Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Analysis of the MODIS-Based Vegetation Phenology Using the HANTS Algorithm

HANTS 알고리즘을 이용한 MODIS 영상기반의 식물계절 분석

  • Choi, Chul-Hyun (Dept. of Landscape Architecture, Kyungpook National University) ;
  • Jung, Sung-Gwan (Dept. of Landscape Architecture, Kyungpook National University)
  • Received : 2014.05.02
  • Accepted : 2014.07.02
  • Published : 2014.09.30
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Abstract

Vegetation phenology is the most important indicator of ecosystem response to climate change. Therefore it is necessary to continuously monitor forest phenology. This paper analyzes the phenological characteristics of forests in South Korea using the MODIS vegetation index with error from clouds or other sources removed using the HANTS algorithm. After using the HANTS algorithm to reduce the noise of the satellite-based vegetation index data, we were able to confirm that phenological transition dates varied strongly with altitudinal gradients. The dates of the start of the growing season, end of the growing season and the length of the growing season were estimated to vary by +0.71day/100m, -1.33day/100m and -2.04day/100m in needleleaf forests, +1.50day/100m, -1.54day/100m and -3.04day/100m in broadleaf forests, +1.39day/100m, -2.04day/100m and -3.43day/100m in mixed forests. We found a linear pattern of variation in response to altitudinal gradients that was related to air temperature. We also found that broadleaf forests are more sensitive to temperature changes compared to needleleaf forests.

식물계절은 기후변화와 관련된 생태계의 중요한 지표로서 지속적인 모니터링을 필요로 한다. 본 연구에서는 Moderate Resolution Imaging Spectrometer(MODIS) 위성영상을 기반으로 구름이나 기타 영향에 의한 오류를 보정한 식생지수를 통해 국내 산림의 식물계절학적 특성을 분석하였으며, 이에 Harmonic Analysis of NDVI Time-Series(HANTS) 알고리즘을 이용하였다. 그 결과, 위성영상 기반 식생지수의 노이즈를 효과적으로 감소시킬 수 있었으며, 고도에 의한 식물계절 변화 및 경향을 파악할 수 있었다. 식물계절시기는 고도에 따른 변화경향이 뚜렷하게 나타났으며, 침엽수의 경우 생육개시일과 생육종료일, 생육기간은 각각 +0.71일/100m, -1.33일/100m, -2.04일/100m, 활엽수의 경우 +1.50일/100m, -1.54/100m, -3.04/100m, 혼효림의 경우 +1.39일/100m, -2.04일/100m, -3.43일/100m로 분석되었다. 고도에 따른 식물계절시기의 변화는 기온과 관련된 것으로 판단되며, 활엽수림이 침엽수림보다 변화에 대한 민감도가 더 높은 것으로 나타났다.

