RESEARCH ARTICLE Bangladesh Journal of Marine Sciences and Fisheries Vol.1, No., 1: 81‐96, 2009
ISSN 1992‐4445
Remote Sensing and GIS Application to Mangrove Forest Mapping in the Meghna Deltaic Islands of Bangladesh M. Shahadat Hossain1, Sam Wong2, M. Zahedur Rahman Chowdhury1 and M. Shamsuddoha3 Institute of Marine Sciences and Fisheries, University of Chittagong Chittagong 4331, Bangladesh. 2 Sustainability Research Institute, School of Earth and Environment University of Leeds, West Yorkshire, BD18 1DS, UK. 3 Participatory Research and Development Initiative Shyamoli, Dhaka 1215, Bangladesh. Corresponding author Email:
[email protected] Abstract The Meghna deltaic region consists of fluvial and tidal geomorphological deposits that has created the world’s largest delta in the form of coastal landscape and islands, which gives an opportunity to raise a complex mangrove ecosystem. The unsupervised technique algorithm ISODATA was operated, for spectral grouping of the TM image, where the supervised classification with maximum likelihood strategy was applied using the training areas from different islands. The accuracy of the image classification was measured by means of an error matrix that output the values of 0.87 and 0.96 for the Kappa and Tau coefficient, respectively. A total of 27,014 ha mangrove forest was identified and the spatial development has clearly indicated the location and extent of the mangrove forest in the islands. The results of the present study is a good example of integrated simultaneous top‐down and bottom‐up approach that combined information from remote sensing imagery, topographic maps, forest maps and field validation by multi‐disciplinary researchers. Accreted and marshy bare substratum, the suitable areas for mangrove plantation, cover about 15% of the islands which could be encouraged for the development of coastal green belt and to maximize socio‐ecological resilience of the islanders in minimizing the hazards of tropical cyclone. Keywords: Mangrove mapping, Landsat TM, climate change, tropical cyclone, resilience 1
Introduction Remotely sensed data have been popularly used with GIS in mangrove forest mapping for inventory and monitoring purpose in many parts of the world (Mausel et al., 1993; Green et al., 1998; Trisurat et al., 2000; Hossain et al., 2003; Kovacs et al., 2005; Hossain et al., 2007a; Kovacs et al., 2008). The increasing use of remote sensing techniques in mangrove forest mapping is possible because of the high reflectance values from forested areas in the near‐infrared, moderate reflectance in the middle‐infrared and low reflectance in the red spectral regions (Trisurat et al., 2000). Landsat TM data could be used to identify the primary mangrove forest (Sader et al., 1990), and different succession stages conveniently (Mausel et al., 1993). On this basis, a mangrove vegetation map could be an effective planning tool to show the spatial distribution of mangrove forest in the central coastal islands of Bangladesh. This is useful to develop greenbelt as natural barrier against natural disasters. Mangrove forest is considered as an extremely important resource, both ecologically and economically (Odum, 1971; Hossain, 2001; Kathiresan and Bingham, 2001, Hossain et al., 2007b). The fringe like root system of the mangroves act as a coastal stabilizer and binder of sediment and so aid in preventing erosion in the coastal areas (Dave, 2006; UNEP‐WCMC, 2006). Walton et al. (2006) reported that fish production related to replanted mangrove was 578‐ 2568 kg/ha/yr (US$463‐2215/ha/yr), which can equal that of brackish water aquaculture ponds. The annual economic value of mangroves, estimated by the cost of the products and services they provide, has been estimated to be $200,000 ‐ $900,000 per ha (Wells, 2006). Its storm buffering characteristic has become more evident after the tsunami that ravaged parts of Asia in December 2004 (UNEP‐WCMC, 2006; Dave, 2006; Quartel et al., 2007; Hossain et al., 2008). One of the greatest limitations to their protection is the lack of proper inventory and monitoring. Traditional techniques of in situ field measurements of these forested wetlands are extremely tedious and labour intensive given the typical inaccessibility of these systems, as well as limited mobility resulting from the maze of roots and stems, thick and unconsolidated substrate, and tidal flooding (Kovacs et al., 2008). Consequently, there has been a recent interest in the use of remotely sensed imagery, which can be acquired periodically and over very large geographical areas, for mapping and monitoring these often vast and remote wetlands (Green et al., 1998). A plethora of image processing methods are available to classify remotely sensed data of mangroves but weighing the relative merits of each is extremely difficult because unfortunately published reports rarely include an assessment of accuracy. Despite more than seventy years of application, it is difficult to obtain an overview of aerial photography for the
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assessment of mangroves, because published accounts are scarce. Further, the accuracy of the final map is affected by the ability of the classification procedure to discriminate the vegetation. Accuracy is important, because it is the criterion against which the success of an image processing method should be judged: no matter how innovative or sophisticated the classification procedure is, the value of any map is severely compromised if its accuracy is insufficient to fulfill the objectives. The initiatives based on a map of unknown accuracy may also lead to unnecessary or inappropriate action. The ability to correct this is partly a function of the sensors’ resolution, and partly a function of the image processing method or classification procedure adopted (Trisurat et al., 2000). Although the lack of accuracy in information processing is by no means unique to remote sensing work on mangroves, it appears to be a feature of remote sensing in the wider tropical coastal zone management context (Green et al., 1996). The objective of this study is to record and assess the spatial extent of mangrove forest and develop a mapping system in the coastal islands of Bangladesh by digital image processing techniques. Study area The Meghna deltaic region is located in between latitude 2150 and 2305 N and longitude 9030 and 9135E (Figure 1) consisting about 80 nearshore islands in the central coastal zone of Bangladesh. The geographical location of the study area is. The dominant soil types include muddy soil and sandy‐clay loam texture. The international rivers ‐ Ganges and Brahmaputra enter Bangladesh from western and northern sides respectively and then flow downstream as Meghna River into the Bay of Bengal. Since prehistoric times the region has been one of the areas of most active sedimentation in the world (Khan et al., 1998). Denudation of the Himalays resulted in the formation of the world’s largest delta which is still active at a rate of about 70 cm per one thousand years (Curry and Moore, 1971; Biswash, 1978). The accretion‐erosion process of the islands like Hatiya, Sandwip and Bhola has indications of strong sedimentary process in the estuary which constitutes about 12,800 km2 of inshore fish habitat (West, 1973; Khan et al., 1998). During field visit, the local residents mentioned that in recent years more accreted lands are visible in seaward direction during low tide. The tides are semi‐diurnal and tidal range is strong, ranging from 0.43 m at neap tide to 4.44 m at spring tide (Hossain, 2008). The mean annual rainfall is 2,547 mm, occurring above 95% during the monsoon season and the highest precipitation falls during May‐September while the lowest in October‐November and March‐ April. The mean annual minimum and maximum temperature is 23°C and 31°C with peaks of ~33°C during March‐June and the relative humidity varies from 72% in March to 87% in July.
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23° N BANGLADESH
LAXMIPUR NOAKHALI
22° 30΄ N
SANDWIP
BHOLA HATIYA
91° 30΄ E
91° E
22° N
Figure 1. Geographical location of the Meghna deltaic islands in the central coastal zone of Bangladesh, based on Landsat TM image of March 2007.
