimated for a homogeneous water surface

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of more than 100 pixels, and the bands were also visually checked. It was decided that the first eight bands (from 430 up to 475 nm) and the last 13 bands (from 896 up to 971 nm) be removed due to stripes, low S/N or bad visibility. The bands in CASI 2003 all had good S/N and quality, but for consistency, the wavelengths which were removed from CAS12001 were also removed from CASI 2003. To minimize the spectral complexity of the image, the regions of interest are isolated from all other spectral features, such as the agricultural fields, port, beach and buildings, and water. The water is excluded by the application of a mask with a threshold in a NIR band, since water absorbs almost all NIR radiation.

3.2. Method based on empirical orthogonal functions

The vector character of most remote sensing image data renders it amenable to spectral transformations which generate new sets of bands. These components then represent an altemative data description, in which the new components of a pixel vector are related to its old brightness values in the original set of spectral bands via a linear operation. A commonly used linear transformation is the Principal Component Transformation (PCT) or Principal Component Analysis (PCA), which finds a new set of orthogonal axes with their origin at the data mean. These are rotated so that the data variance is maximized. PCT is a powerful technique, which decorrelates bands so that maximum information is explained by a few bands. The classification of hyperspectral images can then be based on these bands. For the intertidal zone, the cumulative contribution ratio of principal components (PCs) 1 and 2 accounted for 99.2% for both images. The most important classes present in an intertidal zone are vegetation on the stabilized dunes, silt, sand and mixed sediment (sand and silt). These classes show distinct properties in the NIR reflectance and red absorption, caused by the presence or absence of green pigment. It is expected that the first two PCs explain the variability in the NIR and the red reflectance. The first PC differentiates between classes with high NIR reflectance, namely vegetation and sand, and classes with low NIR reflectance such as silt and mixed sediment. The second PC distinguishes between classes with green pigment such as vegetation and silt with microphytobenthos and classes without green pigment, namely sand and mixed sediment. High correlations were found between PC1 and NIR reflectance and between PC2 and red reflectance (Fig. 3). Since the threshold values for class discrimination for PC1 and PC2 are set to zero, the combination of the two PCs enables separation into four quadrants or classes (Fig. 4). The spectrum of vegetation has high intensities in the NIR region and an absorption feature in the red light (low intensities in the red, but high value on PC2); therefore, the cluster of vegetation pixels will be situated in the first quadrant of the scatter plot of pixels of PC1 versus PC2. Sand shows high reflectance in the NIR region, but there is no absorption feature of the red light. Consequently, sand pixels are expected in the fourth quadrant. The spectrum of silt with algae shows relatively low NIR reflectance and an absorption feature in the red light, so these pixels are situated in the second quadrant. The fourth class contains pixels with mixed sediment composed of sand mixed with varying amounts of silt without absorption features and low NIR reflectance, so these pixels are found in the third quadrant.

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A common method used for the classification of hyperspectral images consists of several consecutive steps" 1. minimum noise fraction (MNF) transformation to reduce the dimensionality and to segregate noise (Green et al., 1988). 2. collection of endmembers from the image by the determination and visualization of pure pixels in n dimensions. When pixels are plotted in a scatter plot using image bands as plot axes, the spectrally purest pixels always occur in the corners of the data cloud, while spectrally mixed pixels always occur on the inside of the data cloud. Pure pixels are determined by repeatedly projecting the n-dimensional scatter plots of the MNF bands onto a random unit vector. Pixels with the largest number of positions at the ends of the unit vector are the purest. The endmembers are visually selected by identifying all the corners of the pure pixel cloud in n-dimensional space. Endmembers correspond to pixels that contain one pure, particular material. 3. spectral angle mapper classification that determines the similarity of the spectrum of each pixel with the spectrum of the endmember expressed as a spectral angle between endmember and pixel spectrum. The pixel is assigned to the class for which it has the lowest spectral angle. A threshold angle for each class defines the minimum required similarity between pixel and endmember. Pixels further away than the threshold angle for each endmember are not classified.

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4. RESULTS AND DISCUSSION 4.1. Classification using PCA

Four regions of interest are delineated using the four quadrants of the scatter plot of PC 1 versus PC2 for CAS12001. A classification image (Fig. 5) is built on these four classes as explained in Section 3.2. The spectral characteristics of the classes and field knowledge are used as the basis for identifying and labelling pixel clusters. Field knowledge reveals misclassification between silt with microphytobenthic algae and vegetation. Sparse vegetation was often misclassified as silt, as shown in the circled area. Both classes have a red absorption feature, but the NIR reflectance of vegetation is higher than the NIR reflectance of the silt pixels. The classification result of CASI 2003 shows the identification of a new class, namely another vegetation type. This type of vegetation is distinct from the former vegetation type because of the lower NIR reflectance. It is recognized in the image as sparse vegetation on wet sediment. The lower NIR reflectance might be due to the influence of bare sediment. The small regions of this type of vegetation in CASI 2001 were misclassified as silt with microphytobenthic algae. The third quadrant (Fig. 6) consists of two distinct classes, namely silt and mixed sediment. These classes are separated on the same principle: a PCT on the pixels of the third quadrant followed by classification based on two PCs. This method is called 'hierarchical' PCA (Fig. 6). The identification of this class in CASI 2003 and not in CASI 2001 is caused by the large area of sparse vegetation covered in 2003 and excluded in 2001. The influence of these pixels on the statistics is considerable and changes the contribution of each spectral band in the formation of PCs. This result indicates that some previous knowledge about the area is necessary to perform the analysis. The number of classes and main spectral characteristics should be known.

