Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data

Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the Envisat MERIS terrestrial chlorophyll index (MTCI) data set for monitoring vegetation phenology, via its estimates of chlorophyll content. The MTCI was used to construct the phenological profile of and extract key phenological event dates from woodland and grass/heath land in Southern England as these represented a range of chlorophyll contents and different phenological cycles. The period 2003–2008 was selected as this was known to be a period with temperature and phenological anomalies. Comparisons of the MTCI-derived phenology data were made with ground indicators and climatic proxy of phenology and with other vegetation indices: MERIS global vegetation index (MGVI), MODIS normalized difference vegetation index (NDVI) and MODIS enhanced vegetation index (EVI). Close correspondence between MTCI and canopy phenology as indicated by ground observations and climatic proxy was evident. Also observed was a difference between MTCI-derived phenological profile curves and key event dates (e.g. green-up, season length) and those derived from MERIS MGVI, MODIS NDVI and MODIS EVI. The research presented in this article supports the use of the Envisat MTCI for monitoring vegetation phenology, principally due to its sensitivity to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change.

[1]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

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

[3]  N. Gobron,et al.  The MERIS Global Vegetation Index (MGVI): Description and preliminary application , 1999 .

[4]  M. D. Schwartz.  Advancing to full bloom: planning phenological research for the 21st century , 1999 .

[5]  C. Malmström,et al.  The effects of phenology on indirect measures of aboveground biomass in annual grasses , 2009 .

[6]  Mark A. Friedl,et al.  Sensitivity of vegetation phenology detection to the temporal resolution of satellite data , 2009 .

[7]  Jiyou Wu,et al.  Seasonal patterns in leaf reflectance red-edge characteristics , 1991 .

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

[9]  Karen C. Abbott,et al.  Sensitivity of plant-pollinator-herbivore communities to changes in phenology. , 2010 .

[10]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[11]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[12]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[13]  Benjamin W. Heumann,et al.  AVHRR Derived Phenological Change in the Sahel and Soudan, Africa, 1982 - 2005 , 2007 .

[14]  Paul J. Curran,et al.  Relationship between herbicide concentration during the 1960s and 1970s and the contemporary MERIS Terrestrial Chlorophyll Index (MTCI) for southern Vietnam , 2006, Int. J. Geogr. Inf. Sci..

[15]  J. Eitel,et al.  Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status , 2007 .

[16]  R. Ceulemans,et al.  How is phenology of grassland species influenced by climate warming across a range of species richness , 2008 .

[17]  Qinghua Guo,et al.  Variation in a satellite-based vegetation index in relation to climate in China , 2004 .

[18]  J. Dash,et al.  Estimating the relative abundance of C3 and C4 grasses in the Great Plains from multi-temporal MTCI data: issues of compositing period and spatial generalizability , 2010 .

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

[20]  C. Defila,et al.  Phytophenological trends in Switzerland , 2001, International journal of biometeorology.

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

[22]  N. Gobron,et al.  An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products , 2008 .

[23]  Corine Davids,et al.  Detecting contamination-induced tree stress within the Chernobyl exclusion zone , 2003 .

[24]  S. Wilson,et al.  Phenology of fine roots and leaves in forest and grassland , 2008 .

[25]  T. Sparks,et al.  Examples of phenological change, past and present, in UK farming , 2005 .

[26]  S. Schneider,et al.  Fingerprints of global warming on wild animals and plants , 2003, Nature.

[27]  Thomas Cooley,et al.  Summer to autumn changes in vegetation spectral indices of deciduous trees , 2008, Optical Engineering + Applications.

[28]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

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

[30]  T. A. Black,et al.  Responses of trembling aspen and hazelnut to vapor pressure deficit in a boreal deciduous forest. , 2000, Tree physiology.

[31]  Juergen Kreyling,et al.  Beyond gradual warming: extreme weather events alter flower phenology of European grassland and heath species , 2009 .

[32]  I. Leinonen,et al.  The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: an overview , 2000, International journal of biometeorology.

