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Analysis of Vulnerable Regions of Forest Ecosystemin the National Parks based on Remotely-sensed Data

원격탐사자료에 기초한 국립공원 산림 생태계의 취약지역 분석

  • Received : 2016.09.21
  • Accepted : 2016.10.28
  • Published : 2016.10.31

Abstract

This study identified vulnerable regions in the national parks of the Republic of Korea (ROK). The potential vulnerable regions were defined as areas showing a decline in forest productivity, low resilience, and high sensitivity to climate variations. Those regions were analyzed with a regression model and trend analysis using the Enhanced Vegetation Index (EVI) data obtained from long-term observed Moderate Resolution Imaging Spectroradiometer (MODIS) and gridded meteorological data. Results showed the area with the highest vulnerability was Naejangsan National Park in the southern part of ROK where 32.5% ($26.0km^2$) of the total area was vulnerable. This result will be useful information for future conservation planning of forest ecosystem in ROK under environmental changes, especially climate change.

Keywords

References

  1. Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., ... & Gonzalez, P. .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
  2. Bentz, B. J., REgniEre, J., Fettig, C. J., Hansen, E. M., Hayes, J. L., Hicke, J. A., ... & Seybold, S. J. 2010. Climate change and bark beetles of the western United States and Canada: direct and indirect effects. BioScience 60(8): 602-613. https://doi.org/10.1525/bio.2010.60.8.6
  3. Bivand R. and D. Yu. 2015. spgwr: Geographically Weighted Regression. R package version 0.6-28. https://CRAN.R-projectorg/package=spgwr
  4. Braswell, B. H., Schimel, D. S., Linder, E., & Moore, B. I. I. I. 1997. The response of global terrestrial ecosystems to interannual temperature variability. Science 278(5339): 870-873. https://doi.org/10.1126/science.278.5339.870
  5. Carver, S. J. 1991. Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information System, 5(3): 321-339. https://doi.org/10.1080/02693799108927858
  6. Choi, K. 2014. Research diameter growth response of major tree species to climatic and topographic condition using tree-ring data of national forest inventory. Master's Thesis. University of Korea. (in Korean with English abstract)
  7. Chuai, X. W., Huang, X. J., Wang, W. J., & Bao, G. 2013. NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998 -2007 in Inner Mongolia, China. International journal of climatology 33(7): 1696-1706. https://doi.org/10.1002/joc.3543
  8. Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. 1990. STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics 6(1): 3-73.
  9. Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A., Brovkin, V., ... & Kucharik, C. 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global change biology 7(4): 357-373. https://doi.org/10.1046/j.1365-2486.2001.00383.x
  10. Dakos, V., Carpenter, S. R., Brock, W. A., Ellison, A. M., Guttal, V., Ives, A. R., ... & Scheffer, M. 2012. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PloS one 7(7): e41010. https://doi.org/10.1371/journal.pone.0041010
  11. Dakos, V., Carpenter, S. R., van Nes, E. H., & Scheffer, M. 2015. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philosophical Transactions of the Royal Society B: Biological Sciences 370(1659): 20130263. https://doi.org/10.1098/rstb.2013.0263
  12. Eastman, J. R. (2012). IDRISI Selva Tutorial, Manual Version 17.
  13. Field, C. B., Randerson, J. T., & Malmström, C. M. 1995. Global net primary production: combining ecology and remote sensing. Remote sensing of Environment 51(1): 74-88. https://doi.org/10.1016/0034-4257(94)00066-V
  14. Fotheringham, A. S., Brunsdon, C., & Charlton, M. 2003. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.
  15. Ryan Hafen. 2016. stlplus: Enhanced Seasonal Decomposition of Time Series by Loess. R package version 0.5.1. https://CRAN.Rproject.org/package=stlplus.
  16. Herrmann, S. M., Anyamba, A., & Tucker, C. J. 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change 15(4): 394-404. https://doi.org/10.1016/j.gloenvcha.2005.08.004
  17. Heyder, U., Schaphoff, S., Gerten, D., & Lucht, W. 2011. Risk of severe climate change impact on the terrestrial biosphere. Environmental Research Letters 6(3): 034036. https://doi.org/10.1088/1748-9326/6/3/034036
  18. Hijmans, Robert J. 2015. raster: Geographic Data Analysis and Modeling. R package version 2.4-20. https://CRAN.R-project.org/package=raster.
  19. Holling, C. S. 1973. Resilience and stability of ecological systems. Annual review of ecology and systematics 1-23.
