References
- Ahas, R. and Aasa, A. 2006. The effects of climate change on the phenology of selected Estonian plant, bird and fish populations. International Journal of Biometeorology 51(1): 17-26. https://doi.org/10.1007/s00484-006-0041-z
- Ahrends, H.E., Etzold, S., Kutsch, W.L., Stockli, R., Brügger, R., Jeanneret, F., Wanner, H., Buchmann, N. and Eugster, W. 2009. Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale. Climate Research 39(3): 261-274. https://doi.org/10.3354/cr00811
- Alberton, B., Almeida, J. Helm, R. Torres, R.S., Menzel, A. and Morellato, L.P. 2014. Using phenological cameras to track the green up in a cerrado savanna and its on-theground validation. Ecological Informatics 19: 62-70. https://doi.org/10.1016/j.ecoinf.2013.12.011
- Alberton, B., Torres, R.S., Cancian, L.F., Borges, B.D., Almeida, J., Mariano, G.C., Santos, J. and Morellato, L.P.C. 2017. Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation. Perspectives in Ecology and Conservation 15(2): 82-90. https://doi.org/10.1016/j.pecon.2017.06.004
- Baghzouz, M., Devitt, D.A., Fenstermaker, L.F. and Young, M.H. 2010. Monitoring vegetation phenological cycles in two different semi-arid environmental settings using a ground-based NDVI system: A potential approach to improve satellite data interpretation. Remote Sensing 2(4): 990-1013. https://doi.org/10.3390/rs2040990
- Brown, T.B., Hultine, K.R., Steltzer, H., Denny, E.G., Denslow, M.W., Granados, J., Henderson, S., Moore, D., Nagai, S. and SanClements, M. 2016. Using phenocams to monitor our changing Earth: toward a global phenocam network. Frontiers in Ecology and the Environment 14(2): 84-93. https://doi.org/10.1002/fee.1222
- Chai, T. and Draxler, R.R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature. Geoscientific Model development 7(3): 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
- Choi, C.H., Jung, S.G. and Park, K.H. 2016. Analysing relationship between satellite-based plant phenology and temperature. Journal of the Korean Association of Geographic Information Studies 19(1): 30-42. https://doi.org/10.11108/kagis.2016.19.1.030
- Crick, H.Q., Dudley, C., Glue, D.E. and Thomson, D.L. 1997. UK birds are laying eggs earlier. Nature 388(6642): 526-526.
- Filippa, G., Cremonese, E., Galvagno, M., Migliavacca, M., Di Cella, U.M., Petey, M. and Siniscalco, C. 2015. Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol. International journal of biometeorology 59(12): 1927-1937. https://doi.org/10.1007/s00484-015-0999-5
- Fisher, J.I., Mustard, J.F. and Vadeboncoeur, M.A. 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
- Garrity, S.R., Vierling, L.A. and Bickford, K. 2010. A simple filtered photodiode instrument for continuous measurement of narrowband NDVI and PRI over vegetated canopies. Agricultural and Forest Meteorology 150(3): 489-496. https://doi.org/10.1016/j.agrformet.2010.01.004
- Graham, E.A., Riordan, E.C., Yuen, E.M., Estrin, D. and Rundel, P.W. 2010. Public Internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. Global Change Biology 16(11): 3014-3023. https://doi.org/10.1111/j.1365-2486.2010.02164.x
- Gu, L., Post, W.M., Baldocchi, D.D., Black, T.A., Suyker, A.E., Verma, S.B, Vesala, T. and Wofsy, S.C. 2009. Characterizing the seasonal dynamics of plant community photosynthesis across a range of vegetation types. Phenology of Ecosystem Processes. Springer 35-58.
- Hufkens, K., Filippa, G., Cremonese, E., Migliavacca, M., D'Odorico, P., Peichl, M., Gielen, B., Hortnagl, L., Soudani, K., Papale, D., Rebmann, C., Brown, T. and Wingate, L. 2018. Assimilating phenology datasets automatically across ICOS ecosystem stations. Agricultural and Forest Meteorology 249: 275-285. https://doi.org/10.1016/j.agrformet.2017.11.003
- Ide, R. and Oguma, H. 2010. Use of digital cameras for phenological observations. Ecological Informatics 5(5): 339-347. https://doi.org/10.1016/j.ecoinf.2010.07.002
- IPCC (Intergovernmental Panel on Climate Change). 2018. Summary for Policymakers. In: Global Warming of 1.5℃. World Meteorological Organization. Switzerland pp. 32.
