Application of Sentinel-2 Imageries for Study Seagrass Beds: A Case Study of Ao Kham, Haad Chao Mai National Park, Trang province
Abstract
Seagrass is an important producer in ecosystem. It is a habitat for aquatic animals that generate food and income for coastal communities. In conservation, restoration, and management of seagrass resources Accurate and up-to-date information is required. Sentinel-2 satellite imageries and field surveys (265 sample points) were used to map seagrass beds around Ao Kham in Haad Chao Mai National Park, Trang Province. Satellite data of wavelengths 2 (blue), 3 (green), 4 (red), and 8 (near-infrared), captured on 25 February 2021, were selected and the water column was corrected. The prepared data were then analyzed using supervised classification. The research found that the maximum likelihood classification technique was able to classify seagrass sites very well with the highest overall accuracy of 71.97% Kappa coefficient 0.53. The analyzed seagrass area was 456.25 Rai. The total is quite high. But in the muddy and sandy areas, seagrass remains. Because the seagrass species found at Ao Kham have small round leaves scattered in patches. Based on the results of this research, Sentinel-2 satellite imageries can be used to map seagrass beds even in small areas. Obtaining accurate and up-to-date seagrass data is critical to the conservation, restoration, and sustainable management of marine and coastal resources. Keywords : seagrass ; remote sensing ; satellite images ; Haad Chao Mai National Park ; SentinelReferences
Banko, G. (1998). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. doi:https://doi.org/10.1016/0034-4257(91)90048-B
Department of National Parks Wildlife and Plant Conservation. (2020). Survey and monitoring of sea grass resources in Hat Chao Mai National Park, Trang Province. (in Thai)
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., . . . Bargellini, P. (2012). Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36. doi:https://doi.org/10.1016/j.rse.2011.11.026
Edmund P. Green, P. J. M., & Clark, A. J. E. a. C. D. (2000). Remote sensing handbook for tropical coastal management. Paris: Unesco Pub.
English, S., Wilkinson, C., & Baker, V. (1998). Survey manual for tropical marine resources. Second edition: Australian Institute of Marine Science.
ERDAS. (1999). ERDAS Field Guide Fifth Edition, Revised and Expanded ™. United States of America.: ERDAS, Inc.
Fauzan, M. A., Kumara, I. S. W., Yogyantoro, R., Suwardana, S., Fadhilah, N., Nurmalasari, I., . . . Wicaksono, P. (2017). Assessing the Capability of Sentinel-2A Data for Mapping Seagrass Percent Cover in Jerowaru, East Lombok. Indonesian Journal of Geography, 49(2), 195-203. doi:10.22146/ijg.28407
Geo-Informatics and Space Technology Development Agency (Public Organization) Remote Sensing and GIS Association of Thailand. (2009). SPACE TECHNOLOGY AND GEO-INFORMATICS (Vol. 5000). Bangkok: Space Technology and Geo-Informatics. (in Thai)
Koedsin, W., Intararuang, W., Ritchie, R. J., & Huete, A. (2016). An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Sensing, 8(4). doi:10.3390/rs8040292
Lau, S. V.-K., Chen, C.-F., & Phuoc, H.-S. T. (2015). Mapping seagrass beds and coral reefs in the coastal of Ninh Hai district, Ninh Thuan province of Vietnam using Landsat 8 OLI image. Paper presented at the American Geophysical Union, Fall Meeting.
Magdalena, M.-K., Bringfried, P., Jerome, L., Vincent, D., Uwe, M.-W., & Ferran, G. (2017). Sen2Cor for Sentinel-2. Paper presented at the Proc.SPIE.
Meyer, C. A., & Pu, R. (2012). Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA. Environmental Monitoring and Assessment, 184(2), 1131-1143. doi:10.1007/s10661-011-2028-4
Nguyen, D. H., Ngo, T. D., Vu, V. D., & Du, Q. V. V. (2022). Establishing distribution maps and structural analysis of seagrass communities based on high-resolution remote sensing images and field surveys: a case study at Nam Yet Island, Truong Sa Archipelago, Vietnam. Landscape and Ecological Engineering, 18(3), 405-419. doi:10.1007/s11355-022-00502-0
Phuwachiranon, S., Adulyanukosol, K., Saelim, P., Charoenwattana, A., & Yamarunpattana, C. (2006). Seagrass in Thai waters. Limmmark Advertising Company Limited (Printing House), Phuket Province: Marine, Coastal and Mangrove Resource Research and Development Institute, Department of Marine and Coastal Resources Ministry of Natural Resources and Environment. (in Thai)
Prathep, A., tontiprapas, P., Jaroenjit, K., & Koedsin, W. (2018). Seagrass as a carbon sink : a role of seagrass in the world climate change. In. Thailand: Prince of Songkla University. (in Thai)
Traganos, D., & Reinartz, P. (2018). Mapping Mediterranean seagrasses with Sentinel-2 imagery. Mar Pollut Bull, 134, 197-209. doi:10.1016/j.marpolbul.2017.06.075
Tso, B., & Mather, P. M. (2009). Classification methods for remotely sensed data (2nd ed. ed.): CRC Press.
