Spatial distribution of submerged aquatic vegetation in  An Chan coastal waters, Phu Yen province using the PlanetScope satellite image

Nguyen Thi Thu Hang; Nguyen Thai Hoa; Nguyen Van Tu; Nguyen Ngoc Lam

doi:10.15625/0866-7187/41/4/14237

Author affiliations

Authors

Nguyen Thi Thu Hang Graduate University of Science and Technology, VAST, Hanoi, Vietnam
Nguyen Thai Hoa Department of Natural Resources and Environment of Phu Yen province, Vietnam
Nguyen Van Tu Institute of Tropical Biology, VAST, Hanoi, Vietnam
Nguyen Ngoc Lam Institute of Oceanography, VAST, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/0866-7187/41/4/14237

Keywords:

PlanetScope satellite image, seagrass, seaweed, SAV mapping, DII, BRI

Abstract

Seaweed and seagrass form marine submerged aquatic vegetation (SAV), which plays an essential role in economic development and ecological protection in coastal areas. In this study, PlanetScope (PS )imaging data was combined with in situ samplings to demonstrate their ability to map SAV distribution in An Chan commune, Tuy An district, Phu Yen province, Central Vietnam. Thanks to data pre-processing by Lyzenga’s algorithm and the masking in PS image allow us to remove partly the signals of spectral noises from sun glint effect as well as other random noises. The analysis and accuracy assessment of SAV classification by four different techniques: DII, enhanced DII, BRI and enhanced BRI were alternately performed. The overall accuracy in the accuracy assessment of SAV classification by the above techniques were alternately 83.33%, 88.58%, 86.17%, and 92.52% respectively. Kappa coefficients in the accuracy assessment of SAV classification by the above techniques were alternately 0.77, 0.84, 0.81 and 0.90 respectively. The results of SAV classification by enhanced BRI technique provided the best accuracies and will be chosen for assessing the distribution of Submerge Aquatic Vegetation (SAV) canopies in An Chan coastal waters from PS satellite image. The seagrass beds in An Chan is spread along the coast as well as lie close to the coast of islets. Whereas, the seaweed meadows lie in deeper waters and in the foot of the reefs in 3–4m deep. The total seagrass area in An Chan region was approximately 12.22 ha, with 10.93 ha seagrasses in My Quang, 1.18 ha in Hon Chua and 0.11 ha in Hon Dua. The total seaweed area in An Chan region was approximately 50.32 ha, with 20.20 ha seaweed meadows in My Quang, 22.8 ha in Hon Chua, 5.72 ha in Hon Dua and a small part of 1.60 ha in underwater small reefs.

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References

Bierwirth P., Lee T., Burne R., 1993. Shallow sea floor reflectance and water depth derived by unmixing multispectral imagery. Photogramm. Eng. Remote Sens., 59, 331–338.

Brando V.E., Anstee J.M., Wettle M., Dekker A.G., Phinn S.R., Roelfsema C., 2009. A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sens. Environ., 11, 755–770.

Brown C.J., Smith S.J., Lawton P., Anderson J.T., 2011. Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuarine, Coastal and Shelf Science, 92(3), 502–520.

Chauvaud S., Bouchon C., Maniere R., 1998. Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove). International Journal of Remote Sensing, 19(18), 3625–3639.

Chen C.F., Va-Khin L., Ni-Bin C., Nguyen Thanh S., Phuoc Hoang Son T., Shou-Hao C., 2016. Multi-temporal change detection of seagrass beds using integrated Landsat TM/ETM +/OLI imageries in Cam Ranh Bay, Vietnam. Ecological Informatics, 35, 43–54.

Congalton R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.

Conger C.L., Hochberg E.J., Fletcher C.H., Atkinson M.J., 2006. Decorrelating remote sensing color bands from bathymetry in optically shallow waters. IEEE Trans. Geosci. Remote Sens., 44, 1655–1660.

Department of Natural Resources and Environment of Phu Yen province (Phu Yen DONRE), 2014. Synthesis report of the project on building the system of information and materials on marine resources and environment in Phu Yen island , Phu Yen, Vietnam.

Hashim M., Nurul N.Y, Samsudin A., Komatsu T., Syarufuddin M., Nadzri R., 2014. Determination of seagrass biomass at Merambong Shoal in Straits of Johor using satellite remote sensing technique. Malayan Nature Journal, 66(1–2), 20–37.

Hedley J., Roelfsema C., Phinn S.R., 2009. Efficient radiative transfer model inversion for remote sensing applications. Remote Sens. Environ., 113, 2527–2532.

Ho H.P., 1969. Vietnamese seaweed; Learning Materials Center, Vietnam, 559p.

