Mapping Posidonia oceanica from IKONOS

ISPRS Journal of Photogrammetry & Remote Sensing 60 (2006) 315 – 322 www.elsevier.com/locate/isprsjprs

Mapping Posidonia oceanica from IKONOS A. Fornes a , G. Basterretxea a , A. Orfila b,⁎, A. Jordi a , A. Alvarez a , J. Tintore a b

a IMEDEA (CSIC-UIB), Miquel Marques, 21, 07190 Esporles, Spain School of Civil and Environmental Engineering, Cornell University, 14853 Ithaca, NY, United States

Received 23 March 2005; received in revised form 21 February 2006; accepted 8 April 2006 Available online 27 June 2006

Abstract Posidonia oceanica is the dominant seagrass in the Mediterranean Sea that affects biological, biogeochemical and physical processes in Mediterranean coastal areas. The widespread loss of this species is attributed to excessive anthropic pressure and other large-scale environmental changes. Seagrass conservation requires mapping to estimate the extent of existing stocks and to measure changes over time. Optical remote sensing provides a cost-effective method to monitor vast areas of shallow waters that are potential P. oceanica habitat. As part of an interdisciplinary research effort, where the effects of these seagrasses on the hydrodynamics were investigated, new technologies of reliable, fast and effective monitoring of P. oceanica were essential. This paper presents a method for using IKONOS multispectral imagery for bottom classification in a shallow coastal area of Mallorca (Balearic Islands). After applying a supervised classification, pixels are automatically classified in four classes: sand, rock, P. oceanica bottoms and unclassifiable pixels. Results indicate that, in these clear waters, the spectral response of P. oceanica can be determined to a depth of about 15 m. In order to validate the method, the image classification is compared with a bottom classification derived from an acoustical survey. Agreement with the reference acoustic seabed classification is up to 84% for the sampled area. Spectral IKONOS image analysis is presented as an effective approach for monitoring P. oceanica meadows in most clear, shallow waters of the western Mediterranean. © 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. Keywords: Posidonia oceanica mapping; multispectral imagery; coastal classification; coastal management

1. Introduction Posidonia oceanica is the dominant seagrass in the Mediterranean Sea covering about 50,000 km2 of coastal sandy and, occasionally, rocky areas (Bethoux and Cópin-Motegut, 1986). P. oceanica develops millenarian ecosystems that affect the surrounding biological production, as well as the biogeochemical and physical

⁎ Corresponding author. Present address: IMEDEA (CSIC-UIB), Spain. Tel.: +34 971611834; fax: +34 971611761. E-mail address: [email protected] (A. Orfila).

processes in the littoral (Mateo et al., 1997). The dependence of seagrass performance on water quality is so high that seagrasses may be used as robust light meters that integrate water quality conditions over time scales of weeks to months, depending on the species, in coastal monitoring programs (Dennison et al., 1993). As with other seagrass species, they play an important role in many coastal processes, contributing to sediment deposition, attenuating currents and wave energy (Gacia et al., 1999) and stabilizing unconsolidated sediments (Fonseca, 1989). Seagrass meadows are also considered to be among the most productive ecosystems. They support diverse flora and fauna and provide nursery and

