Detection of year-to-year spring and autumn bio-meteorological variations in siberian ecosystems

Allen, 2014, Modeling daily flowering probabilities: expected impact of climate change on Japanese cherry phenology, Global Change Biol., 20, 1251, 10.1111/gcb.12364 Anderson, 2016, Using ordinary digital cameras in place of near-infrared sensors to derive vegetation indices for phenology studies of high Arctic vegetation, Rem. Sens., 8, 847, 10.3390/rs8100847 Aono, 2008, Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century, Int. J. Climatol., 28, 905, 10.1002/joc.1594 Archetti, 2013, Predicting climate change impacts on the amount and duration of autumn colors in a New England forest, PLoS One, 8, 10.1371/journal.pone.0057373 Basler, 2016, Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe, Agric. For. Meteorol., 217, 10, 10.1016/j.agrformet.2015.11.007 Brown, 2016, Using phenocams to monitor our changing Earth: toward a global phenocam network, Front. Ecol. Environ., 14, 84, 10.1002/fee.1222 Buitenwerf, 2015, Three decades of multi-dimensional change in global leaf phenology, Nat. Clim. Change, 5, 364, 10.1038/nclimate2533 Chudinova, 2006, Relationship between air and soil temperature trends and periodicities in the permafrost regions of Russia, J. Geophys. Res., 111, F02008 Chuine, 2016, Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break, Global Change Biol., 22, 3444, 10.1111/gcb.13383 De Frenne, 2018, Using archived television video footage to quantify phenology responses to climate change, Methods Ecol. Evol., 9, 1874, 10.1111/2041-210X.13024 Delbart, 2005, Determination of phenological dates in boreal regions using normalized difference water index, Remote Sens. Environ., 97, 26, 10.1016/j.rse.2005.03.011 Delpierre, 2009, Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agric. For. Meteorol., 149, 938, 10.1016/j.agrformet.2008.11.014 Doi, 2010, Genetic diversity increases regional variation in phenological dates in response to climate change, Global Change Biol., 16, 373, 10.1111/j.1365-2486.2009.01993.x Gallinat, 2015, Autumn, the neglected season in climate change research, Trends Ecol. Evol., 30, 169, 10.1016/j.tree.2015.01.004 Garonna, 2014, Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011), Global Change Biol., 10.1111/gcb.12625 Ge, 2016, Spatiotemporal variability in start and end of growing season in China related to climate variability, Rem. Sens., 8, 433, 10.3390/rs8050433 Hadano, 2013, High-resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario, Ecol. Evol., 3, 1798, 10.1002/ece3.575 Ide, 2010, Use of digital cameras for phenological observations, Ecol. Inf., 5, 339, 10.1016/j.ecoinf.2010.07.002 Iijima, 2014, Sap flow changes in relation to permafrost degradation under increasing precipitation in an Eastern Siberian larch forest, Ecohydrology, 7, 177, 10.1002/eco.1366 Iwahana, 2014, Geocryological characteristics of the upper permafrost in a tundra-forest transition of the Indigirka River Valley, Russia, Pol. Sci., 8, 96 Keenan, 2015, The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models, Global Change Biol., 21, 2634, 10.1111/gcb.12890 Kotani, 2014, Temporal variations in the linkage between the net ecosystem exchange of water vapour and CO2 over boreal forests in eastern Siberia, Ecohydrology, 7, 209, 10.1002/eco.1449 Liang, 2014, Importance of soil moisture and N availability to larch growth and distribution in the Arctic taiga-tundra boundary ecosystem, northeastern Siberia, Pol. Sci., 8, 327 Lim, 2007, Leaf senescence, Annu. Rev. Plant Biol., 58, 115, 10.1146/annurev.arplant.57.032905.105316 Linderholm, 2006, Growing season changes in the last century, Agric. For. Meteorol., 137, 1, 10.1016/j.agrformet.2006.03.006 Matsumoto, 2009, Causal factors for spatial variation in long-term phenological trends in Ginkgo biloba L. in Japan, Int. J. Cimatol. Maximov, 2019, Carbon cycles in forests, 69 Menzel, 2006, European phenological response to climate change matches the warming pattern, Global Change Biol., 12, 1969, 10.1111/j.1365-2486.2006.01193.x Mizunuma, 2013, The relationship between carbon dioxide uptake and canopy colour from two camera systems in a deciduous forest in southern England, Funct. Ecol., 27, 196, 10.1111/1365-2435.12026 Monahan, 2016, Climate change is advancing spring onset across the U.S. national park system, Ecosphere, 7, 10.1002/ecs2.1465 Moore, 2016, Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography, Biogeosciences, 13, 5085, 10.5194/bg-13-5085-2016 Morozumi, 2020, Photographic records of plant phenology and spring river flush timing in a river lowland ecosystem at the taiga–tundra boundary, north-eastern Siberia, Ecol. Res., 10.1111/1440-1703.12107 Muraoka, 