Science China Life Sciences

, Volume 58, Issue 7, pp 713–723 | Cite as

Spatio-temporal change in forest cover and carbon storage considering actual and potential forest cover in South Korea

  • Kijun Nam
  • Woo-Kyun Lee
  • Moonil Kim
  • Doo-Ahn Kwak
  • Woo-Hyuk Byun
  • Hangnan Yu
  • Hanbin Kwak
  • Taesung Kwon
  • Joohan Sung
  • Dong-Jun Chung
  • Seung-Ho Lee
Open Access
Research Paper

Abstract

This study analyzes change in carbon storage by applying forest growth models and final cutting age to actual and potential forest cover for six major tree species in South Korea. Using National Forest Inventory data, the growth models were developed to estimate mean diameter at breast height, tree height, and number of trees for Pinus densiflora, Pinus koraiensis, Pinus rigida, Larix kaempferi, Castanea crenata and Quercus spp. stands. We assumed that actual forest cover in a forest type map will change into potential forest covers according to the Hydrological and Thermal Analogy Groups model. When actual forest cover reaches the final cutting age, forest volume and carbon storage are estimated by changed forest cover and its growth model. Forest volume between 2010 and 2110 would increase from 126.73 to 157.33 m3 hm−2. Our results also show that forest cover, volume, and carbon storage could abruptly change by 2060. This is attributed to the fact that most forests are presumed to reach final cutting age. To avoid such dramatic change, a regeneration and yield control scheme should be prepared and implemented in a way that ensures balance in forest practice and yield.

Keywords

climate change final cutting age carbon storage national forestry inventory forest growth model 

