Monitoring spring phenology in Mediterranean beech populations through in situ observation and Synthetic Aperture Radar methods

Allevato, 2019, Canopy damage by spring frost in European beech along the Apennines: effect of latitude, altitude and aspect, Rem. Sens. Environ., 225, 431, 10.1016/j.rse.2019.03.023 Askne, 2009, Automatic model-based estimation of boreal forest stem volume from repeat pass C-band InSAR coherence, IEEE Trans. Geosci. Remote Sens., 47, 513, 10.1109/TGRS.2008.2009764 Askne, 2013, Model-based biomass estimation of a hemi-boreal forest from multitemporal TanDEM-X acquisitions, Remote Sens., 5, 5574, 10.3390/rs5115574 Bajocco, 2012, A satellite-based green index as a proxy for vegetation cover quality in a Mediterranean region, Ecol. Indic., 23, 578, 10.1016/j.ecolind.2012.05.013 Basler, 2012, Photoperiod sensitivity of bud burst in 14 temperate forest tree species, Agric. For. Meteorol., 165, 73, 10.1016/j.agrformet.2012.06.001 Bonamour, 2019, Phenotypic plasticity in response to climate change: the importance of cue variation, Phil. Trans. R. Soc. B, 374, 10.1098/rstb.2018.0178 Bucha, 2017, Phenology of the beech forests in the Western Carpathians from MODIS for 2000-2015, iForest, 10, 537, 10.3832/ifor2062-010 Caffarra, 2010, The ecological significance of phenology in four different tree species: effects of light and temperature on bud burst, Int. J. Biometeorol., 55, 711, 10.1007/s00484-010-0386-1 Chuine, 2010, Why does phenology drive species distribution?, Phil. Trans. R. Soc. B, 365, 3149, 10.1098/rstb.2010.0142 Chuine, 2013, Plant development models, 275 Cleveland, 1992, Local regression models, 309 Colombo, 2010, 313 Cong, 2013, Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis, Glob. Chang. Biol., 19, 881, 10.1111/gcb.12077 Čufar, 2008, Tree-ring variation, wood formation and phenology of beech (Fagus sylvatica) from a representative site in Slovenia, SE Central Europe, Trees, 22, 749, 10.1007/s00468-008-0235-6 Čufar, 2012, Temporal shifts in leaf phenology of beech (Fagus sylvatica) depend on elevation, Trees Struct. Funct., 26, 1091, 10.1007/s00468-012-0686-7 Dallimer, 2016, The extent of shifts in vegetation phenology between rural and urban areas within a human-dominated region, Ecol. Evol., 6, 1942, 10.1002/ece3.1990 De Bernardis, 2016, Contribution to real-time estimation of crop phenological states in a dynamical framework based on NDVI time series: data fusion with SAR and temperature, IEEE J. Select. Topics Appl. Earth Observ. Remote Sens., 9, 3512, 10.1109/JSTARS.2016.2539498 Dittmar, 2006, Phenological phases of common beech (Fagus sylvatica L.) and their dependence on region and altitude in Southern Germany, Eur. J. Forest. Res., 125, 181, 10.1007/s10342-005-0099-x Frate, 2015, Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques, IForest, 9, 63, 10.3832/ifor1529-008 French, 1996, Monitoring variations in soil moisture on fire disturbed sites in Alaska using ERS-1 SAR imagery, Int. J. Remote Sens., 17, 3037, 10.1080/01431169608949126 Friedl, 2014, A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change, Environ. Res. Lett., 9, 1, 10.1088/1748-9326/9/5/054006 Fu, 2014, Recent spring phenology shifts in western Central Europe based on multiscale observations, Glob. Ecol. Biogeogr., 23, 1255, 10.1111/geb.12210 Garate Escamilla, 2020, Greater capacity to exploit warming temperatures in northern populations of European beech is partly driven by delayed leaf senescence, Agric. For. Meteorol., 107908, 1 Garfì, 2011, 280 Giagli, 2015, Monitoring of seasonal dynamics in two age-different european beech stands, Wood Res., 60, 1005 