All Issue

2026 Vol.13, Issue 1

Original Article

Analysis of Topographic, Structural, and Vegetation Factors Influencing Snow-Induced Uprooting of Pinus densiflora for. erecta

폭설에 의한 금강소나무 전도 현상의 지형·구조·식생학적 요인 분석

Minkyu Park1*
박 민규1*
1CTO, R&D Division, SeeNature Corp., Daegu 42710, Korea
1주식회사 씨네이처 연구개발본부 CTO
*Corresponding Author
31 March 2026. pp. 1-9
PDF XML Citation
Abstract

This study comprehensively analyzed the topographic, structural, and vegetation factors influencing uprooting occurrence in Pinus densiflora for. erecta forests that experienced extensive tree failures caused by heavy snowfall. Uprooting locations and information, including the diameter at breast height (DBH) of 2,031 individuals, were obtained through visual identification of fallen trees from unmanned aerial vehicle–derived orthoimages, and Factor Analysis of Mixed Data (FAMD) was applied. The analysis revealed that slope gradient was identified as the dominant factor associated with susceptibility to uprooting. It was also quantitatively confirmed that structural and microtopographic factors interacted to shape the spatial patterns of uprooting. Large P. densiflora for. erecta trees near deciduous forest edges are especially prone to uprooting during heavy snow. This heightened vulnerability is attributed to the combined effects of snow load, increased snow accumulation during the leaf-off period of surrounding broadleaf trees, direct exposure to wind pressure, and the structural instability of isolated old-growth trees during the succession process. These findings highlight the necessity of integrating topographic and tree structural attributes when assessing uprooting risk in P. densiflora for. erecta forests. Ultimately, this study provides essential scientific baseline information for establishing priority management and adaptive strategies aimed at enhancing the resilience and long-term stability of forest ecosystems under changing disturbance regimes.

Keywords
Edge effect
Factor analysis of mixed data
Orthomosaic photo
Vegetation succession

본 연구는 폭설로 인해 대량 전도된 금강소나무숲을 대상으로 전도 발생에 미치는 지형, 구조, 식생 요인을 통합적으로 분석하였다. 무인항공기로 촬영한 정사영상에서 전도목을 육안 탐지하여 2,031개체의 전도 위치 및 흉고직경 등의 정보를 수집하고 혼합자료 요인분석(FAMD)을 적용하였다. FAMD 분석 결과, 경사도는 전도 취약성과 가장 높은 상관성을 보이는 주요 요인으로 확인되었으며, 흉고직경, 군락 경계까지의 거리, 사면 방향 등 전도개체의 구조 및 미지형적 요인들이 복합적으로 작용하여 전도의 공간 패턴을 형성함을 정량적으로 확인하였다. 특히, 활엽수림 경계 부근에 잔존하는 대경목 금강소나무의 폭설에 의한 전도 위험이 매우 높았으며, 이는 적설하중, 주변 활엽수의 낙엽에 의한 적설량 증가 및 풍압의 직접적 노출, 천이 과정에서 고립된 노령 거목의 구조적 불안정성 등이 복합적으로 작용한 것으로 판단된다. 따라서 본 연구는 금강소나무 전도 위험 평가에서 지형 및 수목 구조 요인을 통합적으로 고려할 필요성을 제시하였고, 금강소나무의 우선적 관리 전략 수립을 위한 과학적 기초자료를 분석하였다.

키워드
가장자리 효과
혼합자료 요인분석
정사영상
식생천이
References
1

Chirici, G., Bottalico, F., Giannetti, F., Del Perugia, B., Travaglini, D., et al. 2018. Assessing forest windthrow damage using single-date, post-event airborne laser scanning data. Forestry: An International Journal of Forest Research 91: 27-37.

10.1093/forestry/cpx029
2

Ciocirlan, M. and Câmpu, V.R. 2024. Characteristics of Forest Windthrow Produced in Eastern Carpathians in February 2020. Forests 15: 176.

10.3390/f15010176
3

Dupont, S. and Brunet, Y. 2008. Impact of forest edge shape on tree stability: a large-eddy simulation study. Forestry: An International Journal of Forest Research 81: 299-315.

10.1093/forestry/cpn006
4

Fan, Q., Yang, H., Li, P., Duan, Y., Guo, D., et al. 2024. Ecological Response of Forest Vegetation Communities to Snow Damage: A Meta-Analysis. Forests 15: 1989.

10.3390/f15111989
5

Fujihara, M. 1996. Development of Secondary Pine Forests after Pine Wilt Disease in Western Japan. Journal of Vegetation Science 7: 729-738.

10.2307/3236384
6

Gardiner, B.A. and Quine, C.P. 2000. Management of forests to reduce the risk of abiotic damage—a review with particular reference to the effects of strong winds. Forest Ecology and Management 135: 261-277.

10.1016/S0378-1127(00)00285-1
7

Gardiner, B., Schuck, A.R.T., Schelhaas, M.-J., Orazio, C., Blennow, K., et al. 2013. Living with storm damage to forests. European Forest Institute Joensuu.

8

Griess, V.C. and Knoke, T. 2011. Growth performance, windthrow, and insects: meta-analyses of parameters influencing performance of mixed-species stands in boreal and northern temperate biomes. Canadian Journal of Forest Research 41: 1141-1159.

10.1139/x11-042
9

Hildebrandt, P., Kirchlechner, P., Hahn, A., Knoke, T., and Mujica, R.H. 2010. Mixed species plantations in Southern Chile and the risk of timber price fluctuation. European Journal of Forest Research 129: 935-946.

10.1007/s10342-009-0284-4
10

Kassambara, A. 2025. ggpubr: “ggplot2” Based Publication Ready Plots.