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breeding grounds for many marine organisms (Francour, 1997; Hemminga and Duarte, 2000). Recent studies note the importance of this seagrass in the stabilization of natural beaches in some Mediterranean areas (Basterretxea et al., 2004). P. oceanica appears to be experiencing widespread losses in the Mediterranean (Peirano and Bianchii, 1995) caused by anthropogenic impacts affecting water and sediment quality and other largescale environmental changes (Marbà and Duarte, 1997; Orfila et al., 2005). Seagrass meadows are highly sensitive to deterioration of water quality as evidenced, for instance, by the close relationship between maximum seagrass colonization depth and water transparency (Duarte, 1991), the regression of seagrass depth limits with increasing total nitrogen concentration in coastal waters (Borum, 1996) and the increasing loss of seagrass cover with increasing coastal nitrogen loading (Short and Burdick, 1996). Despite the importance of P. oceanica meadows, the distribution of this species has rarely been mapped in the Balearic Islands in detail. The distribution of P. oceanica is constrained by factors such as sediment suitability and stability, the amount of organic inputs to the sediment or nitrate and ammonium concentrations in the water column (Lopez et al., 1998; Hemminga and Duarte, 2000). In the vertical direction, the upper limit primarily depends on hydrodynamics (Ballesta et al., 2000), whereas the lower limit is a function of irradiance and underwater light attenuation. In the western Mediterranean, the meadows can extend from mean sea level down to a depth of about 40 m (Duarte, 1991). The recently reported widespread decline of P. oceanica in the Mediterranean (Marbà et al., 1996; Delgado et al., 1999) has mainly been attributed to an increasing anthropic pressure (Pasqualini et al., 1999) that is becoming a major concern for Mediterranean coastal managers. Indeed, P. oceanica is a protected species in the Natura2000 network within the European Union. Seagrass conservation programs and coastal management strategies have evidenced the importance of P. oceanica mapping. The rapid decline in the extent of seagrass requires new strategies for monitoring these changes. Consequently, fast, cost-effective and validated methods are needed to observe and monitor the distribution of P. oceanica. While the upper limit of P. oceanica meadows can be mapped from optical satellite or airborne techniques, at deeper or in turbid waters, an alternative technique such as acoustic sampling is necessary (Piazzi et al., 2000). There are some examples of comparisons between airborne photography and other mapping techniques (Pasqua-

lini et al., 1998; Piazzi et al., 2000). More recently, P. oceanica has been successfully mapped using a combined technique involving neural networks and airborne sensor data (Calvo et al., 2003). Despite some references concerning the use of satellite imagery for seagrass detection (Mumby et al., 1999; Pasqualini et al., 2005), there are few references using IKONOS satellite imagery for mapping P. oceanica. This paper examines the potential of IKONOS satellite multispectral image analysis as a fast and reliable method for mapping P. oceanica. The study assesses the possibility of discriminating P. oceanica meadows from sandy and rocky bottoms. Results of a classification system using IKONOS image analysis are compared with those of an acoustic classification using an echo-sounder equipped with a GPS in order to validate the method. 2. Study area The study area, Magalluf, is an embayment located in the southwestern coast of Mallorca (Balearic Islands). The coast extends in a SW–NE direction embracing a shallow water embayment with an estimated area of approximately 1.31 km2 , protected in its offshore opening by the island of Sa Porrassa (Fig. 1). The bathymetry presents a rather uniform and shallow embayment with depths less than 7 m. Magalluf is an appropriate study area to evaluate the potential use of IKONOS images for seagrass detection since the seabed is restricted primarily to seagrass meadows and sandy bottoms, and only occasionally hard bottoms composed by calcareous rests of P. oceanica or rocky bottoms. Since the bathymetry is uniform and shallow, a relatively large surface is detectable from the IKONOS satellite. The existence of a smooth bathymetry also facilitates acoustic detection, since spurious effects derived from strong gradients that affect the shape of the acoustic signal are marginal. 3. Data acquisition and processing To evaluate the potential use of IKONOS images in the detection of P. oceanica meadows, a cloud-free multispectral image was acquired on April 22, 2000 covering the southwestern coast of Mallorca. The spatial resolution of IKONOS images is 1 m for the panchromatic channel and 4 m for the multispectral channels. Concerning the spectral resolution, the satellite acquires data in four different channels, three in the visible (blue 0.45–0.52 μm, green 0.52–0.60 μm

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Fig. 1. Location and bathymetry of the study area.

and red 0.63–0.69 μm) and one in the near-infrared channel (0.76–0.90 μm). This image was geometrically corrected in the Universal Transverse Mercator (UTM) projection with the WGS84 datum and all channels were combined into a single image. The blue, green and red bands were displayed as blue, green and red colors, respectively, to generate a true color image (Lillesand and Kieffer, 2000). To discriminate between those pixels corresponding to the sea versus those that are land, a mask was superposed and a visual interpretation was performed to distinguish between possible P. oceanica, sandy and rocky bottoms. The rocky bottoms include rock fragments, calcareous rests of P. oceanica and zones with compacted sediment. For each visual region, a preliminary inspection of the spectral data was performed. Digital numbers (DN) corresponding to reflectance intensity values in a range between 0 and 255 were randomly analyzed by sampling

