Vegetation phenology from satellite imagery: the case of the Iberian Peninsula and Balearic Islands (2001-2017)
DOI:
https://doi.org/10.4995/raet.2020.13632Keywords:
spring, autumn, seasonality, MODIS, time seriesAbstract
Phenological dynamics of vegetation is considered as an important biological indicator for understanding the functioning of terrestrial ecosystems. Land surface phenology (LSP), the study of vegetation phenology from time series of vegetation indices (IV), has provided a comprehensive overview of ecosystem dynamics. Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent study area for monitoring phenological dynamics of vegetation. The aim of this study is to analyse the spatial variability of the phenology of the vegetation of the Iberian Peninsula and Balearic Islands for the period 2001-2017. NDVI (Normalized Difference Vegetation Index) time series were generated from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters and with a composite period of 8 days. Atmospheric disturbances and noise were reduced using a Savitzky-Golay smoothing filter. Different phenological metrics or phenometrics were extracted using a threshold-based method. Results showed the existence of a different behaviour between spring and autumn phenophases in the Atlantic and Mediterranean biogeographic regions. The Mediterranean mountainous areas showed a similar phenological behaviour to the Atlantic vegetation. Biogeographic regions showed an internal variability, which may be derived from the different behaviour of land covers (e.g., natural vegetation vs. crops).Downloads
References
Adole, T., Dash, J., Atkinson, P.M., 2016. A systematic review of vegetation phenology in Africa. Ecological Informatics, 34, 117-128. https://doi.org/10.1016/j.ecoinf.2016.05.004
Adole, T., Dash, J., Rodriguez-Galiano, V., Atkinson, P.M., 2019. Photoperiod controls vegetation phenology across Africa. Communications Biology, 2(1), 391. https://doi.org/10.1038/s42003-019-0636-7
Ahas, R., Aasa, R., Menzel, A., Fedotova, V.G., Scheifinger, H., 2002. Changes in European spring phenology. International Journal of Climatology, 22(14), 1727-1738. https://doi.org/10.1002/joc.818
Aragones, D., Rodriguez-Galiano, V.F., Caparros-Santiago, J.A., Navarro-Cerrillo, R.M., 2019. Could land surface phenology be used to discriminate Mediterranean pine species? International Journal of Applied Earth Observation and Geoinformation, 78, 281-294. https://doi.org/10.1016/j.jag.2018.11.003
Asam, S., Callegari, M., Matiu, M., Fiore, G., De Gregorio, L., Jacob, A., Menzel, A., Zebisch, M., Notarnicola, C., 2018. Relationship between spatiotemporal variations of climate, snow cover and plant phenology over the Alps-An Earth observation-based analysis. Remote Sensing, 10(11). https://doi.org/10.3390/rs10111757
Atkinson, P.M., Jeganathan, C., Dash, J., Atzberger, C., 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123, 400-417. https://doi.org/10.1016/j.rse.2012.04.001
Atzberger, C., Klisch, A., Mattiuzzi, M., Vuolo, F., 2013. Phenological metrics derived over the European continent from NDVI3g data and MODIS time series. Remote Sensing, 6(1), 257-284. https://doi.org/10.3390/rs6010257
Catry, F.X., Moreira, F., Deus, E., Silva, J.S., Águas, A., 2015. Assessing the extent and the environmental drivers of Eucalyptus globulus wildling establishment in Portugal: results from a countrywide survey. Biological Invasions, 17(11), 3163-3181. https://doi.org/10.1007/s10530-015-0943-y
Chen, X., Wang, D., Chen, J., Wang, C., Shen, M., 2018. The mixed pixel effect in land surface phenology: A simulation study. Remote Sensing of Environment, 211, 338-344. https://doi.org/10.1016/j.rse.2018.04.030
Chen, X., Yang, Y., 2020. Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001-2014. Environmental Research Letters, 15(3). https://doi.org/10.1088/1748-9326/ab6d39
de Beurs, K.M., Henebry, G.M., 2005. Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects. Global Change Biology, 11(5), 779-790. https://doi.org/10.1111/j.1365-2486.2005.00949.x
EEA, 2017. Climate Change, Impacts and Vulnerability in Europe 2016: An indicator-based report. Copenhagen: Environmental Science and Engineering - European Environment Agency (EEA).
Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., 2007. The shuttle radar topography mission. Reviews of Geophysics, 45(2), RG2004. https://doi.org/10.1029/2005RG000183
Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X., 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168-182. https://doi.org/10.1016/j.rse.2009.08.016
Garonna, I., de Jong, R., Schaepman, M.E., 2016. Variability and evolution of global land surface phenology over the past three decades (1982-2012). Global Change Biology, 22(4), 1456-1468. https://doi.org/10.1111/gcb.13168
Gómez-Limón, J.A., Picazo-Tadeo, A.J., 2012. Irrigated agriculture in Spain: Diagnosis and Prescriptions for Improved governance. International Journal of Water Resources Development, 28(1), 57-72. https://doi.org/10.1080/07900627.2012.640876
Gonsamo, A., Chen, J.M., David, T.P., Kurz, W.A., Wu, C., 2012. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. Journal of Geophysical Research: Biogeosciences, 117(3). https://doi.org/10.1029/2012JG002070
Helman, D., 2018. Land surface phenology: What do we really 'see' from space? Science of the Total Environment, 618, 665-673. https://doi.org/10.1016/j.scitotenv.2017.07.237
Hird, J.N., McDermid, G.J., 2009. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113(1), 248-258. https://doi.org/10.1016/j.rse.2008.09.003
Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11), 1417-1434. https://doi.org/10.1080/01431168608948945
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
Ivits, E., Cherlet, M., Horion, S., Fensholt, R., 2013. Global biogeographical pattern of ecosystem functional types derived from earth observation data. Remote Sensing, 5(7), 3305-3330. https://doi.org/10.3390/rs5073305
Jönsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1824-1832. https://doi.org/10.1109/TGRS.2002.802519
Jönsson, P., Eklundh, L., 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
Julien, Y., Sobrino, J.A., 2009. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing, 30(13), 3495-3513. https://doi.org/10.1080/01431160802562255
Keenan, T.F., Gray, J., Friedl, M.A., Toomey, M., Bohrer, G., Hollinger, D.Y., Munger, J.W., O'Keefe, J., Schmid, H.P., Wing, I.S., Yang, B., Richardson, A.D., 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change, 4(7), 598-604. https://doi.org/10.1038/nclimate2253
Klosterman, S.T., Hufkens, K., Gray, J.M., Melaas, E., Sonnentag, O., Lavine, I., Mitchell, L., Norman, R., Friedl, M.A., Richardson, A.D., 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences, 11(16), 4305-4320. https://doi.org/10.5194/bg-11-4305-2014
Menzel, A., 2000. Trends in phenological phases in Europe between 1951 and 1996. International Journal of Biometeorology, 44(2), 76-81. https://doi.org/10.1007/s004840000054
Menzel, A., 2002. Phenology: Its importance to the global change community: An editorial comment. Climatic Change, 54(4), 379-385. https://doi.org/10.1023/A:1016125215496
Pastor-Guzman, J., Dash, J., Atkinson, P.M., 2018. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205, 71-84. https://doi.org/10.1016/j.rse.2017.11.009
Peñuelas, J., Filella, I., 2001. Phenology: Responses to a warming world. Science, 294(5543), 793-795. https://doi.org/10.1126/science.1066860
Peñuelas, J., Filella, I., Zhang, X., Llorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., Terradas, J., 2004. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytologist, 161(3), 837-846. https://doi.org/10.1111/j.1469-8137.2004.01003.x
Piao, S., Liu, Q., Chen, A., Janssens, I.A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., Zhu, X., 2019. Plant phenology and global climate change: Current progresses and challenges. Global Change Biology, 25(6), 1922-1940. https://doi.org/10.1111/gcb.14619
Qader, S.H., Atkinson, P.M., Dash, J., 2015. Spatiotemporal variation in the terrestrial vegetation phenology of Iraq and its relation with elevation. International Journal of Applied Earth Observation and Geoinformation, 41, 107-117. https://doi.org/10.1016/j.jag.2015.04.021
Ramos, A., Pereira, M.J., Soares, A., Rosário, L.D., Matos, P., Nunes, A., Branquinho, C., Pinho, P., 2015. Seasonal patterns of Mediterranean evergreen woodlands (Montado) are explained by long-term precipitation. Agricultural and Forest Meteorology, 202, 44-50. https://doi.org/10.1016/j.agrformet.2014.11.021
Richardson, A.D., Keenan, T.F., Migliavacca, M., Ryu, Y., Sonnentag, O., 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
Rivas-Martínez, S., 1987. Memoria del mapa de series de vegetación de España. Madrid: Ministerio de Agricultura, Pesca y Alimentación -Instituto para la Conservación de la Naturaleza (ICONA).