11

Keeton, W.S. and Franklin, J.F. 2005. Do Remnant Old-Growth Trees Accelerate Rates of Succession in Mature Douglas-Fir Forests? Ecological Monographs 75: 103-118.

10.1890/03-0626
12

Kim, E.S. and Lee, K.W. 2023. Risk Assessment of Pine Tree Dieback in Uljin and Bonghwa. Korean Journal of Agricultural and Forest Meteorology 25: 117-128.

10.5532/KJAFM.2023.25.3.117
13

Lê, S., Josse, J., and Husson. F. 2008. FactoMineR: A Package for Multivariate Analysis. Journal of Statistical Software 25: 1-18.

10.18637/jss.v025.i01
14

Lee, S.J., Shin, D.B., Lee, A.R., and Oh. S.H., 2023. Characteristics of dieback of Pinus densiflora and risk assessment in the Wangpicheon ecosystem and landscape conservation area in Uljin, South Korea. Forests 14: 903.

10.3390/f14050903
15

Li, J., Morimoto, J., Hotta, W., Suzuki, S.N., Owari, T. et al. 2023. The 30-year impact of post-windthrow management on the forest regeneration process in northern Japan. Landscape and Ecological Engineering 19: 227-242.

10.1007/s11355-023-00539-9
16

Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., and Hornik, K. 2023. cluster: Cluster Analysis Basics and Extensions.

17

Mitchell, S.J. 2013. Wind as a natural disturbance agent in forests: a synthesis. Forestry: An International Journal of Forest Research 86: 147-157.

10.1093/forestry/cps058
18

Pagès, J. 2014. Multiple Factor Analysis by Example Using R. Chapman and Hall/CRC, New York.

10.1201/b17700
19

Pebesma, E. and Bivand, R. 2023. Spatial Data Science: With applications in R. Chapman and Hall/CRC.

10.1201/9780429459016
20

Pebesma, E., Mailund, T., and Hiebert, J. 2016. Measurement Units in R. The R Journal 8: 486-494.

10.32614/RJ-2016-061
21

QGIS Development Team. 2025. QGIS Geographic Information System.

22

R Core Team. 2025. R: A Language and Environment for Statistical Computing. .

23

Shin, J.-S., Lee, Y.-S., Kim, E.-S., Jang, K., Lee, J.-S. et al. 2025. The Effects of Climate on the Death and Growth Decline of Pinus densiflora Siebold & Zucc. using the Annual Ring-Width Time Series. Journal of the Korean Wood Science and Technology 53: 441-453.

10.5658/WOOD.2025.53.4.441
24

Slowikowski, K. 2024. ggrepel: Automatically Position Non-Overlapping Text Labels with “ggplot2.”

25

Stovall, A.E.L., Shugart, H., and Yang, X. 2019. Tree height explains mortality risk during an intense drought. Nature Communications 10: 4385.

10.1038/s41467-019-12380-631558795PMC6763443
26

Štraus, H. and Bončina, A. 2025. The vulnerability of four main tree species in European forests to seven natural disturbance agents: lessons from Slovenia. European Journal of Forest Research 144: 267-282.

10.1007/s10342-024-01754-1
27

Takano, K.T., Nakagawa, K., Aiba, M., Oguro, M., Morimoto, J. et al. 2016. Projection of impacts of climate change on windthrows and evaluation of potential adaptation measures in forest management: A case study from empirical modelling of windthrows in Hokkaido, Japan, by Typhoon Songda (2004). Hydrological Research Letters 10: 132-138.

10.3178/hrl.10.132
28

Tavankar, F., Monaco, A.L., Nikooy, M., Venanzi, R., Bonyad, A. et al. 2019. Snow damages on trees of an uneven age in mixed broadleaf forests: effects of topographical conditions and tree characteristics. Journal of Forestry Research 30: 1383-1394.

10.1007/s11676-018-0710-x
29

Watson, A. 2000. Wind-induced forces in the near-surface lateral roots of radiata pine. Forest Ecology and Management 135: 133-142.

10.1016/S0378-1127(00)00305-4
30

Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

10.1007/978-3-319-24277-4_9
31

Wickham, H., François, R., Henry, L., Müller, K., and Vaughan, D. 2023. dplyr: A Grammar of Data Manipulation.

32

Zeng, H., Peltola, H., Talkkari, A., Venäläinen, A., Strandman, H. et al. 2004. Influence of clear-cutting on the risk of wind damage at forest edges. Forest Ecology and Management 203: 77-88.

10.1016/j.foreco.2004.07.057
33

Zhu ChunYu, Z.C., Zhu JiaoJun, Z.J., Zheng, Z.X. Xiao, Lu DeLiang, L.D., and Li XiuFen, L.X. 2017. Comparison of gap formation and distribution pattern induced by wind/snowstorm and flood in a temperate secondary forest ecosystem, northeast China.

10.14214/sf.7693
34

Zubizarreta-Gerendiain, A., Pellikka, P., Garcia-Gonzalo, J., Ikonen, V.-P., and Peltola, H. 2012. Factors affecting wind and snow damage of individual trees in a small management unit in Finland: assessment based on inventoried damage and mechanistic modelling. Silva Fennica 46.

10.14214/sf.441
Information
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 13
  • No :1
  • Pages :1-9
  • Received Date : 2025-11-24
  • Revised Date : 2025-12-04
  • Accepted Date : 2025-12-04
  • DOI :https://doi.org/10.17820/eri.2026.13.1.001
Journal Informaiton Ecology and Resilient Infrastructure Ecology and Resilient Infrastructure
Online Submission
  • NRF
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close