some pixels for each visual coverage. In the deepest part of the study area, very low reflectance values were registered and it was consequently considered as unclassifiable. After this preliminary inspection, a supervised classification was performed automatically categorizing all pixels into four classes (i.e., P. oceanica meadows, sand, rock and unclassifiable bottoms) following three steps. Firstly, the four categories observed in the preliminary inspection were digitally defined using training regions. This phase is usually known as the training phase. The statistics for each region were calculated for all categories and the spectral signatures diagram for each category was built (Table 1). While sandy bottoms showed a distinguishing spectral signature with high values decreasing from the blue to the near-infrared band, rocky bottoms and P. oceanica meadows presented a similar spectral behaviour. The main differences between these two types of bottoms

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Table 1 Values (mean ± S.D.) of the digital numbers (DN) for each bottom type and band obtained in the training phase Bottom type

Sandy bottom Rocky bottom Posidonia oceanica Unclassifiable

Bands Blue

Green

Red

Near-infrared

151 ± 21 18 ± 3 1±4 7±3

120 ± 27 27 ± 3 4±3 2±1

40 ± 29 13 ± 2 2±2 0±0

13 ± 4 9±2 4±2 1±1

can be summarized as; (1) rocky bottoms generally show higher reflectance than P. oceanica in all bands and (2) rocky bottom reflectance decreases from the red to the near-infrared band, whereas at the meadows values are similar. In deeper areas, very low reflectance values were registered, with many DN equal to zero, particularly at long wavelengths (red and near-infrared bands) and, thus, this region was considered as unclassifiable. In the second step, called the allocation phase, all pixels were assigned to one of the four defined categories. Although there are a wide variety of algorithms developed for supervised image classification, the Maximum Likelihood Classification (MLC) technique was adopted. The MLC has become a standard classifier in remote sensing data analysis and has been proved to be a robust algorithm to best fit the original localization of the data (Rees, 2001). Finally, in the last phase, results were verified in order to validate the methodology. The classified satellite image was compared with referenced data acquired by acoustic sampling, an underwater mapping technique that has been widely used and verified for bottom detection (Pouliquen and Lourton, 1992; Bakiera and Stepnowski, 1996; Preston et al., 2000; Orfila et al., 2005). From February 12 to February 21, 2001 an acoustic survey was done in Magalluf with a ship mounted Biosonics DE-4000 echo sounder equipped with a 200 kHz transducer. The draught of the boat allowed sampling depths of about 0.5 m. Echo sounding transects aligned both perpendicular and parallel to the coastline were sampled with a spatial resolution between transects of about 25 m. The acoustic pulse rate was set to 25 s− 1 and the sampling speed was set to 5.6 km/h, which allowed for a resolution of about 1 m along the transects. The acoustic signal was acquired simultaneously with the position recorded by GPS. Despite the several week time lag between the acoustic survey and the acquisition of the IKONOS image, it is assumed that there are no significant differences in the distribution of the seagrass since both observable growth and decay are larger time scale processes.

Given that the echo signal parameters depend on the bottom type (in particular its hardness and roughness), the water characteristics and the configuration of the equipment, an initial calibration with well known bottom types was performed prior the survey and verified by scuba diving. These calibration zones were carefully selected by divers giving close consideration to their homogeneity. The resulting more than 19,000 echo sounding sampling points were averaged (20 pings averaged to one output) and subsequently a low-pass filter was conducted. Information about the bottom type, which is encoded in the echo signal, was extracted using standard signal analysis algorithms. Four methods were employed for clustering the echo signals: (1) first echo cumulative energy curve (Pouliquen and Lourton, 1992); (2) first to second bottom echo ratio technique (Orlowski, 1984; Chivers et al., 1990); (3) first echo division method (Bakiera and Stepnowski, 1996) and (4) fractal dimension of the first bottom echo (Lubniewski and Stepnowski, 1997). Clusters in the acoustic classification were defined following the same classifications used for the IKONOS satellite image, i.e. sand, rock and P. oceanica bottoms. The most probable bottom type was chosen from the results of the four methods. Both remote sensing and acoustic data were incorporated into a Geographic Information System (GIS). The remote sensing data was incorporated as a thematic map, while the processed acoustic records were represented as points interpolated in a 4 m grid using an inverse distance weighting method with a nearest neighbour classifier. Finally, a digital bathymetric model of the area was created from the acoustical survey. Different kinds of analysis and overlay functions were applied to the GIS maps and the results were then compared. The accuracy of classified image was assessed using complementary measurements based on error matrices derived from the comparison between the classified and the acoustic maps. The matrix was used to calculate both the overall accuracies, and the specific category accuracies. 4. Results and discussion P. oceanica coverage, as depicted from the acoustical data (Fig. 2), was found to comprise 89% of the study area. Sandy and rocky bottoms encompass 10% and 1% of this area, respectively. Sandy bottoms typically appear at shallow depths near the beach, whereas rocky bottoms occur mainly in the southern coast of the bay as sparse patches within the meadow.

