• Journal of Life Science
• /
• v.31 no.12
• /
• pp.1088-1093
• /
• 2021

Jeju horse (Equus ferus caballus) is a Korean horse breed that has been native to Jeju Island for a long time. Jeju horses are used as racehorses, and their racing ability is a major economic trait. The role of the myostatin (MSTN) gene in skeletal muscle mass has been studied in various mammals, and mutations in the MSTN gene are known to affect the racing ability and stamina of thoroughbreds. In this study, we compared the frequency of mutations in the MSTN gene in several horse breeds, including 1,433 Jeju horses. Among the mutations (ECA18 g.66493737C>T) in the MSTN gene, the long-distance aptitude genotype (TT) was found to have a frequency of 0.826 in Jeju horses, which was higher than that in Halla horses (0.285) and thoroughbreds (0.252). The genotypes and arrival records of Jeju horses were compared according to various distances (400 m, 800 m, 900 m, 1,000 m, 1,110 m, and 1,200 m). According to the results, the CT type showed a faster-reaching record than the TT type in races of less than 1,000 m. However, almost identical results were confirmed in races over 1,110 m. This study suggests that the MSTN mutation in Jeju horses may be related to race distance aptitude. In future research, the data in this study can be used for developing markers related to race distance aptitude and racing abilities in Jeju horses.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1341-1352
• /
• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.