[en] Phenology is an important ecological indicator for understanding the feedback of plants to climate changes, but observation of plant phenology is not a trivial task, particularly for the large-scale areas of interest. Urban plant phenology monitoring is such a typical case, since massive residents do not necessarily mean enough eligible phenology observers. To handle this traditional challenge, this study attempted those surveillance cameras (SCs) widespread almost in any city all over the world. The schematic plan is to install an automatic software module, which has the function of plant phenology monitoring, as a plug-in into any central unit that wire-controls RGB SCs. The kernel of the module is a general-purposed algorithm capable of deriving the starting and ending dates of the key phenological events of different plants that stand in the field of view of each telecontrolled SC. The kernels of the algorithm comprise deriving phenological indices from the digital number (DN) records by a SC from all of its RGB channels and, then, modeling of plant dynamics based on the proposed novel phase-limited multi-Gaussian model for curve-fitting of phenological phases. In the case of determining the key phenological dates regarding flowering and foliation in this study, tests suggested that the proposed scheme and phenological indices and the programmed software plug-in all worked well. Overall, the feasibility of using the widespread SCs for urban plant phenology monitoring was validated, and the scheme can be further extended to composing phenology observation networks at local or global scales. The solution is of implications for more understanding the interannual rhythms of terrestrial ecosystems as well as the inherent mechanisms of vegetation-climate interactions.
Abu-Asab, M.S., Peterson, P.M., Shetler, S.G., Orli, S.S., Earlier plant flowering in spring as a response to global warming in the Washington, DC, area. Biodivers. Conserv. 10 (2001), 597–612.
Ahl, D.E., Gower, S.T., Burrows, S.N., Shabanov, N.V., Myneni, R.B., Knyazikhin, Y., Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 104 (2006), 88–95.
Ahrends, H.E., Brügger, R., Stöckli, R., Schenk, J., Michna, P., Jeanneret, F., Wanner, H., Eugster, W., Quantitative phenological observations of a mixed beech forest in northern Switzerland with digital photography. J. Geophys. Res.: Biogeosciences, 113, 2008.
Ahrends, H.E., Etzold, S., Kutsch, W.L., Stoeckli, R., Bruegger, R., Jeanneret, F., Wanner, H., Buchmann, N., Eugster, W., Tree phenology and carbon dioxide fluxes: Use of digital photography for process-based interpretation at the ecosystem scale. Clim. Res. 39 (2009), 261–274.
Alberton, B., Torres, R.S., Cancian, L.F., Borges, B.D., Almeida, J., Mariano, G.C., dos Santos, J., Morellato, L.P.C., Introducing digital cameras to monitor plant phenology in the tropics: Applications for conservation. Perspect. Ecol. Conserv. 15 (2017), 82–90.
Anderson, H.B., Nilsen, L., Tømmervik, H., Karlsen, S.R., Nagai, S., Cooper, E.J., Using ordinary digital cameras in place of near-infrared sensors to derive vegetation indices for phenology studies of high Arctic vegetation. Rem. Sens., 8, 2016, 847.
Aono, Y., Saito, S., Clarifying springtime temperature reconstructions of the medieval period by gap-filling the cherry blossom phenological data series at Kyoto, Japan. Int. J. Biometeorol. 54 (2010), 211–219.
Baldocchi, D., Black, T., Curtis, P., Falge, E., Fuentes, J., Granier, A., et al. Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: A synthesis of FLUXNET data. Int. J. Biometeorol. 49 (2005), 377–387.
Calders, K., Schenkels, T., Bartholomeus, H., Armston, J., Verbesselt, J., Herold, M., Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements. Agric. For. Meteorol. 203 (2015), 158–168.
Chuine, I., A unified model for budburst of trees. J. Theor. Biol. 207 (2000), 337–347.
Chuine, I., Cour, P., Rousseau, D., Fitting models predicting dates of flowering of temperate‐zone trees using simulated annealing. Plant Cell Environ. 21 (1998), 455–466.