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Methods Satellite image Landsat TM image was acquired by the SPARRSO (Space Research and Remote Sensing Organization of Bangladesh) for 4 March 2007. The image was geometrically corrected and georeferenced to 1:10,000 scale coastal topographic maps of the Bangladesh Inland Water Transport Authority (BIWTA). The pixel resolution of the image was 30 m. Bands 1‐5 and 7 were used in the analysis, because these are considered the most useful for vegetation mapping (Kay et al., 1991; Manson et al., 2001). Geometric correction was performed with bilinear transformation (Research Systems Inc., 2000a) and the root mean square error 0.38 was controlled within less than one pixel (30 m) for 82 ground control points (GCPs). Road joints, level crossings of road, prominent features and buildings were selected as most of the GCPs in the image. Ground data were obtained for 105 plots by field investigation including some permanent ones. A spatial subset of 3770 X 5385 pixels (18,271 km2) was extracted from the TM scene covering the Meghna deltaic region in the central coastal zone of Bangladesh and masked the islands (4067 km2) from the surrounding water. In this area and period, the image was cloud free. All image processing was performed using ENVI (version 3.4) developed by Research Systems, Inc, USA. All maps and images were transformed into Universal Transverse Mercator (UTM) projection. Meter was defined for the software system as unit of the scale and the unit of the map. The GIS software used in this study was ArcView for windows (version 3.2) developed by Environmental Systems Research Institute Inc, USA. Image analysis Satellite image registration, masking the coastal islands, classification, accuracy assessment and post‐classification techniques were performed following the procedure in Figure 2. The unsupervised technique algorithm ISODATA (Tou and Gonzalez, 1974; Green et al., 1998; Trisurat et al., 2000; Hossain et al., 2007a; Hossain, 2008) was operated for spectral grouping of the same set of TM image, for comparison of the classified thematic maps. Minimum and maximum number of classes of 5‐10, 10‐15 and 15‐20 were assigned for each analysis. The convergence threshold of 95% and the maximum number of 3 iterations were selected to perform ISODATA clustering. After classification, the preliminary spectral classes were visually interpreted and compared with field data (existing forest maps and ground‐truth data). The preliminary classes were then merged subjectively to obtain meaningful classes of mangrove vegetation. A 3 X 3 pixel window of majority 1 was employed to reduce the satl‐and‐pepper effects in the classified image.
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Landsat TM March 2007 Control Point Selection Topographic map 1:10,000 Georeferencing Masking the Coastal Islands Identification of Training Fields ISODATA Supervised Unsupervised Classification Classification Max. Likelihood Accuracy Assessment Classification smoothing using a 3 X 3 filter and thematic maps output Post‐Classification Analysis Mangrove mapping Figure 2. Flow diagram of methodological procedure used to analyze mangrove vegetation in the Meghna deltaic islands of Bangladesh. Supervised classification has been the most frequent method by which remotely sensed data of mangrove areas has been classified (Green et al., 1998; Trisurat et al., 2000; Hossain, 2008). Field data have been used as training data. The training areas were selected throughout the study area in order to obtain good representatives for each island. Existing forest maps were used to identify the
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suitable training areas. Two to three sample plots of 1‐2 ha mangroves were chosen in different islands to use as training areas. Supervised classification were carried out on the basis of Region of Interest (ROIs), where the training areas (collected during field investigation) were regions of terrain with known properties or characteristics (Research Systems Inc., 2000b). Maximum likelihood classification strategy was applied and found to be most useful for discriminating the category of interest (Hord, 1982; Mather, 1987; Woodfine, 1991; Trisurat et al., 2000; Hossain et al., 2007a; Hossain, 2008). Field survey The acquisition of field data is required to supplement and verify features obtained from image processing of Landsat TM. After finishing the image analysis, 146 reference points in the study islands were chosen for ground verification. The reference points were surveyed for collecting data and comparing the preliminary map to the real world. The location of each field site was determined using Garmin map76CSx GPS. A preliminary map of the mangrove forest of different islands were thus corrected and revised. The mangrove map was finalized by using ArcView software. Accuracy assessment Accuracy of image classification was carried out by making comparison between classified image maps and