Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand

Few studies have investigated the long-term temporal dynamics of seagrass beds, especially in Southeast Asia. Remote sensing is one of the best methods for observing these dynamic patterns, and the advent of deep learning technology has led to recent advances in this method. This study examined the feasibility of applying image classification methods to supervised classification and deep learning methods for monitoring seagrass beds. The study site was a relatively natural seagrass bed in Hat Chao Mai National Park, Trang Province, Thailand, for which aerial photographs from the 1970s were available. Although we achieved low accuracy in differentiating among various densities of vegetation coverage, classification related to the presence of seagrass was possible with an accuracy of 80% or more using both classification methods. Automatic classification of benthic cover using deep learning provided similar or better accuracy than that of the other methods even when grayscale images were used. The results also demonstrate that it is possible to monitor the temporal dynamics of an entire seagrass area, as well as variations within sub-regions, located in close proximity to a river mouth

Comparison of the Catch and Income from the Crab Gillnet Fishery between Summer and Rainy Season around the Laem Phak Bia Coast, Petchaburi Province

Around the coastal area of Laem Phak Bia, Petchaburi province, bottom crab gillnet targeting blue swimming crab (Portunus pelagicus) is the typical fishing gear that conducted almost whole year in every season. The fishing boat mainly are small scale boat (< 10 GT). The gillnet made from nylon monofilament (PA), 10 cm mesh size, about 0.5 hanging ratio, 12 mesh depth, 450 m/set (1 set connected by 5 net panels). Fisher hauls the net 2-4 sets for 1-2 GT boat while 18-20 sets/operation day for 8-10 GT boat. The head rope made of polyethylene (PE) z twisted, diameter 4 mm, connected to the flag buoys. The plastic float size of 38x10mm, 2.0 m interval. The foot rope tied to anchor or stone weight 5-8 kg, made of polyethylene (PE) z twisted, diameter 3 mm. Sinker size was 10 g, 0.45m interval. Fishers (8-10 GT boat) usually operate the fishing at 2-7 am in the fishing ground of 18-25 m depth with the soaking time of 3-5 days. Some marketable bycatch are flathead lobster (Thenus orientalis), ray, flatfish and horseshoe crab. Statistics on Fishing community production survey by species of fish, shrimp, crab, shellfish, jellyfish, other aquatic animals, the crab species, highest value was 3,624.24 million baht at 21,570 ton. The crab is an important economic fish for Thai fishery. Thailand is under the influence of southwest and northeast monsoon those impact to the fishing activities. The rainy season (May-Oct) is influenced by southwest monsoon while the winter season (Nov-Jan) influenced by the northeast monsoon. This study aimed to compare the catch, income, operation days and fishing ground of a crab gillnet fisher (9 GT boat) between summer and rainy season

Tsunami Impacts on Biodiversity of Seagrass Communities in the Andaman Sea, Thailand: (2) Abundance and Diversity of Benthic Animals

Seagrass beds support highly productive and diverse animal communities. Extreme physical disturbances such as tsunamis are expected to affect the abundance and diversity of both the seagrass and the infaunal community. To examine the effects of a tsunami on infaunal seagrass communities, we compared the communities in seagrass beds before and after the tsunami that hit the Andaman Sea coast of Thailand on December 26th, 2004. Infauna were collected in 2001 and again in 2005 from inside and outside seagrass vegetation. Polychaetes were the most abundance taxa, followed by crustaceans. Density of macrobenthic animals varied greatly among sites, among areas inside and outside the vegetated areas and between the two years. The density increased over time in the areas with seagrass while it deceased in the non-vegetated areas, suggesting that the tsunami impacted the two areas differently. Multivariate analysis on the polychaete assemblage revealed that temporal changes in assemblage structure differed between vegetated and non-vegetated areas, and that the degree of temporal changes in assemblage structure was not necessarily related to the magnitude of the tsunami