Productivity and phenological responses of natural vegetation to present and future inter-annual climate variability across semi-arid river basins in Chile

  • Francisco E. Glade
  • Marcelo D. MirandaEmail author
  • Francisco J. Meza
  • Willem J. D. van Leeuwen


Time series of vegetation indices and remotely sensed phenological data offer insights about the patterns in vegetation dynamics. Both are useful sources of information for analyzing and monitoring ecosystem responses to environmental variations caused by natural and anthropogenic drivers. In the semi-arid region of Chile, climate variability and recent severe droughts in addition to land-use changes pose threats to the stability of local ecosystems. Normalized difference vegetation index time series (2000–2013) data from the moderate resolution imaging spectroradiometer (MODIS) was processed to monitor the trends and patterns of vegetation productivity and phenology observed over the last decade. An analysis of the relationship between (i) vegetation productivity and (ii) precipitation and temperature data for representative natural land-use cover classes was made. Using these data and ground measurements, productivity estimates were projected for two climate change scenarios (RCP2.6 and RCP8.5) at two altitudinal levels. Results showed negative trends of vegetation productivity below 2000 m a.s.l. and positive trends for higher elevations. Phenology analysis suggested that mountainous ecosystems were starting their growing period earlier in the season, coinciding with a decreased productivity peak during the growing season. The coastal shrubland/grassland land cover class had a significant positive relation with rainfall and a significant negative relation with temperature, suggesting that these ecosystems are vulnerable to climate change. Future productivity projections indicate that under an RCP8.5 climate change scenario, productivity could decline by 12% in the period of 2060–2100, leading to a severe vegetation degradation at lower altitudes and in drier areas.


Vegetation productivity Phenology trends Monitoring land degradation Climate change Semi-arid region 



This research was funded by a FONDEF grant (number D10I1051). Support was also provided by a grant (CRN3056) from the Inter American Institute for Global Change Research.

The MOD13Q1 and MOD11A2 data products were provided courtesy of the online Data Pool at the NASA Land Processes Distributed Active Archive Center, USGS/Earth Resources Observation, and Science Center, Sioux Falls, South Dakota (