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Fig. 2. Survey lines from the acoustic sampling. The classification points are the result of the averaging, filtering and clustering of the sampling points.

Some of these patches correspond to areas where the Posidonia coverage has disappeared, but the underlying calcareous reef structure seems to increase. The percentage of area occupied by each bottom type, for different depth ranges obtained by overlaying the digital bathymetric model and the acoustic map, is shown in Table 2. Variations in the bottom types are found primarily near the shore at depths of less than 3 m where the Posidonia growth is restricted by hydrodynamics and sediment dynamics. The extent of each bottom type, as depicted from the satellite image, provides different values from those provided by the acoustic survey (48%, 8% and 4% for Posidonia meadows, sandy and rocky bottoms, respectively). However, these differences are caused by unreliable classifications at bottoms deeper than 15 m

Table 2 Percentage of bottom types for different depth ranges as obtained in the acoustic survey Depth range (m)

Sandy (%)

Posidonia (%)

Rocky (%)

0–3 4–6 7–9 10–12 >13

48 7 1 0 0

49 91 96 98 100

3 2 3 2 0

in the IKONOS image (Fig. 3). This limit roughly corresponds to an optical depth kz of 0.96. While the unclassifiable surface is around 40% of the total study area, echo sounding data indicates that these locations correspond primarily with P. oceanica. Table 3 displays the percentage of area occupied by each bottom type as measured by the results of the IKONOS image and the acoustic survey, excluding the unclassifiable area derived from the image analysis. In this case, the bottom type values provided by both methods yield very similar results, although significant differences still exist in rocky areas. These differences can be attributed to the intrinsic heterogeneity of this bottom type, which as mentioned before, is comprised of rock fragments, pebbles and calcareous rests of Posidonia that are often mixed with coarse sands, yielding a broad spectral signature. The distinct nature of the acoustic and optical technique (satellite based), one penetrating in the sediment, and the other reflecting the properties of the seafloor surface, can also lead to contradictory results in these substrates. This also occurs when fallen Posidonia leaves accumulate over other bottom types. To determine the accuracy of the classified image using the results of the acoustic map, a classification confusion matrix, also called an error matrix, was computed. Confusion matrices compare, on a category-

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Fig. 3. Bottom types resulting from the IKONOS image classification.

by-category basis, the relationship between known reference data and the corresponding results from an automated classification. Such matrices are square, with the number of rows and columns equal to the number of categories whose classification accuracy is being assessed. The confusion matrix included in Table 4 has been used to illustrate class agreement and error between the acoustic map (rows) and the classified image (columns) in greater detail. The percentage of area classified correctly (coincidental with the acoustic map) is on the diagonal elements of the confusion matrix, while errors (non-coincidental zones) are the offdiagonal elements. Residuals in the rows indicate the types of coverage, according the acoustic classification that did not match with the classified map, whereas residuals in columns indicate the coverage on the classified map that do not fit with the acoustic classification. Therefore, residuals represent omission Table 3 Percentage of area occupied by each bottom type from acoustic and IKONOS image data, excluding that part of the study area that is unclassifiable in the image analysis Bottom type