4.2. Classification using the standard method

The classification results using the standard method are shown in Fig. 7. After application of the MNF transformation, almost all the information in the original bands is represented by the first few MNF bands. The spatial coherence of each of the first 12 MNF bands is large, indicating that each band contains information about a spectral class in the image. Therefore, it is concluded that the inherent dimensionality of the data is 12, meaning that 12 different endmembers should be identified. However, it was impossible to find 12 endmembers during the time-consuming and repetitive process of endmember collection using the visualization tool of the pure pixels in n dimensions. Four endmembers, that is vegetation, silt, sand and mixed sediment, could be selected for CASI 2001, and five endmembers including vegetation 2, for the CASI 2003 image. The SAM algorithm was applied to the image using these endmembers. In the default settings the threshold angle for all classes is 0.10 rad. To improve the classification result, the threshold angles can be changed by the image interpreter based on terrain knowledge. Our best results were obtained with the following angles for CASI 2001 and CASI 2003, respectively: vegetation 1:0.30 and 0.13 rad; silt: 0.25 and 0.10 rad; sand: 0.10 and 0.06 rad; mixed sediment: 0.10 and 0.25 rad and vegetation 2:0.11 rad for CAS12003 (Fig. 7).

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4.3. Discussion

The overall similarity between the classification results of both methods was 82% for CASI 2001 and 65% for CASI 2003. For CASI 2001, differences were found mainly between the results for sand and silt. The other classes show high similarities between the CASI method and the standard method. For CASI 2003, the differences were much larger, mainly between the two vegetation classes, between vegetation and mixed sediment and between silt and sand (Table 2). Since there is too little ground reference available, it is not possible to decide which method is the most accurate. It is also difficult to compare classification results for the two images, since the spatial coverage and spectral resolution are different. More research and field work should be performed to estimate the accuracy of both methods. However, classification procedures show distinct differences. The standard method is time consuming for calculating pure pixels and collecting endmembers, and is very subjective in collecting endmembers and defining threshold angles. In this study the threshold angles were chosen so that a minimum amount of pixels remained unclassified and the classification results corresponded to field knowledge and PCA classification results. The standard method is mainly based on expert knowledge of the terrain and the image and on trial and error. Results are not reproducible, since an image interpreter will not obtain identical results in two procedure runs starting from the same situation. Automation of the method is not feasible, because of endmember extraction from the image itself. The classification method using PCA is objective and robust, and it can be automated in few steps. The procedure is fast and easy to perform, and results are also physically interpretable. Background information about class number and class spectral characteristics is necessary. Therefore, the method is considered to be a semi-supervised classification method. In order to better understand the standard classification procedure, the selected endmembers of CASI 2001 and CASI 2003 are plotted (Fig. 8a, b). The following spectral characteristics can be observed: (i) sand: high reflectance in the VIS and NIR regions, (ii) vegetation 1: red absorption feature and high NIR reflectance, (iii) mixed sediment: low overall reflectance, (iv) silt: low overall reflectance and a small absorption feature in the red light and (v) vegetation 2: similar spectrum as vegetation 1, but with lower NIR reflectance.

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for an average class pixel. The sand endmember, e.g. shows very high overall reflectance (up to 40 and 50%); however, most sandy pixels do not show such high reflectance values. Therefore, it was investigated if the use of the mean spectrum of each class resulting from the PCA classification in SAM leads to a more robust classification (Vitse, 2005). The mean spectra for CASI 2003 are shown in Fig. 9. It can be seen that the overall reflectances for sand are lower and that the vegetation spectrum shows lower values in the NIR region and higher values in the VIS region. The spectra for silt and mixed sediment are similar. Using these spectra for SAM classification leads to a classification result comparable with the PCA classification results. This is expected since the endmembers are derived from the PCA classification result. However, an important feature is the lower sensitivity of the result to the chosen threshold angles, improving the SAM classification procedure if little field information is available. Further research will focus on the application of clustering techniques on important PCs. Since our interest is the classification of sediment types, the vegetated areas will be removed.