References

  1. 1.
    IPCC. Climate Change 2007: The physical scientific basis. The Fourth Assessment Report of Working Group. Cambridge: Cambridge University Press, 2007Google Scholar
  2. 2.
    Bonan GB. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 2008, 320: 1444–1449PubMedCrossRefGoogle Scholar
  3. 3.
    Han WJ, Ahn S, Yoo G, Hwng JH, Kim JE, Lee JT, Na YE, Kim MH, Son Y, Lee WK, Cho Y, Han KJ, Kim JI, Choi EJ, Kim KN, Bae D, Jung IW. Climate change impact assessment and development of adaptation strategies in Korea II. Korea Environment Institute, 2006Google Scholar
  4. 4.
    Fuchs H, Magdon P, Kleinn C, Flessa H. Estimating aboveground carbon in a catchment of the Siberian forest tundra: combining satellite imagery and field inventory. Remote Sens Environ, 2009, 113: 518–531CrossRefGoogle Scholar
  5. 5.
    Neilson RP, Marks D. A global perspective of regional vegetation and hydrological sensitivities from climate change. J Veg Sci, 1994, 5: 175–730CrossRefGoogle Scholar
  6. 6.
    Box EO. Plant functional types and climate at the global scale. J Veg Sci, 1996, 7: 309–320CrossRefGoogle Scholar
  7. 7.
    Kong WS. Selection of vulnerable indicator plants by global warming. Asia-Pa J Atm Sci, 2005, 41: 263–273Google Scholar
  8. 8.
    Lee MA, Lee WK, Song CC, Lee JH, Choi HA, Kim TM. Spatio-temporal change prediction and variability of temperature and precipitation. J GIS Assoc KR, 2007, 15: 1–12Google Scholar
  9. 9.
    Choi S, Lee WK, Son Y, Yoo S. Lim JH. Changes in the distribution of South Korean forest vegetation simulated using thermal gradient indices. Sci China Life Sci, 2010, 53: 784–797PubMedCrossRefGoogle Scholar
  10. 10.
    Korea Forest Service. Statistical Yearbook of Forestry 2011. Korea Forest Service, Seoul, 2011Google Scholar
  11. 11.
    Arris LL, Eagleson PS. Evidence of a physiological basis for the boreal-deciduous forest ecotone in North America. Vegetation, 1989, 82: 55–58CrossRefGoogle Scholar
  12. 12.
    Neilson RP. Transient ecotone response to climate change: Some conceptual and modeling approaches. Ecol Appl, 1993, 3: 385–395CrossRefGoogle Scholar
  13. 13.
    Lenihan JM, Drapek R, Bachelet D, Neilson RP. Climate change effects on vegetation distribution, carbon, and fire in California. Ecol Appl, 1993, 13: 1667–1681CrossRefGoogle Scholar
  14. 14.
    Laurent JM, Bar-Hen A, Francois L, Ghislain M, Cheddadi R. Refining vegetation simulation models: from plant functional types to bioclimatic affinity groups of plants. J Veg Sci, 2004, 15: 739–746CrossRefGoogle Scholar
  15. 15.
    Matsui T, Yagihashi T, Nakaya T, Tanak N, Taoda H. Climate controls on distribution of Fagus crenata forests in Japan. J Veg Sci, 2004, 15: 57–66Google Scholar
  16. 16.
    Kwak DA, Lee WK, Son Y, Choi S, Yoo S, Chung DJ, Lee SH, Kim SH, Choi JK, Lee YJ, Byun WH. Predicting distributional change of forest cover and volume in future climate of South Korea. Forest Sci Technol, 2012, 8: 105–115CrossRefGoogle Scholar
  17. 17.
    Son YM, Lee KH, Kim RH, Pyo JK, Park IH, Son Y, LEE YJ, Kim CS. Carbon emission factor of major species for forest greenhouse gas inventory. Korea Forest Research Institute, 2010Google Scholar
  18. 18.
    Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomin AM. A global biome model based on plant physiology and dominance, soil properties and climate. J Biogeogr, 1992, 19: 117–134CrossRefGoogle Scholar
  19. 19.
    Lenihan JM, Neilson RP. A rule-based vegetation formation model for Canada. J Biogeogr, 1993, 20: 615–628CrossRefGoogle Scholar
  20. 20.
    Brzeziecki B, Kienast F, Wildi O. Modelling potential impacts of climate change on the spatial distribution of zonal forest communities in Switzerland. J Veg Sci, 1995, 6: 257–268CrossRefGoogle Scholar
  21. 21.
    Cao M, Woodward FI. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature, 1998, 393: 249–252CrossRefGoogle Scholar
  22. 22.
    Osborne CP, Mitchell PL, Sheehy JE, Woodward FI. Modeling the recent historical impacts of atmospheric CO2 and climate change on Mediterranean vegetation. Global Change Biol, 2000, 6: 445–458CrossRefGoogle Scholar
  23. 23.
    Bachelet D, Lenihan JM, Daly C, Neilson RP, Ojima DS, Parton WJ. MC1: a dynamic vegetation model for estimating the distribution of vegetation and associated carbon, nutrients, and water technical documentation. Version 1.0. Gen. Tech. Rep. PNW-GTR-508. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR, USA, 2001Google Scholar
  24. 24.
    Watanabe T, Yokozawa M, Emori S, Takata K, Sumida A, Hara T. Developing a multilayered integrated numerical model of surface physics—growing plants interaction (MINoSGI). Global Change Biol, 2004, 10: 963–982.CrossRefGoogle Scholar
  25. 25.
    Kim J, Lee D. A study on the vulnerability assessment of forest vegetation using regional climate model. J Korean Env Res Reveg Tech, 2006, 9: 32–40Google Scholar
  26. 26.
    Choi S, Lee WK, Kwak H, Kim SR, Yoo S, Choi HA, Park S, Lim JH. Vulnerability assessment of forest ecosystem to climate change in Korea using MC1 model. Jpn J For Plan, 2011, 16: 149–161Google Scholar
  27. 27.
    Kim SA, Lee WK, Son Y, Cho YS, Lee MS. Applicability of climate change impact assessment models to Korean forest. J Korean For Soc, 2009, 98: 1–16CrossRefGoogle Scholar
  28. 28.
    Choi S, Lee WK, Kwak DA, Lee S, Son Y, Lim JH, Saborowski J. Predicting forest cover changes in future climate using hydrological and thermal indices in South Korea. Climate Res, 2011, 49: 229–245CrossRefGoogle Scholar
