Categorization of land‐cover change processes based on phenological indicators extracted from time series of vegetation index data

A straightforward method for categorizing temporal patterns of land‐cover change is presented. Two successive years of enhanced vegetation index (EVI) data derived from the Moderate Resolution Imagery Spectrometer (MODIS) were analysed. Five phenological indicators were extracted. Based on the inter‐annual difference of each of the five indicators, indices of change in phenology were calculated. An unsupervised classification of these five indices of change applied to pixels characterized by a high change magnitude led to the identification of seven categories of land‐cover change patterns. Thirty‐one per cent of the change pixels could clearly be explained by a difference in only one or two phenological indicators, e.g. a shift in the start of the growing season or an interruption of the growing season due to floods. The remaining change pixels were explained by a combination of more than two indices of change. The output of this analysis is an allocation of change pixels to broad categories of land‐cover change as a preliminary step for finer resolution analyses.

[1]  C. Justice,et al.  Analysis of the phenology of global vegetation using meteorological satellite data , 1985 .

[2]  R. Tateishi,et al.  Analysis of phenological change patterns using 1982–2000 Advanced Very High Resolution Radiometer (AVHRR) data , 2004 .

[3]  D. Roy,et al.  Achieving sub-pixel geolocation accuracy in support of MODIS land science , 2002 .

[4]  S. Fritz,et al.  A new land‐cover map of Africa for the year 2000 , 2004 .

[5]  Eric F. Lambin,et al.  Monitoring land-cover changes in West Africa with SPOT Vegetation: Impact of natural disasters in 1998-1999 , 2001 .

[6]  S. M. E. Groten,et al.  Monitoring the length of the growing season with NOAA , 2002 .

[7]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[8]  Kazuhito Ichii,et al.  Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation , 2001 .

[9]  Christopher B. Field,et al.  Biospheric Primary Production During an ENSO Transition , 2001, Science.

[10]  Food Security Agriculture Organization of the United Nations (FAO) , 2004 .

[11]  M. D. Fleming,et al.  Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data , 1995, Polar Record.

[12]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[13]  M. Menenti,et al.  Assessment of climate impact on vegetation dynamics by using remote sensing , 2003 .

[14]  Alan H. Strahler,et al.  Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change pro , 1994 .

[15]  C. Markon Seven-year phenological record of the Alaskan ecoregions derived from advanced very high resolution radiometer normalized difference vegetation index data , 2001 .

[16]  Steven W. Running,et al.  A vegetation classification logic-based on remote-sensing for use in global biogeochemical models , 1994 .

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

[18]  D. Lloyd,et al.  A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery , 1990 .

[19]  C. Potter,et al.  Global analysis of empirical relations between annual climate and seasonality of NDVI , 1998 .

[20]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

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

[22]  R. Tobias An Introduction to Partial Least Squares Regression , 1996 .

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

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

[25]  G. Dedieu,et al.  Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements , 1997 .

[26]  Christopher O. Justice,et al.  Monitoring East African vegetation using AVHRR data , 1986 .

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

[28]  G. Woodwell,et al.  Map of the vegetation of South America based on satellite imagery , 1994 .

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

[30]  D. Shaw Global Information and Early Warning System , 2007 .

[31]  Aaron Moody,et al.  Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform , 2001 .

[32]  Jörg Kaduk,et al.  A prognostic phenology scheme for global terrestrial carbon cycle models , 1996 .

[33]  Eric F. Lambin,et al.  Land-cover changes in sub-saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale , 1997 .

[34]  H. Lieth Aims and methods in phenological monitoring , 1994 .

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