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정원 및 도시숲의 산림복지서비스 증진을 위한 관목 탄소흡수계수 개발* - 남천, 병꽃나무, 좀작살나무, 쥐똥나무, 흰말채나무를 대상으로 -

Development of Shrub Carbon Sequestration Factors to Improve Forest Welfare Services in Gardens and Urban Forests –Nandina domestica, Weigela subsessilis, Callicarpa dichotoma, Ligustrum obtusifolium, Cornus alba–

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초록

정원과 도시숲은 휴양․치유, 재해방지, 미세먼지 저감, 생물다양성 증진, 도시열섬 저감 효과 등을 가지고 있으며, 최근 식물체의 생장을 통해 공기 중의 이산화탄소를 흡수하여 고정하는 탄소흡수 효과가 주목받고 있다. 정원과 도시숲의 탄소흡수 효과를 정량화화고 흡수량을 산정하기 위해서는 수종별 목재기본밀도, 바이오매스 확장계수, 뿌리함량비, 탄소함량비 등의 탄소흡수계수가 요구된다. 따라서, 본 연구는 정원과 도시숲에 주로 식재되는 관목 5종의 탄소흡수량을 파악하고 수치화하고자 수종별 탄소흡수계수를 개발하였다. 공시재료는 충청권역의 양묘장에서 생산한 수종별 50본 총 250본이며, 직접수확한 식물을 부위(잎과 소지, 가지, 줄기, 뿌리)별로 구분하여 바이오매스를 측정하였다. 연구결과, 목재기본밀도는 좀작살나무(0.67g/㎤), 쥐똥나무(0.63g/㎤), 남천(0.55g/㎤), 흰말채나무(0.45g/㎤), 병꽃나무(0.44g/㎤) 순으로 산정되었다. 바이오매스 확장계수는 병꽃나무(4.57), 흰말채나무(4.25), 좀작살나무(3.76), 남천(3.07), 쥐똥나무(2.29) 순이었다. 뿌리함량비는 남천(0.64), 좀작살나무(0.40), 병꽃나무(0.30), 흰말채나무(0.29), 쥐똥나무(0.27) 순이었다. 탄소함량비는 병꽃나무(45.91%), 남천(45.67%), 좀작살나무(45.32%), 쥐똥나무(45.04%), 흰말채나무(44.99%) 순으로 나타났다. 또한, MATLAB를 활용한 머신러닝 과정으로 적합한 변수를 선정하고 SPSS Statistics 회귀분석을 통해 탄소저장량 추정을 위한 상대생장식을 개발하였다. 연구결과는 정원과 도시숲 분야의 탄소흡수 효과를 정확하게 추정하는데 기여할 것이다.

Abstract

Gardens and urban forests provide various benefits, including recreation, healing, disaster prevention, particulate matter reduction, promotion of biodiversity, and temperature reduction. Recently, there has been growing interest in the carbon sequestration effect, wherein plants uptake and store carbon dioxide from the air through their growth. To quantify the carbon sequestration effect of gardens and urban forests and calculate the uptake amount, various carbon sequestration factors for each species, such as wood density, biomass expansion factor, root content ratio, and carbon content factor, are essential. Therefore, this study developed a carbon sequestration factor for each species in order to identify and quantify the carbon sequestration effect of 5 shrubs planted in gardens and urban forests. The materials are 250 plants produced in Chung-cheong area, and carbon sequestration factor was measured by classifying the plants that were directly harvested by parts(leaf+twig, branch, stem, root). As a result of the study, wood density was calculated in the order of C. dichotoma(0.67g/㎤), L. obtusifolium(0.63g/㎤), N. domestica(0.55g/㎤), C. alba(0.45g/㎤), and W. subsessilis(0.44g/㎤). Biomass expansion factor was calculated in the order of W. subsessilis(4.57), C. alba(4.25), C. dichotoma(3.76), N. domestica(3.07), and L. obtusifolium(2.29). The root content ratio was calculated in the order of N. domestica(0.64), C. dichotoma(0.40), W. subsessilis(0.30), C. alba(0.29), and L. obtusifolium(0.27). The carbon content factor was calculated in the order of W. subsessilis(45.91%), N. domestica(45.67%), C. dichotoma(45.32%), L. obtusifolium(45.04%) and C. alba(44.99%). In addition, appropriate variables were selected using a Machine Learning process in MATLAB, and relative allometric equations were developed to estimate carbon storage through SPSS Statistics regression analysis. The study results will contribute to accurately estimating the carbon sequestration effect of gardens and urban forests.

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