  1. Anyamba, A., & Tucker, C. J. (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments, 63, 596–614.CrossRefGoogle Scholar
  2. Baldi, G., Nosetto, M. D., Aragón, R., Aversa, F., Paruelo, J. M., & Jobbágy, E. G. (2008). Long-term satellite NDVI data sets: evaluating their ability to detect ecosystem functional changes in South America. Sensors, 8, 5397–5425.CrossRefGoogle Scholar
  3. Blanco, L. J., Aguilera, M. O., Paruelo, J. M., & Biurrun, F. N. (2008). Grazing effect on NDVI across an aridity gradient in Argentina. Journal of Arid Environments, 72(5), 764–776.CrossRefGoogle Scholar
  4. Bradley, N. L., Leopold, A. C., Ross, J., & Huffaker, W. (1999). Phenological changes reflect climate change in Wisconsin. Proceedings of the National Academy of Sciences of the United States of America, 96, 9701–9704.CrossRefGoogle Scholar
  5. Barron-Gafford, G. A., Scott, R. L., Jenerette, G. D., Hamerlynck, E. P., & Huxman, T. E. (2012). Temperature and precipitation controls over leaf-and ecosystem-level CO2 flux along a woody plant encroachment gradient. Global Change Biology, 18(4), 1389–1400.CrossRefGoogle Scholar
  6. Castro, L. M., Miranda, M., & Fernández, B. (2015). Evaluation of TRMM multi-satellite precipitation analysis (TMPA) in a mountainous region of the central Andes range with a Mediterranean climate. Hydrology Research, 46(1), 89–105.CrossRefGoogle Scholar
  7. Cetin, M. (2015). Determining the bioclimatic comfort in Kastamonu City. Environmental Monitoring and Assessment, 187(10). doi: 10.1007/s10661-015-4861-3.
  8. Chen, C., Eamus, D., Cleverly, J., Boulain, N., Cook, P., Zhang, L., Cheng, L., & Yu, Q. (2014). Modelling vegetation water-use and groundwater recharge as affected by climate variability in an arid-zone Acacia savanna woodland. Journal of Hydrology, 519, 1084–1096.CrossRefGoogle Scholar
  9. Christensen, J. H., & Christensen, O. B. (2007). A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Climatic Change, 81(1), 7–30.CrossRefGoogle Scholar
  10. Corporación Nacional Forestal (CONAF), 2004. Catastro y Evaluación de Usos del Suelo y Vegetación, Cuarta Región. Coquimbo. Chile. 32 pp.Google Scholar
  11. Dai, A. (2013). Increasing drought under global warming in observations and models. Nature Climate Change, 3, 52–58.CrossRefGoogle Scholar
  12. Eckert, S., Hüsler, F., Liniger, H., & Hodel, E. (2015). Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113, 16–28.CrossRefGoogle Scholar
  13. Fensholt, R., Langanke, T., Rasmussen, K., Reenberg, A., Prince, S. D., Tucker, C., et al. (2012). Greenness in semi-arid areas across the globe 1981–2007—an earth observing satellite based analysis of trends and drivers. Remote Sensing of Environment, 121, 144–158.CrossRefGoogle Scholar
  14. Fensholt, R., & Rasmussen, K. (2011). Analysis of trends in the Sahelian ‘rain-use efficiency’using GIMMS NDVI, RFE and GPCP rainfall data. Remote Sensing of Environment, 115(2), 438–451.CrossRefGoogle Scholar
  15. Gessner, U., Naeimi, V., Klein, I., Kuenzer, C., Klein, D., & Dech, S. (2013). The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Global and Planetary Change, 110, 74–87.CrossRefGoogle Scholar
  16. Gutiérrez, J. R., & Squeo, F. A. (2004). Importancia de los arbustos en los ecosistemas semiáridos de Chile. Ecosistemas: Revista Cietifica y Tecnica de Ecologia y Medio Ambiente, 13, 36–45.Google Scholar
  17. Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., & Ferreira, L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.CrossRefGoogle Scholar
  18. Jeganathan, C., Dash, J., & Atkinson, P. M. (2014). Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation, controlling for land cover change and vegetation type. Remote Sensing of Environment, 143, 154–170.CrossRefGoogle Scholar
  19. Jönsson, P., & Eklundh, L. (2004). TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences, 30, 833–845.CrossRefGoogle Scholar
  20. Kariyeva, J., van Leeuwen, W. J. D., & Woodhouse, C. A. (2012). Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981–2008). Frontiers of Earth Science, 6, 206–225.CrossRefGoogle Scholar
  21. Kummerow, C., & Barnes, W. (1998). The tropical rainfall measuring mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15, 809–819.CrossRefGoogle Scholar
  22. Liang, L., Schwartz, M. D., & Fei, S. (2011). Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sensing of Environment, 115, 143–157.CrossRefGoogle Scholar
  23. Lieth, H. (1974). Phenology and seasonality modeling. Berlin, Heidelberg: Springer Berlin Heidelberg.CrossRefGoogle Scholar
  24. Meza, F. J. (2013). Recent trends and ENSO influence on droughts in northern Chile: an application of the standardized precipitation evapotranspiration index. Weather and Climate Extremes, 1, 51–58.CrossRefGoogle Scholar