Klonowski W.M., Fearns P.R.C.S., Lynch M.J., 2007. Retrieving key benthic cover types and bathymetry from hyperspectral imagery. Journal of Applied Remote Sensing, 1, 011505.

Koedsin W., Wissarut I., Raymond J.R., Alfredo H., 2016. An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Senings., 8, 292p. Doi:10.3390/rs8040292.

Gašparović M., Medak D., Pilaš I., Jurjević L., Balenović I., 2018. Fusion of sentinel-2 and planetscope imagery for vegetation detection and monitoring. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 155–160, https://doi.org/10.5194/isprs-archives-XLII-1-155-2018.

Green E.P., Mumby P.J., Edwards A.J., Clark C.D., 1996. A review of remote sensing for the assessment and management of tropical coastal resources. Coastal Management, 24(1), 1–40.

Larkum W.L., Dekker A., Brando V., Anstee J., Fyfe S., Malthus T., Karpouzli E., 2006. Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors Seagrasses: Biology, Ecology and Conservation, Springer Netherlands, 347–359.

Le C.Y.L., Zha Y., Sun D., Huang C., Zhang H., 2011. Remote estimation of chlorophyll a in optically complex waters based on optical classification. Remote Sensing of Environment, 115(2), 725–737.

Lee Z.P., Carder K.L, Arnone R.A., 2002. Deriving inherent optical properties from water color: A multi-band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755–5772.

Lyzenga D.R., 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int. J. Remote Sens., 2, 71–82.

Lyzenga D.R., Malinas N.P., Tanis F.J., 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE Trans. Geosci. Remote Sens., 44, 2251–2259.

Mobley C.D., Sundman L.K., Davis C.O., Bowles J.H., Downes T.V., Leathers R.A., Montes M.J., Bissett W.P., Kohler D.D.R., Reid R.P., 2005. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and Look-Up Tables. Appl. Opt., 44, 3576–3592.

Mumby P.J., Clark C.D., Green E.P., Edwards A.J., 1998. Benefits of water column correction and contextual editing for mapping coral reefs. Int. J. Remote Sens., 19, 203–210.

Mumby P.J., Edwards A.J., Clark C.D., 1999. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. Journal of Environmental Management, 55, 157–166.

Phillips R., Mennez C.E.G., 1998. Seagrasses. Publication of Smithsonian institution: Washington D.C., 34, 105p.

Pu R., Bell S., Meyer C., Lesley B., Zhao Y., 2012. Mapping and assessing seagrass along the western coast of Florida using Landsat TM and EO-1 ALI/Hyperion imagery. Estuarine, Coastal and Shelf Science, 115, 234–245.

Pu R., Bell S., 2017. Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery. International Journal of Applied Earth Observation and Geoinformation, 54, 145–158.

Purkis S.J., Pasterkamp R., 2004. Integrating in situ reef-top reflectance spectra with LANDSAT TM imagery to aid shallow-tropical benthic habitat mapping. Coral Reefs, 23, 5–20.

Sagawa T., Boisnier E., Komatsu T., Mustapha K.B., Hattour A., Kosaka N., Miyazaki A., 2010. Using bottom surface reflectance to map coastal marine areas: A new application method for Lyzenga’s model. Int. J. Remote Sens., 31, 3051–3064.

Siregar V.P, Agus S.B., Subarno T., Prabowo N.W., 2017. Mapping shallow waters habitats using OBIA by applying several approaches of depth invariant index in North Kepulauan Seribu, 2016. IOP Conf. Series: Earth and Environmental Science, 149, 012052.

Spitzer D., Dirks R., 1987. Bottom influence on the reflectance of the sea. Int. J. Remote Sens, 8, 279–290.

Tassan S., 1996. Modified Lyzenga’s method for macroalgae detection in water with non-uniform composition. Int. J. Remote Sens., 17, 1601–1607.

Tien V.N., 2013. Resource of seagrass beds in Vietnam; Publisher of Natural Science and Technology: Hanoi, 346p.

Tsutsui I., Nang Q.H., Dinh H.N., Arai S., Yoshida T., 2005. The common marine plants of southern Vietnam. Japan Seaweed Association: Kochi, Japan, 250p.

Vietnam Red Book, Part II: Plants, 2007. Science and Technology Publishing House: Hanoi, Vietnam.

Yang C., Yang D., Cao W., Zhao J., Wang G., Sun Z., Xu Z., Ravi K.M.S., 2010. Analysis of seagrass reflectivity by using a water column correction algorithm. Int. J. Remote Sens, 31, 4595–4608. http://www.algaebase.org/.