본 연구는 대표적인 국내 전통 천연염색소재인 감(persimmon)과 쪽(indigo)으로 염색한 면직물로 구성된 동일색조 2-배색 직물의 색채감성 요인을 규명하고 색채감성 요인 및 색채 선호도와 물리적 색채 변인들 간의 관계를 토대로 색채감성 요인의 예측모델을 수립하고자 수행되었다. 감과 쪽으로 염색하여 동일색조 2개 유형(soft와 dull)과 배색 면적비(감:쪽) 3 수준(1:1, 2:1, 1:2)을 조합한 총 6종의 직물 자극물을 준비하여, 20대 여대생 38명을 대상으로 IRI 색채이미지스케일의 13개 색채감성 형용사와 선호도에 대해 의미미분척도로 색채감성을 평가하였다. 연구 결과로서 ‘안락감’, ‘경량감’, ‘역동성’, ‘고상함’의 4개 색채감성 요인이 추출되었는데, 요인 ‘경량감’은 동일색조 유형인 soft에 서 더 강하게 느껴지는 경향이었으며, 요인 ‘안락감’은 배색 면적비 2:1에서, ‘고상함’은 배색면적비 1:2에서 유의하 게 더 높게 평가되었다. 또한 물리적 배색변인들은 요인 ‘안락감’, ‘경량감’, ‘고상함’과 유의한 상관관계를 보였다. 이 결과를 바탕으로 색채감성 요인 ‘안락감’과 ‘경량감’, ‘고상함’의 예측 회귀모델을 수립하였으며, 색채선호도는 유의한 예측식이 수립되지 않았다. 본 연구 결과는 감과 쪽의 천연염색 섬유제품의 배색을 활용하여 섬유⋅패션제품 의 감성 이미지를 설계하는 데에 활용할 수 있을 것이다.

It is necessary to understand the amount of snowfall and area of snow cover of Mt. Halla to ensure the safety of mountaineers and to protect the ecosystem of Mt. Halla against climate change. However, there are not enough related studies and observation posts for monitoring snow load. Therefore, to supplement the insufficient data, this study proposes an analysis of snow load and snow cover using normalized-difference snow index. Using the images obtained from the Sentinel2 satellite, the normalized-difference snow index image of Mt. Halla could be acquired. This was examined together with the meteorological data obtained from the existing observatory to analyze the change in snow cover for the years 2020 and 2021. The normalized-difference snow index images showed a smaller snow pixel number in 2021 than that in 2020. This study concluded that 2021 may have been warmer than 2020. In the future, it will be necessary to continuously monitor the amount of snow and the snow-covered area of Mt. Halla using the normalized-difference snow index image analysis method.

Global warming affects forests and their ecology. Diversity in the forest is a buffer that reduces the damage due to global warming. Mixed forests are ecologically more valuable as versatile habitats and are effective in preventing landslides. In Korea, most forests were created by simple afforestation with trees of evergreen species. Typically, evergreen trees are shallow-rooted, and deciduous trees are deep-rooted. Mixed forest tree roots grip the soil effectively, which reduces the occurrence of landslides. Therefore, improving the distribution of tree types is essential to reduce damage due to global warming. For this improvement, the investigation of tree types of the forest is needed. However, determining the tree type distribution of forests that are spread over wide areas is labor-intensive and time-consuming. This study suggests effective methods for determining the distribution of tree types in a forest that is spread across a relatively wide area. Using normalized difference vegetation index and RGB images from unmanned aerial vehicles, each evergreen and deciduous tree, and grassland area can be distinguished. The distinguished image determines the distribution of tree type. This method is effective compared to directly determining the tree type distribution in the forest by the use of manpower. The data from these methods could be applied to plan a mixed forest or to prepare for future damage due to global warming.