Churkina, G., Schimel, D., Braswell, B.H., Xiao, X., Spatial analysis of growing season length control over net ecosystem exchange. Glob. Chang. Biol. 11 (2005), 1777–1787.
Comber, A., Brunsdon, C., A spatial analysis of plant phenophase changes and the impact of increases in urban land use. Int. J. Climatol. 35 (2015), 972–980.
Crimmins, M., Crimmins, T.M., Monitoring plant phenology using digital repeat photography. Environ. Manag. 41 (2008), 949–958.
Dai, J., Wang, H., Ge, Q., Multiple phenological responses to climate change among 42 plant species in Xi'an, China. Int. J. Biometeorol. 57 (2013), 749–758.
Eklundh, L., Jönsson, P., TIMESAT: A software package for time-series processing and assessment of vegetation dynamics. Rem. Sens. Digit. Image Process. 22 (2015), 141–158.
Ganguly, S., Friedl, M.A., Tan, B., Zhang, X., Verma, M., Land surface phenology from MODIS: Characterization of the collection 5 global land cover dynamics product. Remote Sens. Environ. 114 (2010), 1805–1816.
Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80 (2002), 76–87.
Gonsamo, A., Chen, J., D'Odorico, P., Deriving land surface phenology indicators from CO 2 eddy covariance measurements. Ecol. Indicat. 29 (2013), 203–207.
Gonsamo, A., Chen, J., Wu, C., Dragoni, D., Predicting deciduous forest carbon uptake phenology by upscaling FLUXNET measurements using remote sensing data. Agric. For. Meteorol. 165 (2012), 127–135.
Graham, E.A., Yuen, E.M., Robertson, G.F., Kaiser, W.J., Hamilton, M.P., Rundel, P.W., Budburst and leaf area expansion measured with a novel mobile camera system and simple color thresholding. Environ. Exp. Bot. 65 (2009), 238–244.
Henneken, R., Dose, V., Schleip, C., Menzel, A., Detecting plant seasonality from webcams using bayesian multiple change point analysis. Agric. For. Meteorol. 168 (2013), 177–185.
Hufkens, K., Friedl, M., Sonnentag, O., Braswell, B.H., Milliman, T., Richardson, A.D., Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 117 (2012), 307–321.
Ide, R., Oguma, H., Use of digital cameras for phenological observations. Ecol. Inf. 5 (2010), 339–347.
Jochner, S., Alves-Eigenheer, M., Menzel, A., Morellato, L.P.C., Using phenology to assess urban heat islands in tropical and temperate regions. Int. J. Climatol. 33 (2013), 3141–3151.
Jochner, S., Sparks, T., Estrella, N., Menzel, A., The influence of altitude and urbanization on trends and mean dates in phenology (1980-2009). Int. J. Biometeorol. 56 (2012), 387–394.
Koerner, C., Basler, D., Phenology under global warming. Science 327 (2010), 1461–1462.
Krehbiel, C.P., Jackson, T., Henebry, G., Web-enabled Landsat data time series for monitoring urban heat island impacts on land surface phenology. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9 (2016), 2043–2050.
Lafleur, P., Moore, T.R., Poon, D., Seaquist, J., Ecosystem phenology from eddy-covariance measurement: Spring photosynthesis in a cool temperate bog. AGU Fall Meet., 2005 2005.
Lin, Y., Jiang, M., Yao, Y., Zhang, L., Lin, J., Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban For. Urban Green. 14 (2015), 404–412.
Lin, Y., West, G., Reflecting conifer phenology using mobile terrestrial LiDAR: A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia. Ecol. Indicat. 70 (2016), 1–9.
Lou, L., Zhang, F.-M., Xu, C., Li, F., Xue, M.-G., Automatic registration of aerial image series using geometric invariance. Proc. IEEE Inter. Conf. Autom. Logist., 2008, 1198–1203 Qingdao, China.
Lu, P., Yu, Q., Liu, J., Lee, X., Advance of tree-flowering dates in response to urban climate change. Agric. For. Meteorol. 138 (2006), 120–131.