2009, Satellite Ecology (SATECO)—linking ecology, remote sensing and micrometeorology, from plot to regional scale, for the study of ecosystem structure and function, J. Plant Res., 122, 3, 10.1007/s10265-008-0188-2 Nagai, 2013, Detection of bio-meteorological year-to-year variation by using digital canopy surface images of a deciduous broad-leaved forest, SOLA, 9, 106, 10.2151/sola.2013-024 Nagai, 2015, Spatio-temporal distribution of the timing of start and end of growing season along vertical and horizontal gradients in Japan, Int. J. Biometeorol., 59, 47, 10.1007/s00484-014-0822-8 Nagai, 2018, 8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network, Ecol. Res., 33, 1091, 10.1007/s11284-018-1633-x Nagai, 2019, Remote sensing of vegetation, 231 Nagai, 2020, Importance of the collection of abundant ground-truth data for accurate detection of spatial and temporal variability of vegetation by satellite remote sensing, pp336 Nasahara, 2015, Review: development of an in-situ observation network for terrestrial ecological remote sensing—the Phenological Eyes Network (PEN), Ecol. Res., 30, 211, 10.1007/s11284-014-1239-x Ogawa-Onishi, 2013, Ecological impacts of climate change in Japan: the importance of integrating local and international publications, Biol. Conserv., 157, 361, 10.1016/j.biocon.2012.06.024 Ohta, 2008, Interannual variation of water balance and summer evapotranspiration in an eastern Siberian larch forest over a 7-year period (1998–2006), Agric. For. Meteorol., 148, 1941, 10.1016/j.agrformet.2008.04.012 Ohta, 2014, Effects of waterlogging on water and carbon dioxide fluxes and environmental variables in a Siberian larch forest, 1998–2011, Agric. For. Meteorol., 188, 64, 10.1016/j.agrformet.2013.12.012 Park, 2017, Spatial and temporal changes in leaf coloring date of Acer palmatum and Ginkgo biloba in response to temperature increases in South Korea, PLoS One, 12, 10.1371/journal.pone.0174390 Peñuelas, 2009, Phenology feedbacks on climate change, Science, 324, 887, 10.1126/science.1173004 Polgar, 2011, Leaf-out phenology of temperate woody plants: from trees to ecosystems, New Phytol., 191, 926, 10.1111/j.1469-8137.2011.03803.x R Project for Statistical Computing Richardson, 2010, Influence of spring and autumn phenological transitions on forest ecosystem productivity, Phil. Trans. R. Soc. B, 365, 3227, 10.1098/rstb.2010.0102 Richardson, 2013, Climate change, phenology, and phenological control of vegetation feedbacks to the climate system, Agric. For. Meteorol., 169, 156, 10.1016/j.agrformet.2012.09.012 Richardson, 2017 Richardson, 2018, Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery, Sci. Data, 5, 180028, 10.1038/sdata.2018.28 Richardson, 2019, Testing hopkins' bioclimatic law with PhenoCam data, Appl. Plant Sci., 7, e1228, 10.1002/aps3.1228 Rosenthal, 1996, Effects of air temperature, photoperiod and leaf age on foliar senescence of western larch (Larix occidentalis Nutt.) in environmentally controlled chambers, Plant Cell Environ., 19, 1057, 10.1111/j.1365-3040.1996.tb00212.x Rosenthal, 1997, Photosynthetic decline and pigment loss during autumn foliar senescence in western larch (Larix occidentalis), Tree Physiol., 17, 767, 10.1093/treephys/17.12.767 Shulgina, 2011, Dynamics of climatic characteristics influencing vegetation in Siberia, Environ. Res. Lett., 6, 10.1088/1748-9326/6/4/045210 Sugiura, 2013, Application of time-lapse digital imagery for ground-truth verification of satellite indices in the boreal forests of Alaska, Pol. Sci., 7, 149 Tadaki, 1994, Leaf opening and falling of Japanese larch at different altitudes, Jpn. J. Ecol., 44, 305 Tang, 2016, Emerging opportunities and challenges in phenology: a review, Ecosphere, 7, 10.1002/ecs2.1436 Templ, 2018, Pan European Phenological database (PEP725): a single point of access for European data, Int. J. Biometeorol., 10.1007/s00484-018-1512-8 Wang, 2015, Parameterization of temperature sensitivity of spring phenology and its application in explaining diverse phenological responses to temperature change, Sci. Rep., 5, 8833, 10.1038/srep08833 Way, 2015, Photoperiod constraints on tree phenology, performance and migration in a warming world, Plant Cell Environ., 38, 1725, 10.1111/pce.12431 Wingate, 2015, Interpreting canopy development and physiology using a European phenology camera network at flux sites, Biogeosciences, 12, 5995, 10.5194/bg-12-5995-2015 Xie, 2018, Predicting autumn phenology: how deciduous tree species respond to weather stressors, Agric. For. Meteorol., 250–251, 127, 10.1016/j.agrformet.2017.12.259 Yu, 2016, An observation-based progression modeling approach to spring and autumn deciduous tree phenology, Int. J. Biometeorol., 60, 335, 10.1007/s00484-015-1031-9