Acoustic

IKONOS

Posidonia oceanica Sand Rock

80 14 6

82 16 2

errors (wrongful exclusion from class-column entries) and commission errors (wrongful inclusion into classrow entries), respectively. The overall accuracy for the classified map (i.e., the area classified correctly assuming that the classification derived from the acoustic survey is accurate) is estimated from the confusion matrix. The producer's and user's accuracy for the P. oceanica class are 92.9% and 91%, respectively, so the probability that an area is occupied by this category on both the acoustic classification map, and the probability that a classified area actually represents this category based on the acoustic classification, is very high. For sandy bottoms, both the producer's and user's accuracy is lower (71.8% and 60%, respectively). Thus, the classification method can reasonably claim that of about 72% of those areas classified as sand, only 60% of these locations would actually have a sandy bottom. For rocky bottoms, both the producer's and user's accuracy is very low confirming that this is a conflictive class as suggested by the comparison of the percentage of area occupied by each bottom type (Table 3). The Kappa-statistics (K), defined as the difference between the actual agreement between reference data and an automated classifier and the chance agreement between the reference data and a random classifier, provides a measure of how well the categorization

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Table 4 Confusion matrix of the comparisons between IKONOS classification and acoustic sampling (units are m2) IKONOS

Echo sounding classification Sandy Posidonia Rocky Commission error (%) User accuracy (%)

Sandy

Posidonia

Rocky

75,808 31,824 18,720 40 60

28,848 580,608 28,880 9 91

896 12,320 2416 84.5 15.5

sample reflects the actual data. A zero value for K indicates that the agreement between reference data and the categorization is the same as it would be obtained by chance. The upper limit of 1 indicates that the agreement is perfect. K-values below 0.5 may suggest that the results of the accuracy assessment do not actually reflect the correctness of the data. The Kstatistics value obtained in the analysis is 0.52, which is lower than the overall accuracy (0.84). Differences between these two values are to be expected since each incorporates different forms of information from the error matrix. The overall accuracy includes only the data along the major diagonals and excludes the errors of omission and commission, whereas K-statistic incorporates the non-diagonal elements of the error matrix. In this study, this K-value indicates that the classified map provides a classification 52% better than a random classification. In summary, these results show that IKONOS image classification can be a valuable method for a rapid identification of seabed types in shallow waters. Even though airborne methods may offer higher resolution than satellite imagery, the lower cost of satellite based classification techniques makes it an attractive tool for the management of coastal waters. Moreover, despite the disadvantages associated with classifying seabed types in deep water, satellite based methods are practical for the detection of the upper limit of seagrass distribution, precisely where significant P. oceanica losses caused by human impact are expected to occur. Hence, satellite based methods should not be disregarded as a means to manage these resources. Major problems arise in the classification of heterogeneous substrates, which in this study included rocky bottoms. Further research is necessary to overcome some of these challenges. 5. Conclusions The general pattern of Magalluf Bay displays a submerged beach, with sandy bottoms extending between 90 and 100 m from the shore at a depth of

Omission error (%)

Producer accuracy (%)

28.2 7.1 95.2

71.8 92.9 4.8

approximately 3 m. The southern side of the bay is shallower with rocky bottoms and few sandy areas. A P. oceanica meadow extends from depths of 3 m to more than 25 m. Within the bay, patches of sandy and rocky bottoms are frequent, whereas further offshore (more than 13 m in depth) the distribution of Posidonia becomes uniform. P. oceanica shoot densities vary between 474 and 743 shoots/m2, a commonly observed shoot density in shallow waters throughout the Mediterranean. In these settings, IKONOS images appear to be a helpful tool for mapping P. oceanica meadows. This technique is particularly suitable when very large areas of seagrasses need to be assessed and monitored at a reasonable cost and direct observation is impractical. The automated classification of satellite imagery provides the most cost-effective solution since large areas can be classified quickly and relatively inexpensively. Results can then be analyzed together with other geo-referenced data using a Geographical Information System. The use of this method is restricted to clear and shallow waters. However, these are the areas where P. oceanica meadows are receding fast due to human activities thus making it important that they be monitored. If deeper or more turbid waters have to be surveyed, alternative techniques such as acoustic sampling should be used. In those cases, the use of the side scan sonar could be a better alternative. The use of a combination technique involving both IKONOS and echo sounding classification in an automatic training process is presently being investigated. Acknowledgments This work has been carried out as part of LIFE financed project POSIDONIA (LIFE00/NAT/E/ 007303) and Govern Balear financed project UGIZC. We grateful thank B. Casas for his valuable assistance with the echo-sounding sampling and post-processing and J. Llabres for the IKONOS image acquisition and processing.

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