봄철 지역축제 활성화를 위한 수종별 개화특성 연구
A Study on Flowering Traits of Spring Flowering Species for the Invigorate of Local Festivals in Spring
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초록
최근 많은 지자체에서 봄철 개화수종을 활용한 다양한 지역축제를 경쟁적으로 유치하고 있어 정확한 개화시기 예측은 축제의 성공을 결정하는 중요한 요소가 되었다. 개화는 온대활엽수림의 식물계절 특성 중 하나이자 기후변화를 체감할 수 있는 중요한 지표 중 하나로 인식됨에 따라 수종별 개화 특성(개화시기, 개화기간 등)을 구명하기 위한 연구가 진행되어 왔다. 과거 이를 관찰자가 직접 기록하여 자료를 수집해 왔으나, 최근 자동관측 시스템을 활용한 식물계절 변화 관찰 방법이 많이 사용되고 있지만, 여전히 정확도를 높이기 위해서는 직접 관측을 통한 보정이 필요한 실정이다. 이에 본 연구는 지역 주민에게 휴양서비스를 제공하고 있는 대구대학교 수목관찰원에 설치 되어있는 자동관측 시스템에서 2019년 3월부터 2020년 6월 사이 수집된 3,647장의 사진 자료를 식물계절 단계별로 분석하였다. 사진에서 각 개체에 해당하는 영역을 추출한 뒤 Red, Green, Blue 채널의 평균값을 계산하고, RGB 채널 이미지 분석을 통해 Red chromatic coordinate (Rcc)와 Green chromatic coordinate (Gcc), Blue chromatic coordinate (Bcc)를 추출하여, 조사수종별 개화특성과 개화 시기를 측정하였다. 또한, 수종별 개화 특성에 대한 자동관측 적용을 위하여 직접관측을 병행하여 서로의 개화일 결과를 비교하여 적용성을 검증하였다. 왕벚나무의 RGB 채널 이미지 분석 개화일은 3월 21일 (DOY : 81)로 직접 관측에 의해 기록된 개화일 (DOY 84)보다 3일 빠른 개화일을 보여주었으며, 올벚나무와 이팝나무는 RGB 채널 이미지 분석 개화일이 각각 3월 15일 (DOY : 74), 5월 4일 (DOY : 124)로, 직접 관측에 의해 기록된 개화일 (DOY : 77, 129)에 비하여 3일과 5일 더 빨랐다. 마지막으로 산딸나무의 RGB 채널 이미지 분석 개화일은 5월 23일 (DOY : 143)로 직접 관측에 의한 개화일 (DOY : 147)에 비해 4일 더 빨리 개화를 판단한 것으로 나타났다. 이러한 차이는 빛에 대한 반사율이 높고 배경과 대비가 낮아 꽃을 정확하게 식별하기 어려운 흰색 계열 꽃이 가지는 근본적인 한계점으로 판단된다. 마지막으로 개화일과 기온과의 상관관계 분석 결과, 모든 수종에서 최저기온과 가장 높은 상관관계가 있는 것으로 나타나 최저기온에 대한 지속적인 모니터링이 필요함을 시사한다. 앞서 밝힌 한계점을 보완한 추가 연구를 통해 정확도를 높이고, 장기간 개화자료를 수집, 수종별 고유의 개화특성을 분석한다면 자동관측에 따른 개화일 예측의 정확도가 높아질 수 있을 것으로 판단되었다.
Abstract
The flowering is one of the most important phenological characteristics of temperate deciduous hardwood forests, and it is very important that predicting the flowering time of tree species is could be determining the success of local spring festivals. Also it is recognized as an important indicator of climate change, researchers a lot of effort into observing flowering characteristics. The digital repeat photography is the most widely used to collect flowering data, however field observation is still needed for its accuracy. In this study, we analyzed the phenological stage, using 3,647 image data, collected between March 2019 and June 2020 through an automatic observation ststem at the Daegu University Arboretum. It provides recreational services to local residents. In the picture, we extracted the area that corresponds to each species, calculated the average value of the Red, Green, and Blue channels. Red chromatic coordinate (Rcc), Green chromatic coordinate (Gcc), and Blue chromatic coordinate (Bcc) were calculated from RGB channel image data and the flowering data was determined. And we compared the flowering data with the field observation. The RGB channel image analysis flowering data of the P. × yedoensis Matsum. was 18 March (DOY 77), showed a 7 days earlier flowering data than the recorded flowering data by field observation (DOY: 84). P. spachiana and C. retusus showed RGB channel image analysis flowering dates of 15 March (DOY: 74) and 4 May (DOY: 124), respectively, 3 and 1 days earlier than those recorded by field observation (DOY: 77, 129). Finally, The RGB channel image analysis data of the C. kousa was 23 May (DOY: 143), which was 4 days earlier than the flowering date by traditional methods (DOY: 147). These differences are considered to be due to the inherent limitations of white flowers, which have high reflectivity to light and low contrast with the background, making them difficult to accurately identify. Lastly, the analysis of the correlation between flowering dates and temperature showed that all species exhibited the highest correlation with minimum temperature, suggesting the necessity of continuous monitoring of minimum temperatures. We believed to be a limitation point that dose not reflect the error of sunlight. It is needed to solve limited factors which mentioned previous in this study for application of totally automatic observation system to predict flowering characteristics through further studies.
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