RGB영상과 다분광영상을 이용한 잔디 녹기 평가

Abstract

Zoysiagrass (Zoysia spp.) is the most widely used warm-season turfgrass in Korea due to its durability and resistance to environmental stresses, which has a big problem to lose its greenness as the temperature gets down. To develop new longer-period greenness cultivars, it is essential to screen and monitor germplasm which maintains greenness as the temperature lowers. Conventional methods to visually evaluate the greenness of turf is too time-consuming and laborious and subjective because of the different results by raters. Therefore, in this study, we demonstrate an objective and efficient method to screen maintaining longer greenness germplasm using RGB images and multispectral images. From August to December, time-series data were acquired and calculated green cover percentage (GCP) and NDVI (normalized difference vegetation index) values of germplasm from RGB images and multispectral images by applying a vegetation index, respectively ExG (excess green) and NDVI. The result showed significant differences in GCP and NDVI among germplasm (p<0.05). The GCP, which evaluated the quantity of greenness by counting pixels of the green area from RGB images, exhibited maintenance of greenness over 90% for August and September but, sharply decrease from October. As time went by, the NDVI values, which showed the quality of greenness, was also declined, but not sharply. In san208, it was observed to show over 90% GCP and high NDVI values. In san9dangugdae, it showed the highest GCP until October, although the lowest NDVI values simultaneously. Significant correlation coefficients (r) were observed in only November (r=0.78). Consequently, the results of this study suggest that the complementary use of two indicators, GCP and NDVI, could be an efficient method for objectively assessing the greenness of turf both quantitatively and qualitatively.