5. BIOTURBATION Within the framework of the recently started CISS project (Bio-physical Characterization of Indicators of Sediment Stability in mudflats using remote sensing, 2005-2008), the Hydraulics Laboratory of the KULeuven and the Marine Biology Section of the University

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of Ghent are collaborating to investigate the influence of sediment properties on erodibility and on spectral properties. The main objective of this research is the quantitative assessment of bio-geophysical characteristics of the surface sediments in the intertidal zone using remote sensing and the determination of the erodibility of these sediments based on their bio-geophysical characteristics. Erosion experiments both in laboratory and in field conditions are performed to determine the erodibility expressed as critical shear stress and erosion rate of the sediment. In the laboratory a classical erosion flume is most appropriate, since suitable flow conditions can be established and controlled relatively easily. An in situ flume will be used in field experiments. Sediment beds with different physical and biological parameters will be prepared and used in erosion experiments. Biological factors like microphytobenthos and macrofaunal species will be applied in close collaboration with the Marine Biology Section of the University of Ghent, where these species are cultivated. Within the framework of complementary PhD research at the Marine Biology Section, the influence of the macrofaunal species Corophium volutator on sediment is investigated. C. volutator is an important species in mudflats all over the world. It exists in the upper intertidal zone on the mudflat-salt marsh edge. Corophium lives in U-shaped burrows in the upper 5 cm of the sediment (Flach, 1996), where it feeds mainly on microphytobenthos (especially diatoms) scraped off the surface into its burrow, with its big second antenna. This species can reach very high densities (10,000-100,000 individuals/m2), especially in the period May-October (Murdoch et al., 1986). If Corophium is present in such high densities and it burrows, feeds, ventilates its burrow and crawls around, there has to be an impact on the bio-geophysical environment. It is not clear what the influence of Corophium is on sediment stability. Opposite effects are attributed to the activity of Corophium. On the one hand, Corophium weakens the sediment by grazing on mucus-producing diatoms (Gerdol and Hughes, 1994; Grant and Daborn, 1994; de Deckere, 2003); on the other hand, stabilization is assumed because sediment particles and secretions are used to build the burrow (Meadows et al., 1990). This could result in a reduced availability of suspended particles and eventually in elevated parts on the mudflat (Grant and Daborn, 1994; Mouritsen et al., 1998). De Deckere (2003) showed a clear seasonal pattern in sedimentation and erosion in intertidal zones as a result of biological processes: net stabilization in spring by the developing diatom film, later undone through bioturbation of macrobenthos, such as Corophium. It is suggested that the activity of Corophium, together with the hydrodynamics, arouses the right sedimentary condition for the survival of mudflats. Corophium would act as a habitat engineer and prevent the germination of pioneer plants on the mudflat-salt marsh edge, counteracting the natural evolution in the upper intertidal zone. Since the bioturbation of Corophium has an important influence on the sedimentary condition, the biological objective of the CISS project is to unravel this process in detail: 1. Quantify the bioturbation effect of Corophium: which layers are impacted and in what way? 2. Qualify the bioturbation effect of Corophium: how are the different layers impacted? 3. Evaluation of the sedimentary condition as a result of the presence of Corophium. The interaction of light with sediment is investigated and modelled by analyzing the results of controlled experiments in a dark room. Series of soil samples will be spectrally characterized

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using the Analytical Spectral Device (ASD). A mathematical procedure will be developed to extract the bio-geophysical characteristics of sediments based on its spectral characteristics. The developed models will be tested in the field and on hyperspectral airborne imagery.

6. CONCLUSION A classification procedure was developed for intertidal mudflats based on an empirical orthogonal function, namely the PCT. The underlying mathematical basis of the method was studied, so that the proposed classification procedure could be easily interpreted and understood. The PCT method was applied to two hyperspectral images. Inherently, the cumulative contribution ratio of the two first PCs is very high, so only these two bands were used for the classification of the images. The first PC caught the information in the NIR region, and the second PC caught the information in the VIS light and more precisely the important red absorption feature. A hierarchical principal component classification was suggested, if more than four classes were present in the image. A standard method of hyperspectral image classification was applied to classify the images. Large differences were noted between the results of both methods. An accuracy assessment could not be performed due to lack of ground data. However, the proposed method is superior with regard to user-friendliness, repeatability, ground truth requirements and physical interpretability. Moreover, it is concluded that the endmembers selected in the standard procedure are not representative of the class. It is suggested that a more objective classification can be performed using the mean spectra of the PCA classification. Within the framework of the CISS project, an entirely new type of classification method is currently under development, and spectral fingerprints of bioturbation (by Corophium) will be identified. Eventually, a usable correlation with erodibility is hoped to be achieved, which can generate erosion threshold maps from airborne images of intertidal mudflats, usable for morphodynamic studies. Results of this ongoing research will be presented at a later stage.

ACKNOWLEDGMENTS The images were acquired from the Natural Environment Research Council (NERC), UK. The radiometric, atmospheric and geometric correction was done by the VITO. This work was supported by the Belgian Science Policy (Federaal Wetenschapsbeleid) in the flamework of the PRODEX Experiment Arrangement between ESA and the Katholieke Universiteit Leuven, project C90164 'The IJzer estuary'. We would like to thank the Institute for Land and Water Management (ILWM) of the KULeuven and the Flemish Marine Institute (VLIZ) for the equipment loaned during field work and the Flemish Institute for Technological Research (VITO) for the preprocessing of the images. The CISS project (2005-2009) is funded by the Flemish Fund for Scientific Research (FWO Vlaanderen) under contract no. G0480.05. The fifth author's position as a research associate is financed by the KULeuven Special Research Fund.

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