[33]  Rik Leemans,et al.  Faculty Opinions recommendation of European phenological response to climate change matches the warming pattern. , 2006 .

[34]  Bradley C. Reed,et al.  Trend Analysis of Time-Series Phenology of North America Derived from Satellite Data , 2006 .

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

[36]  N. Gobron,et al.  Evaluation of the MERIS/ENVISAT FAPAR product , 2007 .

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

[38]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[39]  Xiaoqiu Chen,et al.  Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China , 2005 .

[40]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[41]  Annette Menzel,et al.  Growing season extended in Europe , 1999, Nature.

[42]  Michele Meroni,et al.  Remote sensing of larch phenological cycle and analysis of relationships with climate in the Alpine region , 2010 .

[43]  Benjamin F. Zaitchik,et al.  Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity , 2005 .

[44]  Nadine Gobron,et al.  Monitoring biosphere vegetation 1998–2009 , 2010 .

[45]  Antje Müller,et al.  Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961-2000 , 2004 .

[46]  Andrea Molod,et al.  A New Look at Modeling Surface Heterogeneity: Extending Its Influence in the Vertical , 2003 .

[47]  S. A. Haredasht,et al.  Satellite Derived Forest Phenology and Its Relation with Nephropathia Epidemica in Belgium , 2010, International journal of environmental research and public health.

[48]  Paul J. Curran,et al.  MERIS: the re‐branding of an ocean sensor , 2005 .

[49]  C. Appenzeller,et al.  Inter-Annual Variability and Decadal Trends in Alpine Spring Phenology: A Multivariate Analysis Approach , 2005 .

[50]  Nuno Carvalhais,et al.  Comparative analysis of MODIS-FAPAR and MERIS-MGVI datasets: potential impacts on ecosystem modeling. , 2009 .

[51]  S. Bassow,et al.  HOW ENVIRONMENTAL CONDITIONS AFFECT CANOPY LEAF‐LEVEL PHOTOSYNTHESIS IN FOUR DECIDUOUS TREE SPECIES , 1998 .

[52]  Siham Tabik,et al.  Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II , 2010, Sensors.

[53]  J. Hansen,et al.  Biochemical and Biophysical Changes in Relation to Cold Hardiness , 2001 .

[54]  M. Friedl,et al.  Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product , 2010 .

[55]  Estimating the Nitrogen Concentration of Strawberry Plants from Its Spectral Response , 2006 .

[56]  Gregory A Carter,et al.  Optical properties of intact leaves for estimating chlorophyll concentration. , 2002, Journal of environmental quality.

[57]  George Alan Blackburn,et al.  Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves , 1999 .

[58]  M. Schaepman,et al.  Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006 , 2009 .

[59]  M. Perry,et al.  The development of a new set of long‐term climate averages for the UK , 2005 .

[60]  Wenjiang Huang,et al.  Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices , 2009 .

[61]  J. Abatzoglou,et al.  Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. , 2009 .

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

[63]  EK VIV,et al.  A parameterization of leaf phenology for the terrestrial ecosystem component of climate models , 2004 .

[64]  Lars Eklundh,et al.  Mapping insect defoliation in Scots pine with MODIS time-series data , 2009 .

[65]  Stefan Wunderle,et al.  Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements , 2008, Sensors.

[66]  Giles M. Foody,et al.  Land cover classification using multi‐temporal MERIS vegetation indices , 2007 .

[67]  A. Huete,et al.  Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination , 2000 .

[68]  Lars Eklundh,et al.  Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology , 2010 .

[69]  M. Gilabert,et al.  Vegetation dynamics from NDVI time series analysis using the wavelet transform , 2009 .

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

[71]  P. Newton,et al.  Flowering phenology in a species-rich temperate grassland is sensitive to warming but not elevated CO2. , 2008, The New phytologist.

[72]  Chuang Liu,et al.  Seasonal Variation of MODIS Vegetation Indexes and Their Statistical Relationship With Climate Over the Subtropic Evergreen Forest in Zhejiang, China , 2007, IEEE Geoscience and Remote Sensing Letters.