Veettil, B. K., Ward, R. D., Lima, M. D. A. C., Stankovic, M., Hoai, P. N., & Quang, N. X. (2020). Opportunities for seagrass research derived from remote sensing: A review of current methods. Ecological Indicators, 117. doi:10.1016/j.ecolind.2020.106560
Wongphanit, W. (2021). Seagrass sites in Thailand: biodiversity, ecosystem services and management practices. Bangkok: Marine and Coastal Resources Research and Development Institute. (in Thai)
Yamakita, T., Sodeyama, F., Whanpetch, N., Watanabe, K., & Nakaoka, M. (2019). Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand. Botanica Marina, 62, 291-307. doi:10.1515/bot-2018-0017
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. doi:https://doi.org/10.1016/0034-4257(91)90048-B
Department of National Parks Wildlife and Plant Conservation. (2020). Survey and monitoring of sea grass resources in Hat Chao Mai National Park, Trang Province. (in Thai)
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., . . . Bargellini, P. (2012). Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36. doi:https://doi.org/10.1016/j.rse.2011.11.026
Edmund P. Green, P. J. M., & Clark, A. J. E. a. C. D. (2000). Remote sensing handbook for tropical coastal management. Paris: Unesco Pub.
English, S., Wilkinson, C., & Baker, V. (1998). Survey manual for tropical marine resources. Second edition: Australian Institute of Marine Science.
ERDAS. (1999). ERDAS Field Guide Fifth Edition, Revised and Expanded ™. United States of America.: ERDAS, Inc.
Fauzan, M. A., Kumara, I. S. W., Yogyantoro, R., Suwardana, S., Fadhilah, N., Nurmalasari, I., . . . Wicaksono, P. (2017). Assessing the Capability of Sentinel-2A Data for Mapping Seagrass Percent Cover in Jerowaru, East Lombok. Indonesian Journal of Geography, 49(2), 195-203. doi:10.22146/ijg.28407
Geo-Informatics and Space Technology Development Agency (Public Organization) Remote Sensing and GIS Association of Thailand. (2009). SPACE TECHNOLOGY AND GEO-INFORMATICS (Vol. 5000). Bangkok: Space Technology and Geo-Informatics. (in Thai)
Koedsin, W., Intararuang, W., Ritchie, R. J., & Huete, A. (2016). An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Sensing, 8(4). doi:10.3390/rs8040292
Lau, S. V.-K., Chen, C.-F., & Phuoc, H.-S. T. (2015). Mapping seagrass beds and coral reefs in the coastal of Ninh Hai district, Ninh Thuan province of Vietnam using Landsat 8 OLI image. Paper presented at the American Geophysical Union, Fall Meeting.
Magdalena, M.-K., Bringfried, P., Jerome, L., Vincent, D., Uwe, M.-W., & Ferran, G. (2017). Sen2Cor for Sentinel-2. Paper presented at the Proc.SPIE.
Meyer, C. A., & Pu, R. (2012). Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA. Environmental Monitoring and Assessment, 184(2), 1131-1143. doi:10.1007/s10661-011-2028-4
Nguyen, D. H., Ngo, T. D., Vu, V. D., & Du, Q. V. V. (2022). Establishing distribution maps and structural analysis of seagrass communities based on high-resolution remote sensing images and field surveys: a case study at Nam Yet Island, Truong Sa Archipelago, Vietnam. Landscape and Ecological Engineering, 18(3), 405-419. doi:10.1007/s11355-022-00502-0
Phuwachiranon, S., Adulyanukosol, K., Saelim, P., Charoenwattana, A., & Yamarunpattana, C. (2006). Seagrass in Thai waters. Limmmark Advertising Company Limited (Printing House), Phuket Province: Marine, Coastal and Mangrove Resource Research and Development Institute, Department of Marine and Coastal Resources Ministry of Natural Resources and Environment. (in Thai)
Prathep, A., tontiprapas, P., Jaroenjit, K., & Koedsin, W. (2018). Seagrass as a carbon sink : a role of seagrass in the world climate change. In. Thailand: Prince of Songkla University. (in Thai)
Traganos, D., & Reinartz, P. (2018). Mapping Mediterranean seagrasses with Sentinel-2 imagery. Mar Pollut Bull, 134, 197-209. doi:10.1016/j.marpolbul.2017.06.075
Tso, B., & Mather, P. M. (2009). Classification methods for remotely sensed data (2nd ed. ed.): CRC Press.
Veettil, B. K., Ward, R. D., Lima, M. D. A. C., Stankovic, M., Hoai, P. N., & Quang, N. X. (2020). Opportunities for seagrass research derived from remote sensing: A review of current methods. Ecological Indicators, 117. doi:10.1016/j.ecolind.2020.106560
Wongphanit, W. (2021). Seagrass sites in Thailand: biodiversity, ecosystem services and management practices. Bangkok: Marine and Coastal Resources Research and Development Institute. (in Thai)
Yamakita, T., Sodeyama, F., Whanpetch, N., Watanabe, K., & Nakaoka, M. (2019). Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand. Botanica Marina, 62, 291-307. doi:10.1515/bot-2018-0017
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2023-05-11
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