Keywords

References

  1. Brent, R. P. 2013. Algorithms for minimization without derivatives. Courier Dover Publications.
  2. Chen, J., Z. Gu, M. Tamura, B. Matsushita, P. Jonsson and L. Eklundh. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment 91(3- 4):332-344. https://doi.org/10.1016/j.rse.2004.03.014
  3. Chmielewski, F.-M., A. Muller and E. Bruns. 2004. Climate changes and trends in phenology of fruit trees and field crops in Germany. Agricultural and Forest Meteorology 121(1-2):69-78. https://doi.org/10.1016/S0168-1923(03)00161-8
  4. de Beurs, K.M., and G.M. Henebry. 2008. Northern annular mode effects on the land surface phenologies of northern eurasia. Journal of Climate 21(17): 4257-4279. https://doi.org/10.1175/2008JCLI2074.1
  5. De Wit, A.J.W. and B. Su. 2005. Deriving phenological indicators from SPOT-VGT data using the HANTS algorithm. Proceedings of the 2nd international SPOT-VEGETATION user conference pp.195-201.
  6. Edwards, M. and A.J. Richardson, 2004. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430(7002):881-884. https://doi.org/10.1038/nature02808
  7. Eun, J. 2012. Development of integration method between MODIS NDVI and AVHRR NDVI. Thesis, Univ. of Inha, Incheon, Korea. 16pp (은정. 2012. MODIS NDVI와 AVHRR NDVI의 연계방법 개발. 인하대학교 대학원 석사학위논문. 16쪽).
  8. Fischer, A. 1994. A model for the seasonal variation of seasonal of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment 48(2):220-230. https://doi.org/10.1016/0034-4257(94)90143-0
  9. Fisher, J.I., J.F. Mustard and A. Vadeboncoeur. 2006. Green leaf phenology at landsat resolution: scaling from the field to the satellite. Remote Sensing of Environment 100(2):265-279. https://doi.org/10.1016/j.rse.2005.10.022
  10. Fridely, J.D., 2012. Extended leaf phenology and the autumn niche in deciduous forest invasions. Nature 485(7398):359-362. https://doi.org/10.1038/nature11056
  11. Hopkins, A.D. 1920. The bioclimatic law. Monthly Weather Review 48(6):355-355.
  12. Huh, Y., Y.G. Byun, Y.I. Kim and K.Y. Yu. 2007. A study on cloud-free MODIS NDVI time series reconstruction using HANTS algorithm. Proceedings of the Journal of Korea Spatial Information Society 2007 pp.169-174 (허용, 변영기, 김용일, 유기윤. 2007. HANTS 알고리즘을 이용한 MODIS NDVI 시계열 영상의 구름 화소 문제 해결에 관한 연구. 한국GIS학회 춘.추계 학술대회 발표논문 초록집. 2007: 169-174쪽).
  13. IPCC. 2007. Climate change 2007: synthesis report. IPCC Fourth Assessment Report. Geneva, Switzerland, pp.104-105.
  14. IPCC. 2013. Summary for policy makers. In: T.F. Stoker et al.(eds.). Climate Change 2013: The Physical Science Basis. Contribution of Working Group to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  15. Jakubauskas, M.E., D.R. Legates and J.H. Kastens. 2001. Harmonic analysis of time-series AVHRR NDVI data. Photogrammetric Engineering and Remote Sensing 67(4):461-470.
  16. Jo, H.K. and T.W Ahn. 2008. Differences in phenological phases of plants subsequent to microclimate change. Korean Journal of Environment and Ecology 22(3):221-229 (조현길, 안태원. 2008. 미기후 변화에 따른 식물계절 차이. 한국환경생태학회지 22(3):221- 229).
  17. Jonsson, P., and L. Eklundh. 2004. TIMESAT - a program for analyzing time-series of satellite sensor data. Computers and Geosciences 30(8):833-845. https://doi.org/10.1016/j.cageo.2004.05.006
  18. Kang, S.G. 2005. Analysis on cloudoriginated errors of MODIS leaf area index and primary production images: effect of monsoon climate in Korea. Journal of Ecology and Environment 28(4):215-222 (강신규. 2005. MODIS 엽면적지수 및 일차생산성 영상의 구름 영향 오차 분석. 한국생태학회지 28(4):215-222).