[73]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[74]  M. Cannell,et al.  Thermal time, chill days and prediction of budburst in Picea sitchensis , 1983 .

[75]  Giles M. Foody,et al.  Relations between tropical forest biophysical properties and data acquired in AVHRR channels 1–5 , 1996 .

[76]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[77]  Isabelle Chuine,et al.  Phenology is a major determinant of tree species range , 2001 .

[78]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[79]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[80]  P. Ciais,et al.  Influence of spring and autumn phenological transitions on forest ecosystem productivity , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[81]  I. C. Prentice,et al.  Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect , 2002, Science.

[82]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[83]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[84]  Giles M. Foody,et al.  Remote sensing of terrestrial chlorophyll content , 2009 .

[85]  Emilio Chuvieco,et al.  Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets , 2010 .

[86]  T. Sparks,et al.  How well do the central England temperature and the England and Wales precipitation series represent the climate of the UK? , 2006 .

[87]  Stefan Jansson,et al.  Intermittent low temperatures constrain spring recovery of photosynthesis in boreal Scots pine forests , 2004 .

[88]  J. Ardö,et al.  A recent greening of the Sahel—trends, patterns and potential causes , 2005 .

[89]  Bernard Pinty,et al.  MERIS potential for land applications , 1999 .

[90]  Jonas Ardö,et al.  Disentangling the effects of climate and people on Sahel vegetation dynamics , 2008 .

[91]  Matthias Dobbertin,et al.  Phenological Variation of Forest Trees , 2003 .

[92]  R. Betts.  Offset of the potential carbon sink from boreal forestation by decreases in surface albedo , 2000, Nature.

[93]  J. Mustard,et al.  Cross-scalar satellite phenology from ground, Landsat, and MODIS data , 2007 .

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

[95]  Frédéric Baret,et al.  Intercalibration of vegetation indices from different sensor systems , 2003 .

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

[97]  Fulu Tao,et al.  Land surface phenology dynamics and climate variations in the North East China Transect (NECT), 1982–2000 , 2008 .

[98]  W. Hargrove,et al.  Toward a national early warning system for forest disturbances using remotely sensed canopy phenology , 2009 .

[99]  S. Piao,et al.  Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999 , 2003 .

[100]  Jadunandan Dash,et al.  The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation , 2010 .

[101]  P. Ciais,et al.  Variations in satellite‐derived phenology in China's temperate vegetation , 2006 .

[102]  Mark A. Friedl,et al.  Drought-induced vegetation stress in southwestern North America , 2010 .

[103]  P. Kammer.  Developmental responses of subdominant grassland species to current weather conditions and their relevance for annual vegetation changes , 2002, Folia Geobotanica.

[104]  John S. Kimball,et al.  Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. , 2004 .

[105]  Dennis D. Baldocchi,et al.  Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America , 2000 .

[106]  Steven W. Running,et al.  A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: an application of MODIS leaf area index , 2003 .

[107]  Peter M. Atkinson,et al.  The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India , 2010 .

[108]  Peter R. J. North,et al.  Forest ecosystem chlorophyll content: Implications for remotely sensed estimates of net primary productivity , 2003 .

[109]  Bradley C. Reed,et al.  Remote Sensing Phenology , 2009 .

[110]  Mark A. Friedl,et al.  Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements , 2006 .

[111]  G. Nigh.  Impact of Climate, Moisture Regime, and Nutrient Regime on the Productivity of Douglas-Fir in Coastal British Columbia, Canada , 2006 .

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

[113]  O. Hoegh‐Guldberg,et al.  Ecological responses to recent climate change , 2002, Nature.

[114]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[115]  Jiri Nekovar,et al.  Phenology in central Europe – differences and trends of spring phenophases in urban and rural areas , 2000, International journal of biometeorology.

[116]  Alan H. Strahler,et al.  Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments , 2005 .

[117]  Jürgen Symanzik,et al.  On the use of the advanced very high resolution radiometer for development of prognostic land surface phenology models , 2007 .

[118]  M. Schaepman,et al.  Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics , 2009 .