Increasing water use and droughts, along with climate variability and land use change, have seriously altered vegetation growth patterns and ecosystem response in several regions alongside the Andes Mountains. Thirty years of the new generation biweekly normalized difference vegetation index (NDVI3g) time series data show significant land cover specific trends and variability in annual productivity and land surface phenological response. Productivity is represented by the growing season mean NDVI values (July to June). Arid and semi-arid and sub humid vegetation types (Atacama desert, Chaco and Patagonia) across Argentina, northern Chile, northwest Uruguay and southeast Bolivia show negative trends in productivity, while some temperate forest and agricultural areas in Chile and sub humid and humid areas in Brazil, Bolivia and Peru show positive trends in productivity. The start (SOS) and length (LOS) of the growing season results show large variability and regional hot spots where later SOS often coincides with reduced productivity. A longer growing season is generally found for some locations in the south of Chile (sub-antarctic forest) and Argentina (Patagonia steppe), while central Argentina (Pampa-mixed grasslands and agriculture) has a shorter LOS. Some of the areas have significant shifts in SOS and LOS of one to several months. The seasonal Multivariate ENSO Indicator (MEI) and the Antarctic Oscillation (AAO) index have a significant impact on vegetation productivity and phenology in southeastern and northeastern Argentina (Patagonia and Pampa), central and southern Chile (mixed shrubland, temperate and sub-antarctic forest), and Paraguay (Chaco).
References
[1]
Millennium Assessment Board. Ecosystems and Human Well-being: Current Status and Trends. In Millennium Ecosystem Assessment Series; Hassan, R., Scholes, R., Ash, N., Eds.; Island Press: Washington, DC, USA, 2005.
[2]
Nemani, R.R.; Keeling, C.D.; Hashimoto, H.; Jolly, W.M.; Piper, S.C.; Tucker, C.J.; Myneni, R.B.; Running, S.W. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 2003, 300, 1560–1563.
[3]
Prince, S.D.; Goward, S.N. Global primary production: A remote sensing approach. J. Biogeogr 1995, 22, 815–835.
[4]
Ruimy, A.; Saugier, B.; Dedieu, G. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res.-Atmos 1994, 99, 5263–5283.
[5]
Anyamba, A.; Tucker, C.J. Analysis of sahelian vegetation dynamics using noaa-avhrr ndvi data from 1981–2003. J. Arid Environ 2005, 63, 596–614.
[6]
Fensholt, R.; Langanke, T.; Rasmussen, K.; Reenberg, A.; Prince, S.D.; Tucker, C.; Scholes, R.J.; Le, Q.B.; Bondeau, A.; Eastman, R.; et al. Greenness in semi-arid areas across the globe 1981–2007—An earth observing satellite based analysis of trends and drivers. Remote Sens. Environ 2012, 121, 144–158.
[7]
Ferreira, L.G.; Huete, A.R. Assessing the seasonal dynamics of the brazilian cerrado vegetation through the use of spectral vegetation indices. Int. J. Remote Sens 2004, 25, 1837–1860.
[8]
Myneni, R.B.; Tucker, C.J.; Asrar, G.; Keeling, C.D. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J. Geophys. Res.-Atmos 1998, 103, 6145–6160.
[9]
Tucker, C.J.; Slayback, D.A.; Pinzon, J.E.; Los, S.O.; Myneni, R.B.; Taylor, M.G. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int. J. Biometeorol 2001, 45, 184–190.
[10]
Fensholt, R.; Rasmussen, K. Analysis of trends in the sahelian ‘rain-use efficiency’ using gimms ndvi, rfe and gpcp rainfall data. Remote Sens. Environ 2011, 115, 438–451.
[11]
Herrmann, S.M.; Anyamba, A.; Tucker, C.J. Recent trends in vegetation dynamics in the african sahel and their relationship to climate. Glob. Environ. Change 2005, 15, 394–404.
[12]
Kariyeva, J.; van Leeuwen, W. Environmental drivers of ndvi-based vegetation phenology in central asia. Remote Sens 2011, 3, 203–246.