  19. Kim, B.D., S.G. Kang, S.T. Yu, H.T. Shin, K.H. Park, M.H., Yi, J.W. Yoon, G.S. Kim and J.W. Sung. 2012. A study on the plants for phenology of the Mt. Gaya national park. Journal of Climate Reserch 7(2):174-186 (김병도, 강신구, 유성태, 신현탁, 박기환, 이명훈, 윤정원, 김기송, 성정원. 2012. 가야산국립공원 식물종의 생물계절성 연구. 기후연구 7(2):174-186).
  20. Kim, M.K., D.H. Lee and J.U. Kim. 2013a. Production and validation of daily grid data with 1km resolution in South Korea. Journal of Climate Research 8(1):13-25 (김맹기, 이대희, 김진욱. 2013a. 남한 1km 해상도의 격자형 일 기상 자료 생산과 검증. 기후연구 8(1):13-25).
  21. Kim, N.S., H.C. Lee and J.Y. Cha. 2013b. A study on changes of phenology and characteristics of spatial distribution using MODIS images. Journal of the Korea Society of Environmental Restoration Technology 16(5):56-69 (김남신, 이희천, 차진열. 2013b. MODIS 영상을 이용한 식물계절의 변화와 공간적 분포 특징에 관한 연구. 한국환경복원기술학회지 16(5):59-69). https://doi.org/10.13087/kosert.2013.16.5.059
  22. Kramer, K. 1996. Phenology and growth of European trees in relation to climate change. Thesis, Landbouw Universiteit Wageningen.
  23. Lee, K.M., W.T Kwon and S.G. Lee. 2009. Articles: a study on plant phenological trends in South Korea. Journal of The Korean Association of Regional Geographers 15(3):337-350 (이경미, 권원태, 이승호. 2009. 우리나라 식물계절 시기의 변화 경향에 관한 연구. 한국지역지리학회지 15(3):337-350).
  24. Lee, W.S., S.G. Jung, K.H. Park and K.T. Kim. 2010. Analysis of urban thermal environment for environment-friendly spatial plan. Journal of the Korean Association of Geographic Information Studies 13(1):142-154 (이우성, 정성관, 박경훈, 김경태. 2010. 친환경적 공간계획을 위한 도시의 열환경 분석. 한국지리정보학회지 13(1):142-154).
  25. Liu, H.Q. and A. Huete. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. Geoscience and Remote Sensing, IEEE Transactions on, 33(2):457-465. https://doi.org/10.1109/36.377946
  26. Liu, L., L. Liu and Y. Hu. 2012. Response of spring phenology to climate change across Tibetan Plateau. Proceedings of the Remote Sensing, Environment and Transportation Engineering(RSETE) 2012 2nd International Conference on pp.1-4.
  27. Lloyd, D. 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing 11(12):2269-2279. https://doi.org/10.1080/01431169008955174
  28. Lu, X., R. Liu, J. Liu and S. Liang. 2007. Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogrammetric Engineering and Remote Sensing 73(10):1129-1139.
  29. Markon, C.J., M.D. Fleming and E.F. Binnian. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record 31(177):179-190. https://doi.org/10.1017/S0032247400013681
  30. Menenti, M., S. Azzali, W. Verhoef and R. van Swol. 1993. Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images. Advances in Space Research 13(5):233-237.
  31. Monson, R.K., J.P. Sparks, T.N. Rosenstiel, L.E. Scott-Denton, T.E. Huxman, P.C. Harley, A.A. Turnipseed, S.P. Burns, B. Backlund and J. Hu. 2005. Climatic influences on net ecosystem $CO_{2}$ exchange during the transition from wintertime carbon source to springtime carbon sink in a high-elevation, subalpine forest. Oecologia 146(1):130-147. https://doi.org/10.1007/s00442-005-0169-2
  32. Moulin, S., N. Kergoat, N. Viovy, and G. Dedieu. 1997. Global-scale assessment of vegetation phonology using NOAA/ AVHRR satellite measurements. Journal of Climate 10(6):1154-1170. https://doi.org/10.1175/1520-0442(1997)010<1154:GSAOVP>2.0.CO;2
  33. Niu, S., Y. Fu, L. Gu and Y. Luo. 2013. Temperature sensitivity of canopy photosynthesis phenology in northern ecosystems. Phenology: An Integrative Environmental Science, Springer. pp.503-519.
  34. Park, S.Y. 2013. Satellite-measured vegetation phenology and atmospheric aerosol time series in the Korean peninsula. Journal of the Korean Geographical Society 48(4):497-508 (박선엽. 2013. 위성기반의 한반도 식물계절학적 패턴과 대기 에어로졸의 시계열 특성 분석. 대한지리학회지 48(4):497-508).