existing land cover. A simple random sampling from different islands was performed to identify 146 sites for subsequent visit and assessment. Two weeks were set aside for the verification of GIS results. Accessible sample points were located with the help of a GPS, and the field checks were carried out for the accuracy assessment. For those samples located in remote islands or areas difficult to access, the forest maps were used as the reference data for the accuracy assessment, since the mangroves in these difficult‐to‐access areas have not changed. On‐the‐ground, verification is the most reliable and also the most time consuming. Such an approach is appropriate to verify individual sites after the GIS have been employed to identify the mangrove forest cover. The approach was to compare the locations and site‐ related performances of existing mangroves with locations provided by the GIS mapping. Consequently, the accuracy report is generated to calculate statistics of the percentages of accuracy based on the results of the error matrix using SPSS software (version 11.5). To assess the accuracy of image classification a standard error matrix was determined, using data from the output map as the rows, and the field data (ground truth points) as the columns in the matrix. The overall accuracy of the classification was calculated simply by summing the major diagonal entries of
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the matrix, which refer to the correctly classified units, and dividing this number by the total number of sample units in the entire error matrix. To measure the improvement of the classification over a random assignment of pixels, the Kappa and Tau coefficient and their 95% confidence intervals were calculated for testing significant differences (Congalton and Green, 1999; Ma and Redmond, 1995). Finally, to evaluate individually the accuracy of image classification for each class, Producer and User’s accuracies were estimated. Producer’s accuracy (PA) was measured as the ratio between the number of sampling units correctly classified in a given class by the total sampling units assigned to the same class in the reference data. User’s accuracy (UA) was calculated in a similar way, but the correctly classified units were divided by the total number of units classified in the same category. Both values were ways of representing individual accuracies instead of the overall accuracy (Congalton and Green, 1999). Results The results indicated that thematic classes derived from the supervised classification produced 90% accuracy, where the accuracy was 85% for unsupervised classification (Table 1). The accuracy of the image classification was measured by means of an error matrix and the values of 0.87 and 0.96 for the Kappa and Tau coefficient, respectively at 95% confidence were found. A total of 27,014 ha mangrove forest was identified and the spatial development has clearly indicated the location and extent of the mangrove forest in the islands (Figure 3). The only island district of Bangladesh, Bhola, occupied 388 ha mangrove forest. The Figure 3. Spatial distribution of mangrove forest in the Meghna deltaic islands of Bangladesh, other two sub‐district (locally based on classification of Landsat TM image of called Upazila) islands are March 2007. Hatiya and Sandwip having
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2,729 ha and 487 ha mangrove forest, respectively. Most of the mangrove forest lies in the south of Bhola (8,841 ha) and northwest of Hatiya islands (7,148 ha). A total of 2,634 ha mangrove forest is scattered in two islands of northwest Sandwip. The islands in the north and west sides of Bhola found no mangrove forest in the present study. Most of the mangrove forests in the islands are distributed in intertidal zone, with the range of elevation 0‐1 meter. The tide always influences mangrove ecology and distribution. An inundated area during high tides and a subsequent dry area during low tides around whole of the year is suitable for mangrove distribution. Three species of mangroves such as Sonneratia, Avicennia and Excoecaria were all locally called as ‘keora’, ‘baen’, and ‘gewa’ respectively are found in the islands. The euryhaline characteristic of both the species are favourable for their growth and distribution in the study area. Accurate discrimination was found from Landsat TM data and accuracy of image classification found better due to intensive image processing method. The result also suggests a possible correlation between image processing effort and accuracy. Unsupervised image processing method was relatively rapid, requiring the operator to do little more than edit the final classes but was the least accurate. The supervised classification procedure required greater effort from the operator during the process of signature editing. As a result supervised classification required the most effort as it was computationally intensive and also cost‐ effective: the extra investment of time produces a