59 research outputs found
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
Diel habitat-use patterns of commercially important fishes in a marine protected area in the Philippines
The diel habitat-use patterns of commercially mportant fishes in a small marine protected area (MPA) (0.31 km2) containing coral reef and seagrass habitats were examined by passive acoustic telemetry during 2011 and 2012. The occurrence patterns of the target fishes both inside and outside the MPA were also observed. Thirty individuals from 6 species (20.2 to 41.4 cm fork length) were caught, acoustically tagged and released inside the MPA, and 4 to 210 d of tracking data were then obtained from 28 detected fishes. Lutjanus monostigma, Lethrinus atkinsoni, and Lethrinus obsoletus were found to mostly inhabit the coral reef. The remaining 3 species (Lutjanus argentimaculatus, Lethrinus harak, and Siganus guttatus) utilized both coral and seagrass habitats but showed different patterns: Lutjanus argentimaculatus visited seagrass only at night; Lethrinus harak occurred in the coral reef more at night than in the day, showing the opposite pattern in seagrass; and S. guttatus exhibited the converse pattern to L. harak. More than one-third of the tracked individuals moved inside and outside the MPA more than once per day on average during the tracking period. However, 95.4% of detections were recorded by acoustic receivers deployed inside the MPA. Underwater visual surveys revealed that the densities of some target fishes were significantly higher inside than outside the MPA. These findings suggest that the MPA protects the core of fish home ranges
Application of the deep-learning technics for the extraction of seagrass beds: over 30 years changes of beds at Futtsu in the Tokyo Bay, Japan and Hat Chao Mai National Park, Thailand.
Pain Intensity Measurement after Gynecological Laparoscopic Surgery Using the “Pain Vision(TM)" System for the Quantitative Analysis of Perception and Pain Sensation
Importance of outer reef slopes for commercially important fishes: implications for designing a marine protected area in the Philippines
A passive acoustic telemetry survey was conducted to determine occurrence patterns of commercially important fishes on a steep reef slope along a marine protected area (MPA) in the southern Philippines, where the outer reef edge is often set as an offshore MPA boundary. Based on 4-61 days of tracking data from 21 detected individuals of five species (Lutjanus argentimaculatus, Lutjanus monostigma, Lethrinus atkinsoni, Lethrinus obsoletus, and Siganus guttatus; 20.7-69.2 cm fork length) caught near the reef slope of the MPA, S. guttatus occurred most frequently on the reef flat of the MPA, whereas all individuals of the four lutjanid and lethrinid species were primarily (99.4-100%) detected near the reef slope, and nine individuals (56.3% of these four species) of three of these species (not L. obsoletus) most likely used the shallow (ae10 m) and deep (ae20 m) layers, and thus, middle layers of the slope. These findings indicate that commercially important lutjanid and lethrinid species predominantly and vertically used the areas near the reef slope, suggesting the importance of fully including reef slopes in MPAs to enhance their effectiveness for the conservation of such fishes
Application of deep learning techniques for determining the spatial extent and classification of seagrass beds, Trang, Thailand
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