Nagai, S., Inoue, T., Ohtsuka, T., Yoshitake, S., Nasahara, K., Saitoh, T., Uncertainties involved in leaf fall phenology detected by digital camera. Ecol. Inf. 30 (2015), 124–132.
Neil, K., Landrum, L., Wu, J., Effects of urbanization on flowering phenology in the metropolitan phoenix region of USA: Findings from herbarium records. J. Arid Environ. 74 (2010), 440–444.
Neil, K., Wu, J., Effects of urbanization on plant flowering phenology: A review. Urban Ecosyst. 9 (2006), 243–257.
Nijland, W., de Jong, R., de Jong, S., Wulder, M., Bater, C., Coops, N., Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agric. For. Meteorol. 184 (2014), 98–106.
Palacios-Orueta, A., Huesca, M., Whiting, M., Litago, J., Khanna, S., Garcia, M., Ustin, S., Derivation of phenological metrics by function fitting to time-series of spectral shape indexes AS1 and AS2: Mapping cotton phenological stages using MODIS time series. Remote Sens. Environ. 126 (2012), 148–159.
Petach, A., Toomey, M., Aubrecht, D., Richardson, A., Monitoring vegetation phenology using an infrared-enabled security camera. Agric. For. Meteorol. 195 (2014), 143–151.
Richardson, A.D., Jenkins, J.P., Braswell, B.H., Hollinger, D.Y., Ollinger, S.V., Smith, M.-L., Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152 (2007), 323–334.
Rutishauser, T., Luterbacher, J., Jeanneret, F., Pfister, C., Wanner, H., A phenology‐based reconstruction of interannual changes in past spring seasons. J. Geophys. Res.: Biogeosciences, 112, 2007.
Sonnentag, O., Detto, M., Vargas, R., Ryu, Y., Runkle, B., Kelly, M., Baldocchi, D., Tracking the structural and functional development of a perennial pepperweed (lepidium latifolium l.)infestation using a multi-year archive of webcam imagery and eddy covariance measurements. Agric. For. Meteorol. 151 (2011), 916–926.
Studer, S., Stockli, R., Appenzeller, C., Vidale, P.L., A comparative study of satellite and ground-based phenology. Int. J. Biometeorol. 51 (2007), 405–414.
Toomey, M., Friedl, M.A., Frolking, S., Hufkens, K., Klosterman, S., Sonnentag, O., Baldocchi, D., et al. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecol. Appl. 25 (2015), 99–115.
Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D., Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114 (2010), 106–115.
Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A.K., Wang, T., Zurita-Milla, R., Oosterbeek, K., O'Connor, B., Paganini, M., Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sens. Environ. 215 (2018), 517–529.
Wu, X., Liu, H., Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982-2006. Glob. Chang. Biol. 19 (2013), 870–880.
Xie, Y., Civco, D.L., Silander, J.A., Species-specific spring and autumn leaf phenology capture by time-lapse digital cameras. Ecosphere, 9, 2018, e02089.
Yi, F., Moon, I., Automatic calculation of tree diameter from stereoscopic image pairs using digital image processing. Appl. Opt. 51 (2015), 4120–4128.
Zavalloni, C., Andresen, J., Flore, J., Phenological models of flower bud stages and fruit growth of ‘Montmorency’ sour cherry based on growing degree-day accumulation. J. Am. Soc. Hortic. Sci. 131 (2006), 601–607.
Zhang, A., Applicability analysis of phenological models in the flowering time prediction of ornamental plants in Beijing area. J. Appl. Meteorol. Sci. 25 (2014), 483–492.
Zhang, A., Zhang, J., Prediction of first flowering date of prunus discoidea in Beijing Yuyuantan Park using phenologica model. Meteorol. Sci. Technol., 43, 2015.
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Climate controls on vegetation phenological patterns in northern mid‐and high latitudes inferred from MODIS data. Glob. Chang. Biol. 10 (2004), 1133–1145.