[13]
Barbosa, H.A.; Huete, A.R.; Baethgen, W.E. A 20-year study of ndvi variability over the northeast region of brazil. J. Arid Environ 2006, 67, 288–307.
[14]
De Beurs, K.M.; Henebry, G.M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in kazakhstan. Remote Sens. Environ 2004, 89, 497–509.
[15]
Guerschman, J.P.; Paruelo, J.M. Agricultural impacts on ecosystem functioning in temperate areas of north and south america. Global Planet. Change 2005, 47, 170–180.
[16]
Olsson, L.; Eklundh, L.; Ardo, J. A recent greening of the sahel-trends, patterns and potential causes. J. Arid Environ 2005, 63, 556–566.
[17]
Vina, A.; Henebry, G.M. Spatio-temporal change analysis to identify anomalous variation in the vegetated land surface: Enso effects in tropical south america. Geophys. Res. Lett. 2005, doi:10.1029/2005GL023407.
[18]
Baldi, G.; Nosetto, M.D.; Aragon, R.; Aversa, F.; Paruelo, J.M.; Jobbagy, E.G. Long-term satellite ndvi data sets: Evaluating their ability to detect ecosystem functional changes in south america. Sensors 2008, 8, 5397–5425.
[19]
IPCC. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2007; p. 996.
[20]
White, M.A.; Nemani, R.R. Real-time monitoring and short-term forecasting of land surface phenology. Remote Sens. Environ 2006, 104, 43–49.
[21]
Phenology: An Integrative Environmental Science; Schwartz, M.D., Ed.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2003; p. 564.
[22]
Reed, B.C. Trend analysis of time-series phenology of north america derived from satellite data. GISci. Remote Sens 2006, 43, 24–38.
[23]
Zhang, X.Y.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.C.F.; Gao, F.; Reed, B.C.; Huete, A. Monitoring vegetation phenology using modis. Remote Sens. Environ 2003, 84, 471–475.
[24]
Schwartz, M.D.; Reed, B.C.; White, M.A. Assessing satellite-derived start-of-season measures in the conterminous USA. Int. J. Climatol 2002, 22, 1793–1805.
De Jong, R.; de Bruin, S.; de Wit, A.; Schaepman, M.E.; Dent, D.L. Analysis of monotonic greening and browning trends from global ndvi time-series. Remote Sens. Environ 2011, 115, 692–702.
[27]
Jolly, W.M.; Nemani, R.; Running, S.W. A generalized, bioclimatic index to predict foliar phenology in response to climate. Glob. Change Biol 2005, 11, 619–632.
[28]
Justice, C.O.; Townshend, J.R.G.; Holben, B.N.; Tucker, C.J. Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sens 1985, 6, 1271–1318.
[29]
Van Leeuwen, W.J.D. Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with modis multitemporal data. Sensors 2008, 8, 2017–2042.
[30]
Van Leeuwen, W.J.D.; Davison, J.E.; Casady, G.M.; Marsh, S.E. Phenological characterization of desert sky island vegetation communities with remotely sensed and climate time series data. Remote Sens 2010, 2, 388–415.
[31]
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.; et al. Intercomparison, interpretation, and assessment of spring phenology in north america estimated from remote sensing for 1982–2006. Glob. Change Biol 2009, 15, 2335–2359.
[32]
Wittemyer, G.; Rasmussen, H.B.; Douglas-Hamilton, I. Breeding phenology in relation to NDVI variability in free-ranging african elephant. Ecography 2007, 30, 42–50.
[33]
Eva, H.D.; Belward, A.S.; de Miranda, E.E.; Di Bella, C.M.; Gond, V.; Huber, O.; Jones, S.; Sgrenzaroli, M.; Fritz, S. A land cover map of south america. Glob. Change Biol 2004, 10, 731–744.
[34]
Garreaud, R.D.; Vuille, M.; Compagnucci, R.; Marengo, J. Present-day south american climate. Palaeogeogr. Palaeoclimatol 2009, 281, 180–195.
[35]
Pinzon, J.; Brown, M.E.; Tucker, C.J. Satellite Time Series Correction of Orbital Drift Artifacts using Empirical Mode Decomposition. In Hilbert-Huang Transform: Introduction and Applications; Huang, N., Ed.; World Scientific Publishing: London, UK, 2005; pp. 167–186.