significant increase in accuracy. Table 1. Summary of overall accuracy of March 2007 Landsat TM image classification for mangrove vegetation from the Meghna deltaic islands of Bangladesh. Band combination Unsupervised (%) Supervised (%) 2,3,4 84.25 89.43 3,4,5 85.01 89.73 7,3,4 83.71 88.76 7,3,5 86.52 90.92 Overall 84.87 89.71 The pre‐dominant land cover classes that were found includes agriculture, grassland, mangrove forest, secondary succession, bare substratum, and canal/creek (Figure 4). After validation of the classification, the area covered by each of the selected categories was estimated (Table 2). Excluding the river and sea surface area (Meghna River and Bay of Bengal), the selected islands of study are mainly utilized by agriculture, almost 68% of the total area occupied. The second largest land coverage was bare substratum (15 %), with the remaining coverage’s amounting less than 10% each. The classification error matrix for the
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2007 Landsat TM image shows the incorrectly classified pixels, based on 146 ground truth points. Most points (131) were correctly classified, obtaining an overall accuracy of 90%. UA and PA accuracies for each of the classes showed that Bas (bare substratum) had the lowest PA (0.73), although its UA was as high as 0.94. Agriculture was well discriminated from the rest of the classes (PA = 1.00 and UA = 0.94). The mangrove forest was highly represented in the sampling (42/146) and UA reached 0.95, and the PA had values above 0.90 (Table 3). The Kappa (K) and Tau (T) coefficient had the values of 0.87 and 0.96 respectively at 95% confidence level. About 90% of the pixels were classified correctly, better than would be expected by a completely random classification. Table 2. Land cover categories used in the Landsat TM image for March 2007 interpretation. Land cover Description Area % categories (ha) Agriculture Soils with intensive agricultural production. 276,973 68 land Paddy cultivation during monsoon months. Part of the land used for vegetables in winter months Grassland Natural grasslands and wet meadows 11,923 3 Mangrove Hydrophilic and halophytic vegetation, 27,014 7 forest normally homogeneous, composed mainly by Sonneratia species and Avicennia species, located along the coastal belt and tidal flats Secondary Non‐arboreous vegetation, halophytic species, 23,518 6 succession and shrubs in areas further away from the canal/creeks, growing mainly on non‐ agriculture areas. Bare Low vegetated and un‐vegetated salt marshes 59,231 15 substratum and newly accreted land. Canal/creek Canals or creeks systems surface, navigational 8,136 2 route of the islanders. Waterways which criss‐ cross the islands, and drain water from the surrounding lands, play a vital role in carrying runoff from agriculture and homestead sources. Total 406,795 100
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Table 3. Accuracy assessment for a a supervised classification of March 2007 Landsat TM image from the Meghna deltaic islands of Bangladesh. Row Producerʹs Userʹs Reference data accuracy Classification data Agr Gra Maf Ses Bas Cac totals accuracy 33 Agr 2 0 0 0 0 35 1.00 0.94 15 Gra 0 4 0 0 0 19 0.88 0.79 40 Maf 0 0 2 0 0 42 0.91 0.95 Ses 0 0 0 16 6 0 22 0.89 0.73 Bas 0 0 0 0 16 1 17 0.73 0.94 11 Cac 0 0 0 0 0 11 0.92 1.00 Column total 33 17 44 18 22 12 146
Diagonal sum (bold) = 131; Overall accuracy = 0.897 Kappa agreement = 0.872; Kendallʹs tau = 0.962 Agr = Agriculture land, Gra = Grassland, Maf = Mangrove forest, Ses = Secondary succession, Bas = Bare substratum, Cac = Canal/creek Discussion In this study, technique of application, mapping the mangrove forest in the coastal islands of Bangladesh was explored using unsupervised and supervised classification techniques of Landsat TM data. It was found that the thematic classes from the supervised classification present better visualization. This is due to the fact that thematic classes of the classified map derived from digital techniques offer improved resolution of the mapping device. The results revealed that higher accuracy of image classification was achieved with the supervised classification method that coincided with Trisurat et al. Figure 4. Thematic map with area estimation of (2000), Alonso‐Perez et al. (2003) different land cover categories of the coastal and Hossain et al. (2003). This islands obtained by Landsat TM image March may be due to the analyst’s 2007 classification. 91
