Mapping regions of high temporal variability in Africa

Long term observation of space-borne remote sensing data provides a means to explore temporal variation on the Earth’s surface. This improved understanding of variability is required by numerous global change studies to explain annual and interannual trends and to separate those from individual events. This knowledge also can be included into budgeting and modeling for global change studies. The study employs daily 8km NOAA AVHRR data of the Pathfinder program to study changes in the annual variability of the African continent between 1982 and 2000. The daily data were processed to improved 10-day composites using an iterative approach including metadata and robust statistical techniques. Seasonality analysis using harmonics and its explained variance is combined with cross correlation between years with identical seasonality to account for temporal shifts. Deserts and the inner-tropical rain forest with none and moderate variability, respectively, are circled by a zone of transition assigned with high variability and changing seasonality. In between there are two gradual units of stability, usually identified as grassland or woodland by continental classifications. This relatively zonal pattern is altered in the eastern tropics and Namibia by varying topography and oceanic influences, respectively. The results can provide a basis for spatial distributed modeling of dynamic hotspots such as the transition zones bordering the savannas and for linking vegetation dynamics with continental climate models.

[1]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

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

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

[4]  D. Fuller,et al.  Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993 , 1998 .

[5]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[6]  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 .

[7]  D. Legates,et al.  Crop identification using harmonic analysis of time-series AVHRR NDVI data , 2002 .

[8]  Geoffrey M. Henebry,et al.  Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of central Asia , 2004, IEEE Geoscience and Remote Sensing Letters.

[9]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[10]  Christopher B. Field,et al.  Mapping the land surface for global atmosphere‐biosphere models: Toward continuous distributions of vegetation's functional properties , 1995 .

[11]  Stuart E. Marsh,et al.  Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands , 2001 .

[12]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

[13]  C. Tucker,et al.  Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999 , 2001, International journal of biometeorology.

[14]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[15]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[16]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[17]  Geoffrey M. Henebry,et al.  Land surface phenology and temperature variation in the International Geosphere–Biosphere Program high‐latitude transects , 2005 .

[18]  E. F. Ledrew,et al.  ARMA time series modelling of remote sensing imagery: A new approach for climate change studies , 2002 .

[19]  Alexander Ignatov,et al.  Global land monitoring using AVHRR time series , 1996 .

[20]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[21]  M. E. Maiden,et al.  NASA's Pathfinder data set programme: land surface parameters , 1994 .

[22]  K. Price,et al.  Interannual variations of the grassland boundaries bordering the eastern edges of the Gobi Desert in central Asia , 2004 .

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

[24]  Christian Töttrup,et al.  Mapping long-term changes in savannah crop productivity in Senegal through trend analysis of time series of remote sensing data , 2004 .

[25]  Compton J. Tucker,et al.  AVHRR data sets for determination of desert spatial extent , 1994 .