  20. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, & L.G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(1): 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  21. IPCC. 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability: Summary for Policymakers.
  22. Keersmaecker, W., Lhermitte, S., Honnay, O., Farifteh, J., Somers, B., & Coppin, P. 2014. How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Global change biology 20(7): 2149-2161. https://doi.org/10.1111/gcb.12495
  23. Keersmaecker, W., Lhermitte, S., Tits, L., Honnay, O., Somers, B., & Coppin, P. 2015. A model quantifying global vegetation resistance and resilience to short‐term climate anomalies and their relationship with vegetation cover. Global Ecology and Biogeography 24(5): 539-548. https://doi.org/10.1111/geb.12279
  24. Levine, N. M., Zhang, K., Longo, M., Baccini, A., Phillips, O. L., Lewis, S. L., ... & Feldpausch, T. R. 2016. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proceedings of the National Academy of Sciences 113(3): 793-797.
  25. Lloret, F., Lobo, A., Estevan, H., Maisongrande, P., Vayreda, J., & Terradas, J. 2007. Woody plant richness and NDVI response to drought events in Catalonian (northeastern Spain) forests. Ecology 88(9): 2270-2279. https://doi.org/10.1890/06-1195.1
  26. Lopatin, E., Kolstrom, T., & Spiecker, H. 2006. Determination of forest growth trends in Komi Republic (northwestern Russia): combination of tree-ring analysis and remote sensing data. Boreal Environment Research 11(5): 341.
  27. Mildrexler, D. J., Zhao, M., Heinsch, F. A., & Running, S. W. 2007. A new satellitebased methodology for continental‐scale disturbance detection. Ecological Applications 17(1): 235-250. https://doi.org/10.1890/1051-0761(2007)017[0235:ANSMFC]2.0.CO;2
  28. Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., ... & Running, S. W. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. science 300(5625): 1560-1563. https://doi.org/10.1126/science.1082750
  29. Pimm, S. L. 1984. The complexity and stability of ecosystems. Nature 307(5949): 321-326. https://doi.org/10.1038/307321a0
  30. Prince, S. D., Goetz, S. J., & Goward, S. N. 1995. Monitoring primary production from earth observing satellites. Water, air, and soil pollution 82(1-2): 509-522. https://doi.org/10.1007/BF01182860
  31. Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 351: 309.
  32. Seddon, A. W., Macias-Fauria, M., Long, P. R., Benz, D., & Willis, K. J. 2016. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531(7593): 229-232. https://doi.org/10.1038/nature16986
  33. Simoniello, T., Lanfredi, M., Liberti, M., Coppola, R., & Macchiato, M. 2008. Estimation of vegetation cover resilience from satellite time series. Hydrology and Earth System Sciences Discussions 5(1): 511-546. https://doi.org/10.5194/hessd-5-511-2008
  34. Team, R. C. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
  35. Telesca, L., & Lasaponara, R. 2006. Quantifying intra-annual persistent behaviour in SPOTVEGETATION NDVI data for Mediterranean ecosystems of southern Italy. Remote Sensing of Environment 101(1): 95-103. https://doi.org/10.1016/j.rse.2005.12.007
  36. Van Nes, E. H., & Scheffer, M. 2007. Slow recovery from perturbations as a generic indicator of a nearby catastrophic shift. The American Naturalist 169(6): 738-747. https://doi.org/10.1086/516845
  37. Van Ruijven, J., & Berendse, F. 2010. Diversity enhances community recovery, but not resistance, after drought. Journal of Ecology 98(1): 81-86. https://doi.org/10.1111/j.1365-2745.2009.01603.x
  38. Vogel, A., Scherer-Lorenzen, M., & Weigelt, A. 2012. Grassland resistance and resilience after drought depends on management intensity and species richness. PLoS One 7(5): e36992. https://doi.org/10.1371/journal.pone.0036992
  39. Wang, J., Price, K. P., & Rich, P. M. 2001. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International Journal of Remote Sensing 22(18): 3827-3844. https://doi.org/10.1080/01431160010007033
  40. Williams, A. P., Allen, C. D., Macalady, A. K., Griffin, D., Woodhouse, C. A., Meko, D. M., ... & Dean, J. S. 2013. Temperature as a potent driver of regional forest drought stress and tree mortality. Nature Climate Change 3(3): 292-297. https://doi.org/10.1038/nclimate1693
  41. Yang, L., Wylie, B. K., Tieszen, L. L., & Reed, B. C. 1998. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains. Remote Sensing of Environment 65(1): 25-37. https://doi.org/10.1016/S0034-4257(98)00012-1
  42. Yang, X., Xie, X., Liu, D. L., Ji, F., & Wang, L. 2015. Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region. Advances in Meteorology, 2015.
  43. Zhao, Z., Gao, J., Wang, Y., Liu, J., & Li, S. 2015. Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression. Theoretical and Applied Climatology 120(3-4): 507-519. https://doi.org/10.1007/s00704-014-1188-x