Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

Abstract The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.

[1]  Eric F. Lambin,et al.  Land-use and land-cover change : local processes and global impacts , 2010 .

[2]  José M. Paruelo,et al.  Identification of current ecosystem functional types in the Iberian Peninsula , 2006 .

[3]  R. Dean Graetz,et al.  Remote Sensing of Terrestrial Ecosystem Structure: An Ecologist’s Pragmatic View , 1990 .

[4]  H. Mooney,et al.  Human Domination of Earth’s Ecosystems , 1997, Renewable Energy.

[5]  O. Sala,et al.  Current Distribution of Ecosystem Functional Types in Temperate South America , 2001, Ecosystems.

[6]  Millenium Ecosystem Assessment Ecosystems and human well-being: synthesis , 2005 .

[7]  Rasmus Fensholt,et al.  Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers , 2012 .

[8]  Harold A. Mooney,et al.  International Ecosystem Assessment , 1999, Science.

[9]  C. Tucker,et al.  Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.

[10]  Jennifer Small,et al.  Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa , 2007 .

[11]  M. F. Parry,et al.  A Retrospective Analysis , 1990 .

[12]  K.A. Hogda,et al.  Climatic change impact on growing season in Fennoscandia studied by a time series of NOAA AVHRR NDVI data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[13]  X. Chen,et al.  An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China , 2001, International journal of biometeorology.

[14]  C. D. Keeling,et al.  Increased activity of northern vegetation inferred from atmospheric CO2 measurements , 1996, Nature.

[15]  John F. Mustard,et al.  A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data , 2007 .

[16]  A. Menzel,et al.  Trends in phenological phases in Europe between 1951 and 1996 , 2000, International journal of biometeorology.

[17]  Chang-Hoi Ho,et al.  Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008 , 2011 .

[18]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[19]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[20]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[21]  Wenquan Zhu,et al.  Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006 , 2012 .

[22]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[23]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[24]  Achim Röder,et al.  Mediterranean desertification and land degradation: Mapping related land use change syndromes based on satellite observations , 2008 .

[25]  G. Henebry,et al.  Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan , 2004 .

[26]  Xiaoqiu Chen,et al.  Relationships among phenological growing season, time‐integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China , 2002 .

[27]  Steven W. Running,et al.  Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari , 2004 .

[28]  C. Appenzeller,et al.  A comparative study of satellite and ground-based phenology , 2007, International journal of biometeorology.

[29]  P. Legendre,et al.  Forward selection of explanatory variables. , 2008, Ecology.

[30]  Chang‐Hoi Ho,et al.  Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2 × CO2 climate , 2011 .

[31]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[32]  José A. Sobrino,et al.  Global land surface phenology trends from GIMMS database , 2009 .

[33]  Eric F. Lambin,et al.  Land-Use and Land-Cover Change , 2006 .

[34]  R. Stöckli,et al.  European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset , 2004 .

[35]  Jürgen Vogt,et al.  Combining satellite derived phenology with climate data for climate change impact assessment , 2012 .

[36]  Christian Töttrup,et al.  Regional desertification: A global synthesis , 2008 .

[37]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[38]  Mark D. Schwartz,et al.  Surface phenology and satellite sensor-derived onset of greenness: an initial comparison , 1999 .

[39]  Sally Archibald,et al.  Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa , 2011 .

[40]  Massimo Menenti,et al.  Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data , 2000 .