SW (Seaweed) is a valuable coastal resource for its use in food, cosmetics, medicine and other items. In this study, PS (PlanetScope) imagery was combined with field sampling to demonstrate the capability of mapping of SAV (Submerge Aquatic Vegetation) (including both SW and SG (Seagrass) beds) and biomass mapping of Sargassum meadows in An Chan coastal waters, Tuy An district, Phu Yen province, Vietnam. In term of SAV and Sargassum mapping, authors proposed an improved remote sensing technique based on Sagawa's BRI (Bottom Reflectance Index) algorithm with attention to Tassan's concept in discrimination of light attenuation coefficient K d between shallow and deep waters. Authors' results showed high accuracy in mapping of SAV and Sargassum distribution with overall accuracy and Kappa coefficient of 92.52% and 0.8957, respectively. The classified class of SW (i.e. Sargassum sp.) then was separated absolutely from other classes in SAV map for estimation of Sargassum biomass. The red and green spectral pre-processed BRI channels (i.e. BRI 3 and BRI 2) of PS were used to estimate the Sargassum biomass using a multiple 2nd order polynomial regression model with very high accuracy (R 2 = 0.9707; RMSE = 109.21 g/m 2). The average total Sargassum biomass was 897.8 g/m 2 with total Sargassum yield in whole region reaching a value of 449.57 tons in cover area of 50.32 ha of Sargassum meadows. This result opens the great potential of biomass and yield estimation of Sargassum and other SW meadows in coastal waters (including enough optically deep waters) by remote sensing techniques based on PS imagery.
Oysters are nutritious food organisms, rich in protein, minerals and vitamins. Production from Pacific oyster (Crassostrea gigas) aquaculture is increasing and supports the livelihoods of coastal communities. To ensure both success and long-term sustainability of oyster production, the determination of suitable sites is an important step in any aquaculture operation. This study applied GIS (Geographic Information System) based MCE (Multi-Criteria Evaluation) for locating suitable sites for Pacific oyster (Crassostrea gigas) farms in coastal regions in Central Vietnam. Remote sensing data were obtained viaMODIS (Moderate Resolution Imaging Spectroradiometer) and high resolution imagery from the Google Earth Engine, while oceanic data were obtained from HYCOM-NCODA (Hybrid Coordinate Ocean Model-Navy Coupled Ocean Data Assimilation) coupled with local hydrodynamic FEM (Finite Element Model) Hydrographic charts and GPS (Global Positioning System) data were used to extract required information layers for GIS based MCE for the suitable site selection of oyster farms. Six thematic layers of biophysical parameters were used to analysis MCE non-constraints including the depth, temperature, salinity, Chl-a (Chlorophyll-a) content, suspended matter concentration and velocity of sea current. Then, an MCE analysis with constraint was used to exclude the areas from suitability maps where oyster aquaculture could not be developed. They were conducted in two subgroups, biophysical subgroup (including unsuitable depth, unsuitable substratum and shore line types, potential regions prone to strong impact of natural disasters and environmental risks, sensitive habitats in MPAs (Marine Protection Areas), and social-infrastructural subgroups (including human settlement in urban area, wastewater system, industrial zones, piers, harbors etc.). A series of GIS models was developed to identify the most suitable areas for oyster culture using MCE with the estimating of factor score (X i) based on expert knowledge as well as calculating of Weighting (W i) based on Saaty's matrix relevant to the importance level of assessed factors in comparison. Suitability scores were ranked on a scale from 1 (least suitable) to 8 (most suitable). The best suitable sites for oyster aquaculture in coastal areas of the central region of Vietnam have been found. These study results confirmed that a GIS based MCE model is a powerful tool that supports site selection decision-making in aquaculture.
The summer upwelling that occurs in coastal waters of South Central Vietnam is one of the major hydrographic features in the East Sea. A weakening of the upwelling after major El Niño events was observed in the literature for previous El Niño events and was verified here from the analysis of new satellite image data sets of sea surface temperature (SST) and surface wind. The analysis of empirical orthogonal function (EOF) from of monthly SST as well as of temporal and spatial variations of SST and wind force allow us to identify abnormal characteristics in ocean surface water that happened after El Niño episode, in agreement with previous studies. Those abnormal characteristics in Vietnam upwelling waters appeared mainly during the summers of 1998, 2003, 2010 and 2016 years for the El Niño decline phase. The upwelling weakening during El Niño decline episodes is associated with the following signals: (1) Wind force and Ekman pump are very weak; (2) the cold and high chlorophyll-a tongue is shifted northward but not extended eastward; (3) for years when El Niño occurs, SST strongly increases and reaches a peak in May or early June of next year, during the declining phase of El Niño episode; (4) upwelling phenomenon typically occurs during August and not July. Using a reanalysis dataset derived from the HYCOM/NCODA system coupled with a local Finite Element Model (FEM) allow us to complete our knowledge about the abnormal oceanographic characteristics of deeper water layers after El Niño episodes. The analysis of spatial variations of oceanography fields derived from HYCOM/NCODA/FEM system along zonal and meridional sections and vertical profiles as well as the results obtained from water mass analysis allow us to identify in details the abnormal oceanic characteristics of deeper water layers during the declining El Niño phase. Those are; (5) Sea water in both surface and deeper water layers were transported dominantly northward but not eastward; (6) The thermo-halocline layer in South Vietnam upwelling center was deeper (about 90 -100m), compared with previous El Niño and normal years (50-60 m and 35-40 m, respectively); (7) Extreme global warming in recent years (2012)(2013)(2014)(2015)(2016) pressed the thermo-halocline layer in upwelling center deeper (90-100 m) during summer. Under the influence of the ocean global warming, this process should progress continuously, the depth of thermo-halocline layer should become therefore deeper and deeper in next years.
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