References
- Allen, C.D., Macalady, A.K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D.D., Hogg, E.H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.-H., Allard, G., Running, S.W., Semerci, A. and Cobb, N. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259(4): 660-684. https://doi.org/10.1016/j.foreco.2009.09.001
- Allen-Reid, D., Anhold, J., Cluck, D., Eager, T., Mask, R., McMillin, J., Munson, S., Negron, J., Rogers, T., Ryerson, D., Smith, E., Smith, S., Steed, B. and Thier, R. 2008. Pinon pine mortality event in the Southwest: An update for 2005. U.S. Forest Service.
- Bae, S.W., Lee, C.Y., Park, B.W., Hong, S.C., Kim, I.S., Han, S.U., Hong, K,N., Lee, S.W., Cho, K.H., Hwang, J.H., Lee, S.T., Kim, K.H., Moon, I.S., Son, Y.M., Cheon, C.H., Park, J.H., Ka, K.H., Lee, H.J., Park, M.J., Kim, C.Y., Kim, K.W., Lim, J.H. and Kim, S.J. 2012. Commercial tree species (1) Pinus densiflora. NIFoS. pp. 250.
- Bennett, A.C., McDowell, N.G., Allen, C.D. and Anderson‐ Teixeira, K.J. 2015. Larger trees suffer most during drought in forests worldwide. Nature Plants 1: 15139. https://doi.org/10.1038/nplants.2015.139
- Beven, K.J. and Kirkby, M.J. 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Science Bulletin 24(1): 43-69. https://doi.org/10.1080/02626667909491834
- Bottero, A., D'Amato, A.W., Palik, B.J., Bradford, J.B., Fraver, S., Battaglia, M.A. and Asherin, L.A. 2017. Densitydependent vulnerability of forest ecosystems to drought. Journal of Applied Ecology 54: 1605-1614. https://doi.org/10.1111/1365-2664.12847
- Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. 1984. Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.
- Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37-46. https://doi.org/10.1177/001316446002000104
- Cortes, C. and Vapnik, V. 1995. Support-vector networks. Machine Learning 20(3): 273. https://doi.org/10.1007/BF00994018
- Fawcett, T. 2006. An Introduction to ROC Analysis. Pattern Recognition Letters 27(8): 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
- Greenwood, S., Ruiz‐Benito, P., Martinez‐Vilalta, J., Lloret, F., Kitzberger, T., Allen, C.D. and Kraft, N.J. 2017. Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecology Letter 2: 539-553.
- Halofsky, J.E. and Peterson, D.L. 2016. Climate Change Vulnerabilities and Adaptation Options for Forest Vegetation Management in the Northwestern USA. Atmosphere 7(46): 1-14.
- Ho, T.K. 1995. Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14-16 August 1995. pp. 278-282.
- Jeong, J.H., Won, H.K. and Kim, I.H. 2004. Forest Site in Korea -Forest Soil-. NIFoS. pp. 621.
- Jung, H.S. 2018. Improved the Stand Structure Map for Pinus densiflora Areas in Sogwang-ri, Ul-Jin based on Airborne LiDAR. NIFoS. pp. 102.
- Kim, E.S., Lee, J.S., Kim, J., Lim, J.H. and Lee, J.S. 2016. Conservation and management of Korean pine forest. NIFoS. pp. 22.
- Kim, J., Kim, E.S. and Lim, J.H. 2017. Topographic and Meteorological Characteristics of Pinus densiflora Tree Dieback in Sogwang-Ri, Uljin. Korean Journal of Agricultural and Forest Meteorology 19(1): 10-18. https://doi.org/10.5532/KJAFM.2017.19.1.10
- Klein, T. and Hartmann, H. 2019. Climate change drives tree mortality. Science 362(6416): 758.
- Kurz, W.A., Dymond, C.C., Stinson, G., Rampley, G.J., Neilson, E.T., Carroll, A.L., Ebata, T. and Safranyik, L. 2008. Mountain pine beetle and forest carbon feedback to climate change. Nature 452: 987-990. https://doi.org/10.1038/nature06777
- Li, M., Im, J. and Beier, C. 2013. Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GIScience & Remote Sensing 50(4): 361-384. https://doi.org/10.1080/15481603.2013.819161
- Lim, J.H., Kim, E.S., Lee, B., Kim, S.H. and Chang, G.C. 2017. An analysis of the hail damages to Korean forests in 2017 by meteorology, species and topography. Korean Journal of Agricultural and Forest Meteorology 19(4): 280-292. https://doi.org/10.5532/KJAFM.2017.19.4.280
- Mobbertin, M., Mayer, P., Wohlgemuth, T., Feldmeyer-Christe, E., Graf, U., Zimmermann, N.E. and Rigling, A. 2005. The decline of Pinus sylvestris L. Forests in the Swiss Rhone Valley - a Result of Drought Stress?. Phyton 45(4): 153-156.
- Nagel, L.M., Palik, B.J., Battaglia, M.A., D'Amato, A.W., Guldin, J.M., Swanston, C.W., Janowiak, M.K., Powers, M.P., Joyce, L.A., Millar, C.I., Peterson, D.L., Ganio, L.M., Kirschbaum, C. and Roske, M.R. 2017. Adaptive silviculture for climate change: A national experiment in managerscientist partnerships to apply an adaptation framework. Journal of Forestry 115(3): 167-178. https://doi.org/10.5849/jof.16-039
- NIFoS (National Institute of Forest Science). 2009. Causes and future outlook of Korean red pine dieback. NIFoS. pp. 21.
- Oh, H.J. 2010. Landslide detection and landslide susceptibility mapping using aerialphotos and artificial neural networks. Korean Journal of Remote Sensing 26(1): 47-57.
- Rowland, L., da Costa, A.C.L., Galbraith, D.R., Oliveira, R.S., Binks, O.J., Oliveira, A.A.R., Pullen, A.M., Doughty, C.E., Metcalfe, D.B., Vasconcelos, S.S., Ferreira, L.V., Malhi, Y., Grace, J., Mencuccini, M. and Meir, P. 2015. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528: 119-121. https://doi.org/10.1038/nature15539
- Ryo, M., and Rillig, M.C. 2017. Statistically reinforced machine learning for nonlinear patterns and variable interactions. Ecosphere 8(11)d: e01976. https://doi.org/10.1002/ecs2.1976
- Seo, M.G. 2014. Data processing and analysis using R. Publisher Gilbut. pp. 580.
- Stockwell, D.R.B. and Peterson, A.T. 2002. Effects of sample size on accuracy of species distribution models. Ecological Modelling 148(1): 1-13. https://doi.org/10.1016/S0304-3800(01)00388-X
- Thessen, A. 2016. Adoption of machine learning techniques in ecology and earth science. One Ecosystem 1(2): e8621. https://doi.org/10.3897/oneeco.1.e8621
- USDA (United States Department of Agriculture). 2018. Southwestern region arizona forest health 2018. ArgGIS MapJournal.
- Ye, H., Beamish, R.J., Glaser, S.M., Grant, S.C., Hsieh, C.H., Richards, L.J., Schnute, J.T. and Sugihara, G. 2015. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proceedings ofthe National Academy of Sciences 112(13): E1569-E1576. https://doi.org/10.1073/pnas.1417063112
- Zhang, J., Finley, K.A., Johnson, N.G. and Ritchie, M.W. 2019. Lowering stand density enhances resiliency of ponderosa pine forests to disturbances and climate change. Forest Science 65(4): 496-507. https://doi.org/10.1093/forsci/fxz006