Gömöry, 2011, Trade-off between height growth and spring flushing in common beech (Fagus sylvatica L.), Ann. For. Sci., 68, 975, 10.1007/s13595-011-0103-1 Huete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195, 10.1016/S0034-4257(02)00096-2 Hufkens, 2019, Monitoring crop phenology using a smartphone based near-surface remote sensing approach, Agric. For. Meteorol., 265, 327, 10.1016/j.agrformet.2018.11.002 Koch, 2007, 13 Kolář, 2016, Response of the leaf phenology and tree-ring width of European beech to climate variability, Silva Fennica, 50, 18, 10.14214/sf.1520 Kramer, 2017, Chilling and forcing requirements for foliage bud burst of European beech (Fagus sylvatica L.) differ between provenances and are phenotypically plastic, Agric. For. Meteorol., 234-235, 172, 10.1016/j.agrformet.2016.12.002 Kraus, 2016, Elevational response in leaf and xylem phenology reveals different prolongation of growing period of common beech and Norway spruce under warming conditions in the Bavarian Alps, Eur. J. Forest Res., 135, 1011, 10.1007/s10342-016-0990-7 Laube, 2014, Chilling outweighs photoperiod in preventing precocious spring development, Glob. Chang. Biol., 20, 170, 10.1111/gcb.12360 Leblon, 2016, Remote Sensing of Wildfires, Land Surf. Remote Sens., 55, 10.1016/B978-1-78548-105-5.50003-7 Lee, 1981, Speckle analysis and smoothing of synthetic aperture radar images, Comp. Graphics Image Process., 17, 24, 10.1016/S0146-664X(81)80005-6 Lee, 1983, A simple speckle smoothing algorithm for synthetic aperture radar images, IEEE Trans. Syst. Man Cybernet., 85, 10.1109/TSMC.1983.6313036 Lee, 1991, Speckle reduction in multipolarization, multifrequency SAR imagery, IEEE Trans. Geosci. Remote Sens., 29, 535, 10.1109/36.135815 Liu, 2001, Satellite Remote Sensing SAR, Encycl. Ocean Sci., 103, 10.1016/B978-012374473-9.00339-8 López-Martínez, 2017, Polarimetric SAR techniques and applications Lopez-Sanchez, 2014, Polarimetric response of rice fields at C-band: analysis and phenology retrieval, IEEE Trans. Geosci. Remote Sens., 52, 2977, 10.1109/TGRS.2013.2268319 McNairn, 2018, Estimating canola phenology using synthetic aperture radar, Remote Sens. Environ., 219, 196, 10.1016/j.rse.2018.10.012 Meier, 2001, Growth stages of mono- and dicotyledonous plants Menzel, 2006, European phenological response to climate change matches the warming pattern, Glob. Chang. Biol., 12, 1969, 10.1111/j.1365-2486.2006.01193.x Menzel, 2015, Patterns of late spring frost leaf damage and recovery in a European beech (Fagus sylvatica L.) stand in south-eastern Germany based on repeated digital photographs, Front. Plant Sci., 6, 110, 10.3389/fpls.2015.00110 Nagai, 2016, Review: advances in in situ and satellite phenological observations in Japan, Int. J. Biometeorol., 60, 615, 10.1007/s00484-015-1053-3 Nasrallah, 2019, Sentinel-1 data for winter wheat phenology monitoring and mapping, Remote Sens., 11, 2228, 10.3390/rs11192228 Ogle Peel, 2007, Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633, 10.5194/hess-11-1633-2007 Ploberger, 1992, The CUSUM test with OLS residuals, Econometrica, 60, 271, 10.2307/2951597 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 Primack, 2015, From observations to experiments in phenology research: investigating climate change impacts on trees and shrubs using dormant twigs, Ann. Bot., 116, 889, 10.1093/aob/mcv032 Prislan, 2019, Growing season and radial growth predicted for Fagus sylvatica under climate change, Clim. Change, 181, 10.1007/s10584-019-02374-0 Proisy, 2000, Monitoring seasonal changes of a mixed temperate forest using ERS SAR observations, IEEE Trans. Geosci. Remote Sens., 38, 