  25. Millennium Ecosystem Assessment. 2005. Ecosystem and human wellbeing. Desertification synthesis. Washington DC: World resource Institute
  26. Naeem, S., Chapin III, F.S., Constanza, R., Ehrlich, P.R., Golley, F.B., Hooper, D.U., Lawton, J.H., O’Neill, R. V., Mooney, H. A., Sala, O. E., Symstad, A. J., Tilman, D., 1999. Biodiversity and ecosystem functioning: maintaining natural life support processes., Nov 14, 2014.
  27. NASA Earth Observing System Data and Information System (EOSDIS), 2013, Multi-satellite precipitation analysis (TMPA 3B43 version 7, product on a 0.25° × 0.25° latitude-longitude grid (
  28. Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B., & Running, S. W. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science (New York, N.Y.), 300, 1560–1563.CrossRefGoogle Scholar
  29. Nezlin, N. P., Kostianoy, A. G., & Li, B.-L. (2005). Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. Journal of Arid Environments, 62(4), 677–700.CrossRefGoogle Scholar
  30. Olagunju, T. E. (2015). Drought, desertification and the Nigerian environment: a review. Journal of Ecology and The Natural Environment, 7(7), 196–209.CrossRefGoogle Scholar
  31. Parga, F., León, A., Vargas, X., Fuster, Y., 2006. El índice de pobreza hídrica aplicado a la cuenca del río Limarí en Chile semiárido. Eval. Usos del Agua en Tierras Secas de Iberoamérica, 93–109.Google Scholar
  32. Paruelo, J. M. (2008). La caracterización funcional de ecosistemas mediante sensores remotos. Revista Ecosistemas, 17(3), 4–22.Google Scholar
  33. Paruelo, J. M., Oesterheld, M., Bella, D., Carlos, M., Arzadum, M., Lafontaine, J., Cahuepé, M., & Rebella, C. M. (2000). Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science, 3, 189–195.CrossRefGoogle Scholar
  34. Perez-Quezada, J. F., Bown, H. E., Fuentes, J. P., Alfaro, F. A., & Franck, N. (2012). Effects of afforestation on soil respiration in an arid shrubland in Chile. Journal of Arid Environments, 83, 45–53.CrossRefGoogle Scholar
  35. Propastin, P.P., Kappas, M., Muratova, N.R., 2006. Temporal responses of vegetation to climatic factors in Kazakhstan and Middle Asia, shaping the change. XXIII FIG Congress, Munich, Germany, pp. 16.Google Scholar
  36. Squeo, F.A.., Ibacache, E., Warner B., Espinoza D., Aravena R., Gutierréz J.R., 2006. Productividad y diversidad florística de la Vega Los Tambos, Cordillera de Doña Ana: variabilidad interanual, herbivoría y nivel freático. Geoecología de los Andes Desérticos: La Alta Montaña del Valle del Elqui, Ediciones Universidad de La Serena, La Serena, Chile, pp. 333–362.Google Scholar
  37. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment., 8, 127–150.CrossRefGoogle Scholar
  38. Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International Journal of Remote Sensing, 7, 1395–1416.CrossRefGoogle Scholar
  39. van Leeuwen, W., Hartfield, K., Miranda, M., & Meza, F. (2013). Trends and ENSO/AAO driven variability in NDVI derived productivity and phenology alongside the Andes mountains. Remote Sensing, 5, 1177–1203.CrossRefGoogle Scholar
  40. van Leeuwen, W. J. D., Davison, J. E., Casady, G. M., & Marsh, S. E. (2010). Phenological characterization of desert sky island vegetation communities with remotely sensed and climate time series data. Remote Sensing, 2, 388–415.CrossRefGoogle Scholar
  41. van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: an overview. Climatic Change, 109, 5–31.CrossRefGoogle Scholar
  42. Verbist, K., Santibañez, F., Gabriels, D., & Soto, G., 2010. ATLAS de Zonas Áridas de América Latina y el Caribe. Documento Técnico del PHI-LAC, (25), 48.Google Scholar
  43. Vicuña, S., Garreaud, R. D., & McPhee, J. (2011). Climate change impacts on the hydrology of a snowmelt driven basin in semi-arid Chile. Climatic Change, 105, 469–488.CrossRefGoogle Scholar
  44. Wang, C., Cao, R., Chen, J., Rao, Y., & Tang, Y. (2015). Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the northern hemisphere. Ecological Indicators, 50, 62–68.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francisco E. Glade
    • 1
  • Marcelo D. Miranda
    • 1
    • 2
    Email author
  • Francisco J. Meza
    • 1
    • 3
  • Willem J. D. van Leeuwen
    • 4
    • 5
  1. 1.Department of Ecosystem and EnvironmentPontificia Universidad Católica de ChileSantiagoChile
  2. 2.Center of Applied Ecology & Sustainability (CAPES)Pontificia Universidad Católica de ChileSantiagoChile
  3. 3.Centro Interdisciplinario de Cambio GlobalPontificia Universidad Católica de ChileSantiagoChile
  4. 4.School of Natural Resources and the Environment, Office of Arid Lands Studies, Arizona Remote Sensing CenterThe University of ArizonaTucsonUSA
  5. 5.School of Geography and DevelopmentThe University of ArizonaTucsonUSA

Personalised recommendations