[36]
Tucker, C.J.; Pinzon, J.E.; Brown, M.E.; Slayback, D.; Pak, E.W.; Mahoney, R.; Vermote, E.; Saleous, N.E. An extended avhrr 8-km ndvi data set compatible with modis and spot vegetation ndvi data. Int. J. Remote Sens 2005, 26, 4485–5598.
[37]
Hinojosa, L.; Villagrán, C. History of the southern south american forests, I: Paleobotanical, geological and climatical background on tertiary of southern south america. Rev. Chil. Hist. Nat 1997, 70, 225–239.
[38]
Paruelo, J.M.; Jobbagy, E.G.; Sala, O.E. Current distribution of ecosystem functional types in temperate south america. Ecosystems 2001, 4, 683–698.
[39]
Villagrán, C.; Hinojosa, L. History of the southern south american forests, II: Phytogeographical analisys. Rev. Chil. Hist. Nat 1997, 70, 241–267.
[40]
Houston, J.; Hartley, A.J. The central andean west-slope rainshadow and its potential contribution to the origin of hyper-aridity in the atacama desert. Int. J. Climatol 2003, 23, 1453–1464.
[41]
Acosta Reveles, I.L. Balance del modelo agroexportador en américa latina al comenzar el siglo xxi. Mundo Agrario 2006, 7, 1–25.
[42]
Fensholt, R.; Proud, S.R. Evaluation of earth observation based global long term vegetation trends—Comparing gimms and modis global ndvi time series. Remote Sens. Environ 2012, 119, 131–147.
[43]
Myneni, R.B.; Keeling, C.D.; Tucker, C.J.; Asrar, G.; Nemani, R.R. Increased plant growth in the northern high latitudes from 1981–1991. Nature 1997, 386, 698–702.
[44]
Trishchenko, A.P. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17,18 and METOP-A. Remote Sens. Environ 2009, 113, 335–341.
[45]
Trishchenko, A.P.; Cihlar, J.; Li, Z.Q. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sens. Environ 2002, 81, 1–18.
[46]
Brown, M.E.; Pinzon, J.E.; Didan, K.; Morisette, J.T.; Tucker, C.J. Evaluation of the consistency of long-term NDVI time series derived from avhrr, spot-vegetation, seawifs, modis, and landsat etm+ sensors. IEEE Trans. Geosci. Remote Sens 2006, 44, 1787–1793.
[47]
Van Leeuwen, W.J.D.; Orr, B.J.; Marsh, S.E.; Herrmann, S.M. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sens. Environ 2006, 100, 67–81.
[48]
Vermote, E.F.; Saleous, N.Z. Calibration of NOAA16 AVHRR over a desert site using MODIS data. Remote Sens. Environ 2006, 105, 214–220.
[49]
Vermote, E.; Kaufman, Y.J. Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views. Int. J. Remote Sens 1995, 16, 2317–2340.
[50]
White, M.A.; Running, S.W.; Thornton, P.E. The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern us deciduous forest. Int. J. Biometeorol 1999, 42, 139–145.
[51]
Cleland, E.E.; Chuine, I.; Menzel, A.; Mooney, H.A.; Schwartz, M.D. Shifting plant phenology in response to global change. Trends Ecol. Evol 2007, 22, 357–365.
[52]
Prince, S.D.; Goetz, S.J.; Goward, S.N. Monitoring primary production from earth observing satellites. Water Air Soil Pollut 1995, 82, 509–522.
[53]
J?nsson, P.; Eklundh, L. Timesat—A program for analyzing time-series of satellite sensor data. Comput. Geosci 2004, 30, 833–845.
[54]
J?nsson, P.; Eklundh, L. Users Guide for Timesat 2.3. Timesat—A Program for Analysing Time-Series of Satellite Sensor Data, Available online: http://www.nateko.lu.se/personal/Lars.Eklundh/TIMESAT/timesat2_3_users_manual.pdf (accessed on 24 November 2012).