540, 10.1109/36.823949 Puletti, 2018, Use of Sentinel-2 for forest classification in Mediterranean environments, Ann. Silvic. Res., 42, 32 R Core Team, 2018 Rüetschi, 2018, Using multitemporal Sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in Northern Switzerland, Remote Sens., 10, 55, 10.3390/rs10010055 Santopuoli, 2012, Application of indicators network analysis to support local forest management plan development: a case study in Molise, Italy, IForest, 5, 31, 10.3832/ifor0603-009 Santoro, 2018, Forest stem volume estimation using C-band interferometric SAR coherence data of the ERS-1 mission 3-days repeat-interval phase, Remote Sens. Environ., 216, 684, 10.1016/j.rse.2018.07.032 Schuster, 2014, Shifting and extension of phenological periods with increasing temperature along elevational transects in southern Bavaria, Plant Biol., 16, 332, 10.1111/plb.12071 Small, 2011, Flattening gamma: radiometric terrain correction for SAR imagery, IEEE Trans. Geosci. Remote Sens., 49, 3081, 10.1109/TGRS.2011.2120616 Song, 2019, Mapping winter wheat planting area and monitoring its phenology using Sentinel-1 backscatter time series, Remote Sens., 11, 449, 10.3390/rs11040449 Stendardi, 2019, Exploiting time series of Sentinel-1 and Sentinel-2 imagery to detect meadow phenology in mountain regions, Remote Sens., 11, 542, 10.3390/rs11050542 Suepa, 2016, Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast AAia, Environ. Res., 147, 621, 10.1016/j.envres.2016.02.005 Tamm, 2016, Relating Sentinel-1 interferometric coherence to mowing events on grasslands, Remote Sens., 8, 802, 10.3390/rs8100802 Tanase, 2011, Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests, Remote Sens. Environ., 115, 2075, 10.1016/j.rse.2011.04.009 Vincent, 2015, RADAR - synthetic aperture radar (land surface applications), 470 Vitasse, 2013, What role for photoperiod in the bud burst phenology of European beech, Eur. J. For. Res., 132, 1, 10.1007/s10342-012-0661-2 Vitasse, 2010, Quantifying phenological plasticity to temperature in two temperate tree species, Funct. Ecol., 24, 1211, 10.1111/j.1365-2435.2010.01748.x Vitasse, 2014, The interaction between freezing tolerance and phenology in temperate deciduous trees, Front. Plant Sci., 5, 541, 10.3389/fpls.2014.00541 Vizzarri, 2014, Mapping forest ecosystem functions for landscape planning in a mountain Natura2000 site, Central Italy, J. Environ. Plan. Manag., 0568, 1 Vreugdenhil, 2018, Sensitivity of Sentinel-1 backscatter to vegetation dynamics: an Austrian case study, Remote Sens., 10, 1396, 10.3390/rs10091396 Walter, 1960 Wang, 2016, Temporal trends and spatial variability of vegetation phenology over the northern hemisphere during 1982–2012, PLoS One, 11 Way, 2015, Photoperiod constraints on tree phenology, performance and migration in a warming world, Plant Cell Environ., 38, 1725, 10.1111/pce.12431 Wu, 2014, Modelling growing season phenology of North American forests using seasonal mean vegetation indices from MODIS, Remote Sens. Environ., 147, 79, 10.1016/j.rse.2014.03.001 Xie, 2018, Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval, 11 (5), 1482 You, 2013, Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method, Remote Sens., 5, 3190, 10.3390/rs5073190 Zeileis, 2002, strucchange: an R package for testing for structural change in linear regression models, J. Stat. Softw., 7, 1, 10.18637/jss.v007.i02 Zeileis, 2003, Testing and dating of structural changes in practice, Comput. Stat. Data Anal., 44, 109, 10.1016/S0167-9473(03)00030-6 Zohner, 2016, Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants, Nat. Clim. Chang., 6, 1120, 10.1038/nclimate3138