increased control in defining signatures for the classification decision rule (Joria and Jorgenson, 1996; Trisurat et al., 2000). Recent studies indicated that optical sensor data (e.g., SPOT, IKONOS) are more useful in extraction of mangrove forest properties for assessing the vegetation status (Kovacs et al., 2005; Kovacs et al., 2008). A digital elevation model is useful to correct topographic effects and would aid in increasing accuracy between mangrove and secondary succession vegetation of the islands. Unfortunately, such data are currently not available in most islands and requires substantial time and resources to generate at local condition. Thus, it was necessary to define simplified cover classes more suited to remotely sensed data capacities, such as Landsat TM. Moreover, reliable field data and ground truthing are essential for assessment of accuracy, because this technique minimizes errors, which may have occurred due to forest succession (Trisurat et al., 2000). However, the accuracy analysis showed that image classification was meaningful and significantly better than a random classification. The findings revealed that the landscape associated with coastal islands is defined as predominantly agriculture (68% of total area). The result shows that about 60,000 ha accreted and marshy bare substratum remained unutilized in the study area. As mangrove is being distributed along the coast for quite a long period, the local people utilize their experience of the local environment and the knowledge gathered from researchers in generating mangrove buffer zone with suitable species. The protective benefit of mangrove forest against tropical cyclone and wave action is important and well‐recognized (Siddiqi et al., 1992; Hossain et al., 2003; Badola and Hussain, 2005). Measurement of wave forces and modelling of fluid dynamics suggest that mangrove vegetation may shield coastlines from cyclone, storm surge and tsunami damage by dissipating incoming wave energy and reducing the erosion rates (Walton et al., 2006; Kerr and Baird, 2007). Besides, the wave‐driven, wind‐driven, and tidal currents also reduce due to the dense network of trunks, branches and aboveground roots of the mangroves. This latter can be seen as an increased bed roughness (Quartel et al., 2007). Analytical models show that 30 trees per 100 m2 in a 100 m wide belt may reduce the maximum tsunami flow pressure by more than 90% (Hiraishi and Harada, 2003). Human death greatly reduced by having mangrove forest, but damages to house and livestock loss were comparatively less responsive in Orissa coast of India during the super cyclone (T7 category) of October 1999 (Das, 2007). The monstrous tsunami of December 2007 killed 174,000 people and destroyed tens of thousands of buildings in Thailand, Indonesia, India, the Maldives and Sri Lanka. Thailandʹs Ranong areas were almost unaffected by the tsunami due to the resistance provided by luxuriant offshore mangrove forests. Effective governance structures, socioeconomic risk policies, and education
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strategies are needed to enable societies around the world to reverse the trend of mangrove loss and ensure that future generations enjoy the ecosystem services provided by such valuable natural ecosystems (Kavanagh, 2007). Acknowledgements This research was carried out as part of the Higher Education Link (HEL) Programme, supported by the British Council reference no BGL/BC‐HEL/2007‐ 08/NY of the “Conserving mangrove forests to mitigate the impact of climate change in the coast of Bangladesh”. As well as the authors, the fieldwork team included Ms. Fahmida Islam Munni, M. Mizanur Rahman, AZM Monjur Hossain, Ziaur Rahman, Abdur Rahim and Saiful Islam. COAST Trust, the local administration of Hatiya and Bhola Islands provided logistical assistance during fieldwork. Dr Andy Dougill, Mr. Sayedur Rahman Chowdhuruy, Dr. Rashed‐ Un‐Nabi and Professor Nani Gopal Das provided advice, inspiration and institutional support. We acknowledge all of this assistance with gratitude. References Alonso‐Perez, F., Ruiz‐Luna, A., Turner, J., Berlanga‐Robles, C.A and Mitchelson‐Jacob, G., 2003. Land cover changes and impact of shrimp aquaculture on the landscape in the Ceuta coastal lagoon system, Sinaloa, Mexico. Ocean and Coastal Management, 46: 583‐600. Badola, R and Hussain, S.A., 2005. Valuing ecosystem functions: an empirical study on the storm protection function of Bhitarkanika mangrove ecosystem, India. Environmental Conservation, 32(1): 85‐92. Biswash, A.K., 1978. Environmental implication of water development for developing countries. Water supply and Management, 2: 283‐297. Congalton, R.G and Green, K., 1999. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: Lewis Publishers, 137 p. Curry, J.R and Moore, D.G., 1971. Growth of the Bengal deep‐sea fan and denudation in the Himalayas. In: Submarine Canyon and deep sea fans, ed. J.H.M. Whitaker, pp. 236‐245. Hutchinson and Ross Inc. Pennsylvania. Das, S., 2007. Storm protection by mangroves in Orissa: an analysis of the 1999 super cyclone. SANDEE Working Papers, ISSN 1893‐1891; 2007‐ WP 25, pp. 66. Dave, R., 2006. Mangrove ecosystems of Southwest Madagascar: an ecological, human impact and subsistence value assessment. Tropical Resources Bulletin, 25: 7‐13. Green, E.P., Mumby, P.J., Edwards, A.J and Ur‐Clark, C.D., 1996. A review of remote sensing for tropical coastal resources assessment and management. Coastal Management, 24: 1‐40.
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