[55]
Wolter, K.; Timlin, M.S. El ni?o/southern oscillation behaviour since 1871 as diagnosed in an extended multivariate enso index (mei.Ext). Int. J. Climatol 2011, 31, 1074–1087.
[56]
Wolter, K.; Timlin, M.S. NOAA Multivariate Enso Index (Mei), Available online: http://www.esrl.noaa.gov/psd/enso/mei/table.html (accessed on 24 November 2012).
[57]
NOAA Antarctic Oscillation Index (AAO). Available online: http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/monthly.aao.index.b79.current.ascii.table (accessed on 24 November 2012).
[58]
Mo, K.C. Relationships between low-frequency variability in the southern hemisphere and sea surface temperature anomalies. J. Clim 2000, 13, 3599–3610.
[59]
Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated world map of the koppen-geiger climate classification. Hydrol. Earth. Syst. Sci 2007, 11, 1633–1644.
[60]
Samanta, A.; Ganguly, S.; Vermote, E.; Nemani, R.R.; Myneni, R.B. Why is remote sensing of amazon forest greenness so challenging? Earth Interact 2012, 16, 1–14.
[61]
De La Maza, M.; Lima, M.; Meserve, P.L.; Gutierrez, J.R.; Jaksic, F.M. Primary production dynamics and climate variability: Ecological consequences in semiarid chile. Global Change Biol 2009, 15, 1116–1126.
[62]
Dessay, N.; Laurent, H.; Machado, L.A.T.; Shimabukuro, Y.E.; Batista, G.T.; Diedhiou, A.; Ronchail, J. Comparative study of the 1982–1983 and 1997–1998 el nino events over different types of vegetation in south america. Int. J. Remote Sens 2004, 25, 4063–4077.
[63]
Paruelo, J.M.; Garbulsky, M.F.; Guerschman, J.P.; Jobbagy, E.G. Two decades of normalized difference vegetation index changes in south america: Identifying the imprint of global change. Int. J. Remote Sens 2004, 25, 2793–2806.
[64]
Xiao, J.; Moody, A. Geographical distribution of global greening trends and their climatic correlates: 1982–1998. Int. J. Remote Sens 2005, 26, 2371–2390.
[65]
Nahuelhual, L.; Carmona, A.; Lara, A.; Echeverría, C.; González, M.E. Land-cover change to forest plantations: Proximate causes and implications for the landscape in south-central chile. Landscape Urban Plan 2012, 107, 12–20.
[66]
Gasparri, N.I.; Grau, H.R. Deforestation and fragmentation of Chaco dry forest in NW Argentina (1972–2007). Forest. Ecol. Manage 2009, 258, 913–921.
[67]
Izquierdo, A.E.; Grau, H.R. Agriculture adjustment, land-use transition and protected areas in Northwestern Argentina. J. Environ. Manage 2009, 90, 858–865.
[68]
Grau, H.R.; Aide, M. Globalization and land-use transitions in Latin America. Available online: http://www.ecologyandsociety.org/vol13/iss2/art16/ (accessed on 24 November 2012).
[69]
Clark, M.L.; Aide, T.M.; Grau, H.R.; Riner, G. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the dry Chaco ecoregion of South America. Remote Sens. Environ 2010, 114, 2816–2832.
[70]
Redo, D.J.; Aide, T.M.; Clark, M.L. The relative importance of socioeconomic and environmental variables in explaining land change in bolivia, 2001–2010. Ann. Assn. Amer. Geogr 2012, 102, 778–807.
[71]
Sparovek, G.; Berndes, G.; Barretto, A.G.D.O.P.; Klug, I.L.F. The revision of the Brazilian Forest Act: Increased deforestation or a historic step towards balancing agricultural development and nature conservation? Environ. Sci. Policy 2012, 16, 65–72.
[72]
Meza, F.J. Variability of reference evapotranspiration and water demands. Association to ENSO in the Maipo river basin, Chile. Global Planet. Change 2005, 47, 212–220.
[73]
Podesta, G.P.; Messina, C.D.; Grondona, M.O.; Magrin, G.O. Associations between grain crop yields in central-eastern Argentina and EL Nino-Southern Oscillation. J. Appl. Meteorol 1999, 38, 1488–1498.