  35. Reed, B.C., J.F. Brown, D. Vanderaee, T.R. Loveland, J.W. Merchant, and D.O. Ohlen. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science 5(5): 703-714. https://doi.org/10.2307/3235884
  36. Roerink, G.J., M.H.G.I. Danes, A.J.W. De Wit, O.G. Prieto and A.J.H. Van Vliet. 2011. Deriving plant phenology from remote sensing. Proceedings of the 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011 2011:261-264.
  37. Roerink, G.J., M. Menenti and W. Verheof. 2000. Reconstructing cloudfree NDVI composites using fourier analysis of time series. International Journal of Remote Sensing 21(9):1911-1917. https://doi.org/10.1080/014311600209814
  38. Rouse, J.W., R.H. Haas, J.A. Schell and D.W. Deering. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the 3rd ERTS Symposium 1 pp.48-62.
  39. Rotzer, T., and F.M. Chmielewski. 2001. Phenological maps of Europe. Climate Research 18(3):249-257. https://doi.org/10.3354/cr018249
  40. Schwartz, M.D., B.C. Reed and M.A.White. 2002. Assessing satellite-derived start-of-season measures in the conterminous USA. International Journal of Climatology 22(14):1793-1805. https://doi.org/10.1002/joc.819
  41. Song, B.G. and K.H. Park. 2012. Analysis of heat island characteristics considering urban space at nighttime. Journal of the Korean Association of Geographic Information Studies 15(1): 133-143 (송봉근, 박경훈. 2012. 도시공간을 고려한 야간시간대의 열섬특성 분석. 한국지리정보학회지 15(1):133-143). https://doi.org/10.11108/kagis.2012.15.1.133
  42. Soudani, K., G. le Maire, E. Dufrene, C. Francois, N. Delpierre, E. Ulrich and S. Cecchini. 2008. Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sensing of Environment. 112(5):2643-2655. https://doi.org/10.1016/j.rse.2007.12.004
  43. Tan, B., F. Gao, R.E. Wolfe, J. Nightingale, J.A.Pedelty, J.T. Morisette and G.A. Ederer. 2011. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4(2):361-371. https://doi.org/10.1109/JSTARS.2010.2075916
  44. Vitasse, Y., S. Delzon, J.-M. Louvet, A. Kremer, R. Michalet, E. Dufrene and J.-Y. Pontailler. 2009. Leaf phenology sensitivity to temperature in European trees: do within-species populations exhibit similar responses?. Agricultural and Forest Meteorology 149(5):735-744. https://doi.org/10.1016/j.agrformet.2008.10.019
  45. White, M.A., G. Zhang, K.M. de Beurs, K. Didan, D.W. Inouye, A.D. Richardson, O.P. Jensen, J. O'Keefe, R.R. Nemani, W.J.D. van Leeuwen, J.F. Brown, A. de Wit, M. Schaepman, X. Lin, M. Dettinger, M. Bailey, J. Kimball, M.D. Schwartz, D.D. Baldocchi, J.T. Lee and W.K. Lauenroth. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology 15(10):2335-2359. https://doi.org/10.1111/j.1365-2486.2009.01910.x
  46. White, M.A., M.D. Schwartz and Running, S.W. 2000. Young students, satellites aid understanding of climate biosphere link. Eos, Transactions American Geophysical Union 81(1):1-5.
  47. White, M.A., P.E. Thornthon and S.W. Running. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global biochemical cycles 11(2):217-234. https://doi.org/10.1029/97GB00330
  48. White, M.A., S.W. Running and P.E. Thornton. 1999. The impact of growing -season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest. International Journal of Biometeorology 42(3):139-145. https://doi.org/10.1007/s004840050097
  49. Zhang, X., M.A. Friedl, C.B. Schaaf and A.H. Strahler. 2004. Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data. Global Change Biology 10(7):1133-1145. https://doi.org/10.1111/j.1529-8817.2003.00784.x
  50. Zhang, X., M.A. Friedl, C.B. Schaaf, A.H. Strahler, J.C.F. Hodges, F. Gao, B.C. Reed, and A. Huete. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84(3): 471-475. https://doi.org/10.1016/S0034-4257(02)00135-9

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