  29. 29.
    Korea Forest Research Institute. Standardized Production System for Digital Forest Type Map (1:25000). Korea Forest Research Institute, Seoul, South Korea, 2008Google Scholar
  30. 30.
    Lee WK. Estimation of carbon stock in future climate using NFI and remotely sensed data in South Korea. International Symposium on a New Era of Forest Management for Ecosystem Services, 2012. 17–20Google Scholar
  31. 31.
    Korea Forest Research Institute. The 5th National Forestry Inventory Report. Korea Forest Research Institute, Seoul, South Korea, 2011Google Scholar
  32. 32.
    Lee CS, Lee WK, Yoon JH, Song CC. Distribution pattern of Pinus densiflora and Quercus spp. stand Korea using spatial statistics and GIS. J Korean For Soc, 2006, 95: 663–671Google Scholar
  33. 33.
    Korea Forest Service. Forest and forestry technique. Korea Forest Service, Daejeon, South Korea, 2000Google Scholar
  34. 34.
    Lull H W, Ellison L. Precipitation in Relation to Altitude in Central Utah. Ecology, 1950, 31:479–484CrossRefGoogle Scholar
  35. 35.
    Yun J I, Choi J Y, Ahn J H. Seasonal Trend of Elevation Effect on Daily Air Temperature in Korea. Korean J Agr For Meteorol, 2001, 3: 96–104Google Scholar
  36. 36.
    Cho H, Jeong J. Application of Spatial Interpolation to Rainfall Data. J GIS Assoc KR, 2006, 14: 29–41Google Scholar
  37. 37.
    Park N W, Jang D H. Mapping of Temperature and Rainfall Using DEM and Multivariate Kriging. J Korean Geogr Soc, 2008, 43: 1002–1015Google Scholar
  38. 38.
    IPCC. Special report on emissions scenarios. Cambridge University Press, Cambridge, United Kingdom, 2000Google Scholar
  39. 39.
    Sit V, Poulin-Costello M. Catalog of Curves for Curve Fitting. Forest Science Research Branch, Ministry of Forests, Victoria, BC, Canada, 1994Google Scholar
  40. 40.
    SAS Institute Inc. SAS user guide. SAS Institute Inc., Cary, NC, USA, 2010Google Scholar
  41. 41.
    Lee WK. Stand and general height-DBH curve models for Pinus densiflora in KangWon Province. Korean J For Economics, 1996, 4: 66–78Google Scholar
  42. 42.
    Gadow Kv, Hui GY. Zur Entwicklung von Einheitshöhenkurven am Beispiel der Baumart Cunninghamia lanceolata. Allgemeine Forst und Jagd Zeitung, 1993, 110: 41–48Google Scholar
  43. 43.
    Wenk G, Antanaitis V, Šmelko Š. Waldertragslehre. Deutscher Landwirtschaftsverlag, Berlin, Germany, 1990Google Scholar
  44. 44.
    Michailow I. Zahlenmäβiges Verfahren für die Ausführung der Bestandeshöhenkurven. Fw Cbl U That Forstl Jahrb Heft, 1943, 6: 273–279Google Scholar
  45. 45.
    Gadow Kv. Wachstums- und Ertragsmodelle für die Forsteinrichtung. Deutscher Verband Forstl Forsch Anst, Sektion Ertragskunde. Jahrestagung in Grillenberg, 1992Google Scholar
  46. 46.
    Lee WK. A Dynamic regional forest management model for the sustainability of forest practice-considering forest growth and economical conditions. Korean J For Economics, 1995 3: 71–98Google Scholar
  47. 47.
    Woo J, Ahn J, Yoon H, Lee D, Lee S. Forest management. Hyanmunsa, 2007, 78–242Google Scholar
  48. 48.
    Zhao DH, Kane M, Borders BE. Development and applications of the relative spacing model for loblolly pine plantations. For Ecol Manage, 2010, 259: 1922–1929CrossRefGoogle Scholar
  49. 49.
    Byun WH, Lee WK, Bae SW. Forest growth. Yoochun Media, 1996, 93–123Google Scholar
  50. 50.
    Lee WK, Seo JH, Bae SW. Maximum stem number and mortality model for even-aged Pinus densiflora stand in Kangwon-Province, Korea. J Korean For Soc, 2000, 89: 634–644Google Scholar
  51. 51.
    Schabenberger O, Pierce FJ. Contemporary statistical models for the plant and soil sciences. New York: CRC Press, 2002Google Scholar
  52. 52.
    Reineke LH. Perfecting a stand density index for even-aged forests. J Agric Res, 1993, 46: 627–638Google Scholar
  53. 53.
    Korea Forest Service. The 5th Forest Master Plan. Korea Forest Service, Daejeon, South Korea, 2005Google Scholar
  54. 54.
    Yu H, Lee WK, Son Y, Kwak DA, Nam KJ, Kim MI, Byun JY, Lee SJ, Kwon T. Estimating carbon stock in Korean forests between 2010 and 2110: A prediction based on forest volume-age relationships. For Sci Technol, 2013, 9: 105–110Google Scholar
  55. 55.
    Lee WK, Biging GS, von Gadow Kv, Byun WH. A forest planning model for continuous employment in a forested village with primarily young stands in Korea. New Forests, 2005, 29: 15–32CrossRefGoogle Scholar
  56. 56.
    Byun JG, Lee WK, Kim MI, Kwak DA, Kwak H, Park T, Byun WH, Son Y, Choi JK, Lee YJ, Saborowski J, Chung DJ, Jung JH. Radial growth response of Pinus densiflora and Quercus spp. to topographic and climatic factors in South Korea. J Plant Ecol, 2013, 6: 380–392CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Kijun Nam
    • 1
  • Woo-Kyun Lee
    • 1
  • Moonil Kim
    • 1
  • Doo-Ahn Kwak
    • 2
  • Woo-Hyuk Byun
    • 1
  • Hangnan Yu
    • 1
  • Hanbin Kwak
    • 2
  • Taesung Kwon
    • 3
  • Joohan Sung
    • 3
  • Dong-Jun Chung
    • 4
  • Seung-Ho Lee
    • 5
  1. 1.Division of Environmental Science and Ecological EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.GIS/RS Center for Environmental ResourcesKorea UniversitySeoulRepublic of Korea
  3. 3.Division of Forest EcologyKorea Forest Research InstituteSeoulRepublic of Korea
  4. 4.National Forestry Cooperative FederationDaejeonRepublic of Korea
  5. 5.Division of Forest Economics & ManagementKorea Forest Research InstituteSeoulRepublic of Korea

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