- Jang, J.G., Yoo, S.T., Kim, B.D., Son, S.W. and Yi, M.H. 2020. The Relationship Between Temperature and Spring Phytophenological Index. Korean Journal of Plant Resources 33(2): 106-115.
- Jin, Y.H., Zhu, J.R., Sung, S.Y. and Lee, D.K. 2017. Application of satellite data spatiotemporal fusion in predicting seasonal NDVI. Korean Journal of Remote Sensing 33(2): 149-158. https://doi.org/10.7780/kjrs.2017.33.2.4
- Kim, H.J., Hong, J.K., Kim, S.C., Oh, S.H. and Kim, J.H. 2011. Plnat phenology of threatened species for climate change in sub-alpine zone of Korea. Korea Journal of Plant Resources 24(5): 549-556. https://doi.org/10.7732/kjpr.2011.24.5.549
- Kim, H.W. 2019. Changes of the flowering time of tree in spring by climate change in Seoul, South Korea. Seoul. Ewha Womans University.
- Kurc, S.A. and Benton, L.M. 2010. Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland. Journal of Arid Environments 74(5): 585-594. https://doi.org/10.1016/j.jaridenv.2009.10.003
- Lee, B.R., Kim, E.S., Lee, J.S., Chung, J.M. and Lim, J.H. 2018. Detecting phenology using MODIS vegetation indices and forest type map in South Korea. Korean Journal of Remote Sensing 34(2-1): 267-282. https://doi.org/10.7780/KJRS.2018.34.2.1.9
- Lee, K.M., Kwon, W.T. and Lee, S.H. 2009. A study on plant phenological trends in South Korea. Journal of The Koraen Association of Regional Geographers 15(2): 337-350.
- Menzel, A. 2000. Trends in phenological phases in Europe between 1951 and 1996. International Journal of Bio-meteorology 44(2): 76-81. https://doi.org/10.1007/s004840000054
- Migliavacca, M., Galvagno, M., Cremonese, E., Rossini, M., Meroni, M., Sonnentag, O., Cogliati, S., Manca, G., Diotri, F. and Busetto, L. 2011. Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake. Agricultural and Forest Meteorology 151(10): 1325-1337. https://doi.org/10.1016/j.agrformet.2011.05.012
- Morellato, L.P.C., Alberton, B., Alvarado, S.T., Borges, B., Buisson, E., Camargo, M.G.G., Cancian, L.F., Carstensen, D.W., Escobar, D.F. and Leite, P.T. 2016. Linking plant phenology to conservation biology. Biological Conservation 195: 60-72. https://doi.org/10.1016/j.biocon.2015.12.033
- Morisette, J.T., Richardson, A.D., Knapp, A.K., Fisher, J.I., Graham, E.A., Abatzoglou J., Wilson, B.E., Breshears, D.D., Henebry, G.M., Hanes, J.M. and Liang, L. 2008. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Frontiers in Ecology and the Environment 7(5): 253-260. https://doi.org/10.1890/070217
- Moulin, S., Kergoat, L., Viovy, N. and Dedieu, G. 1997. GlobalScale Assessment of Vegetation Phenology 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
- Parry, M.L., Canziani, O.F., Palutikof, J.P., Linden, P.J. and Hanson, C.E. 2007. Climate Change 2007: Impacts, Adaptation, and Vulnerability. Cambridge University Press pp. 939.
- Pau, S., Wolkovich, E.M., Cook, B.I., Davies, T.J., Kraft, N.J.B., Bolmgren, K., Betancourt, J.L. and Cleland, E.E. 2011. Predicting phenology by integrating ecology, evolution and climate science. Global Change Biology 17(12): 3633-3643. https://doi.org/10.1111/j.1365-2486.2011.02515.x
- Petach, A.R., Toomey, M., Aubrecht, D.M. and Richardson, A.D. 2014. Monitoring vegetation phenology using an infrared-enabled security camera. Agricultural and forest meteorology 195: 143-151. https://doi.org/10.1016/j.agrformet.2014.05.008
- Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J. and Stenseth, N.C. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution 20(9): 503-510. https://doi.org/10.1016/j.tree.2005.05.011
- Polgar, C.A. and Primack, R.B. 2011. Leaf‐out phenology of temperate woody plants: from trees to ecosystems. New Phytologist 191(4): 926-941. https://doi.org/10.1111/j.1469-8137.2011.03803.x
- R-Forge Phenopix. https://r-forge.r-project.org/projects/phenopi/. (2020.06.04.).