Rodrigues, A., Marcal, A.R.S., Cunha, M., 2013. Monitoring vegetation dynamics inferred by satellite data using the pheno sat tool. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 2096-2104. https://doi.org/10.1109/TGRS.2012.2223475
Rodriguez-Galiano, V.F., Dash, J., Atkinson, P.M., 2015a. Characterising the land surface phenology of Europe using decadal MERIS data. Remote Sensing, 7(7), 9390-9409. https://doi.org/10.3390/rs70709390
Rodriguez-Galiano, V.F., Dash, J., Atkinson, P.M., 2015b. Intercomparison of satellite sensor land surface phenology and ground phenology in Europe. Geophysical Research Letters, 42(7), 2253-2260. https://doi.org/10.1002/2015GL063586
Rodriguez-Galiano, V.F., Sanchez-Castillo, M., Dash, J., Atkinson, P.M., Ojeda-Zujar, J., 2016. Modelling interannual variation in the spring and autumn land surface phenology of the European forest. Biogeosciences, 13(11), 3305-3317. https://doi.org/10.5194/bg-13-3305-2016
Savitzky, A., Golay, M.J.E., 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627-1639. https://doi.org/10.1021/ac60214a047
Schaber, J., Badeck, F.W., 2005. Plant phenology in Germany over the 20th century. Regional Environmental Change, 5(1), 37-46. https://doi.org/10.1007/s10113-004-0094-7
Sobrino, J.A., Julien, Y., Soria, G., 2013. Phenology estimation from meteosat second generation data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1653-1659. https://doi.org/10.1109/JSTARS.2013.2259577
Tong, X., Tian, F., Brandt, M., Liu, Y., Zhang, W., Fensholt, R., 2019. Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012. Remote Sensing of Environment, 232. https://doi.org/10.1016/j.rse.2019.111307
Valderrama-Landeros, L.H., España-Boquera, M.L., Baret, F., 2016. Deforestation in Michoacan, Mexico, from CYCLOPES-LAI Time Series (2000-2006). Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(12), 5398-5405. https://doi.org/10.1109/JSTARS.2016.2597742
Verger, A., Filella, I., Baret, F., Peñuelas, J., 2016. Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION. Revista de Teledeteccion, 2016(47), 1-11. https://doi.org/10.4995/raet.2016.5718
Vermote, E., 2015. MOD09A1 MODIS/terra surface reflectance 8-day L3 global 500m SIN grid V006 [Dataset]. NASA EOSDIS Land Processes DAAC. Accessed 2018-02-25 from https://doi.org/10.5067/MODIS/MOD09Q1.006.
Vrieling, A., De Leeuw, J., Said, M.Y., 2013. Length of growing period over africa: Variability and trends from 30 years of NDVI time series. Remote Sensing, 5(2), 982-1000. https://doi.org/10.3390/rs5020982
Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A.K., Wang, T., Zurita-Milla, R., Oosterbeek, K., O'Connor, B., Paganini, M., 2018. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sensing of Environment, 215, 517-529. https://doi.org/10.1016/j.rse.2018.03.014
White, M.A., de Beurs, K.M., Didan, K., Inouye, D.W., Richardson, A.D., Jensen, O.P., O'Keefe, J., Zhang, G., Nemani, R.R., van Leeuwen, W.J.D., Brown, J.F., de Wit, A., Schaepman, M., Lin, X., Dettinger, M., Bailey, A.S., Kimball, J., Schwartz, M.D., Baldocchi, D.D., Lee, J.T., Lauenroth, W.K., 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
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., 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
Downloads
Published
Issue
Section
License
This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