- Richardson, A.D. 2019. Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery. New Phytologist 222(4): 1742-1750. https://doi.org/10.1111/nph.15591
- Richardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P. and Ollinger, S.V. 2009. Near‐surface remote sensing of spatial and temporal variation in canopy phenology. Ecological Applications 19(6): 1417-1428. https://doi.org/10.1890/08-2022.1
- Richardson, A.D., Jenkins, J.P., Braswell, B.H., Hollinger, D.Y., Ollinger, S.V. and Smith, M.L. 2007. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152(2): 323-334. https://doi.org/10.1007/s00442-006-0657-z
- Richardson, A.D., Keenan, T.F., Migliavacca, M., Ryu, Y., Sonnentag, O. and Toomey, M. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology 169: 156-173. https://doi.org/10.1016/j.agrformet.2012.09.012
- Ryu, Y., Baldocchi, D.D., Verfaillie, J., Ma, S., Falk, M., Ruiz-Mercado, I., Hehn, T. and Sonnentag, O. 2010. Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function. Agricultural and Forest Meteorology 150(12): 1597-1606. https://doi.org/10.1016/j.agrformet.2010.08.009
- Scrantona, K. and Amarasekarea, P. 2017. Predicting phenological shifts in a changing climate. Proceedings of the National Academy of Sciences of the United States of America 114(50): 13212-13217. https://doi.org/10.1073/pnas.1711221114
- Snyder, K.A., Wehan, B.L., Filippa, G., Huntington, J.L., Stringham, T.K. and Snyder, D.K. 2016. Extracting plant phenology metrics in a great basin watershed: Methods and considerations for quantifying phenophases in a cold desert. Sensors 16(11): 1948. https://doi.org/10.3390/s16111948
- Sonnentag, O. Detto, M., Vargas, R., Ryu, Y., Runkle, B.R.K., Kelly, M. and Baldocchi, D.D. 2011. Tracking the structural and functional development of a perennial pepper-weed (Lepidium latifolium L.) infestation using a multiyear archive of webcam imagery and eddy covariance measurements. Agricultural and Forest Meteorology 151(7): 916-926. https://doi.org/10.1016/j.agrformet.2011.02.011
- Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A.M., Friedl, M., Braswell, B.H., Milliman, T., O'Keefe, J. and Richardson, A.D. 2012. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology 152: 159-177. https://doi.org/10.1016/j.agrformet.2011.09.009
- Tan B., Morisette J.T., Wolfe, R.E., Gao, F., Ederer, G.A., Nightingale, J. and Pedelty, J.P. 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
- Tooke F. and Battey, N.H. 2010. Temperate flowering phenology. Journal of Experimental Botany 61(11): 2853-2862. https://doi.org/10.1093/jxb/erq165
- Toomey, M., Friedl, M.A., Frolking, S., Hufkens, K., Klosterman, S., Sonnentag, O., Baldocchi, D.D., Bernacchi, C.J., Biraud, S.C. and Bohrer, G. 2015. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecological Applications 25(1): 99-115. https://doi.org/10.1890/14-0005.1
- Ulsig, L., Nichol, Caroline J., Huemmrich, K.F., Landis, D.R., Middleton, E.M., Lyapustin, A.I., Mammarella, I., Levula, J. and Porcar-Castell, A. 2017. Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series. Remote sensing 9(1): 49. https://doi.org/10.3390/rs9010049
- Wald, L. 2002. Data fusion: definitions and architectures: fusion of images of different spatial resolutions. Les Presses del'E cole des Mines. Paris. pp. 198.
- Whitfield, J. 2001. The budding amateurs. Nature 414: 578-579. https://doi.org/10.1038/414578a
- WMO (World Meteorological Organization). 2019. WMO statement on the state of the global climate in 2019. WMO-No. 1248: 1-44.
- Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. and Huete, A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84(3): 471-475. https://doi.org/10.1016/S0034-4257(02)00135-9