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

References

• Bartholomé , E. and Belward , A. S. 2005 . GLC2000: a new approach to global land cover mapping from Earth observation data. . International Journal of Remote Sensing , 26 : 1959 – 1977 .
   Web of Science ®Google Scholar
• Behrenfeld , M. J. , Randerson , J. T. , McClain , C. R. , Feldman , G. C. , Los , S. O. , Tucker , C. J. , Falkowski , P. G. , Field , C. B. , Frouin , R. , Esaias , W. E. , Kolber , D. D. and Pollack , N. H. 2001 . Biospheric primary production during an ENSO transition. . Science , 291 : 2594 – 2597 .
   PubMed Web of Science ®Google Scholar
• FAO, Food and Agriculture Organization of the United Nations. Available online at: http://www.fao.org/ (accessed April 2004)
   Google Scholar
• FAO/GIEWS, Global Information and Early Warning System. Available online at: http://geoweb.fao.org (accessed April 2004)
   Google Scholar
• GPCC, Global Prediction Climate Center (1994–2003). Available online at: http://gpcc.dwd.de (accessed December 2003)
   Google Scholar
• Groten , S. M. E. and Ocatre , R. 2002 . Monitoring the length of the growing season with NOAA. . International Journal of Remote Sensing , 23 : 2797 – 2815 .
   Web of Science ®Google Scholar
• Huete , A. , Didan , K. , Miura , T. , Rodriguez , E. P. , Gao , X. and Ferreira , L. G. 2002 . Overview of the radiometric and biophysic performance of the MODIS vegetation indices. . Remote Sensing of Environment , 83 : 195 – 213 .
   Web of Science ®Google Scholar
• Jönsson , P. and Eklundh , L. 2002 . Seasonality extraction by function fitting to time‐series of satellite sensor data. . IEEE Transactions on Geoscience and Remote Sensing , 40 : 1824 – 1832 .
   Web of Science ®Google Scholar
• Justice , C. O. , Townshend , J. R. G. , Holben , B. N. and Tucker , C. J. 1985 . Analysis of the phenology of global vegetation using meteorological satellite data. . International Journal of Remote Sensing , 6 : 1271 – 1318 .
   Web of Science ®Google Scholar
• Justice , C. O. , Holben , B. N. and Gwynne , M. D. 1986 . Monitoring East African vegetation using AVHRR data. . International Journal of Remote Sensing , 7 : 1453 – 1474 .
   Web of Science ®Google Scholar
• Kaduk , J. and Heimann , M. 1996 . A prognostic phenology model for global terrestrial carbon cycle models. . Climate Research , 6 : 1 – 19 .
   Web of Science ®Google Scholar
• Kawabata , A. , Ichii , K. and Yamaguchi , Y. 2001 . Global monitoring of inter‐annual changes in vegetation activities using NDVI and its relationships to temperature and precipitation. . International Journal of Remote Sensing , 22 : 1377 – 1382 .
   Web of Science ®Google Scholar
• Lambin , E. F. and Ehrlich , D. 1997 . 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. . Remote Sensing of Environment , 61 : 181 – 200 .
   Web of Science ®Google Scholar
• Lambin , E. F. and Strahler , A. 1994 . Multitemporal change‐vector analysis: a tool to detect and categorise land‐cover change processes using high temporal resolution satellite data. . Remote Sensing of Environment , 48 : 231 – 244 .
   Web of Science ®Google Scholar
• Lieth , H. 1998 . “ Aims and methods in phenological monitoring. ” . In Phenology in Seasonal Climates I. , Edited by: Lieth , H and Schwartz , M. D . 12, 1 – 22 . Leiden, , The Netherlands : Backhuys Publishers .
   Google Scholar
• Lloyd , D. 1990 . A phenological classification of terrestrial vegetation cover using short‐wave vegetation index imagery. . International Journal of Remote Sensing , 11 : 2269 – 2279 .
   Web of Science ®Google Scholar
• Lupo , F. , Reginster , I. and Lambin , E. F. 2001 . Monitoring land‐cover changes in West Africa with SPOT VEGETATION: impact of natural disasters in 1998–1999. . International Journal of Remote Sensing , 22 : 2633 – 2639 .
   Web of Science ®Google Scholar
• Markon , C. J. 2001 . “ Seven‐year phenological record of Alaskan ecoregions derived from AVHRR NDVI Data. ” . US Geological Survey Open‐File Report 01–11
   Google Scholar
• Markon , C. J. , Flemming , M. D. and Binnian , E. F. 1995 . Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time‐series data. . Polar Research , 31 : 179 – 190 .
   Google Scholar
• Mayaux , P. , Bartholomé , E. , Fritz , S. and Belward , A. 2004 . A new land‐cover map of Africa for the year 2000. . Journal of Biogeography , 31 : 861 – 877 .
   Web of Science ®Google Scholar
• Moody , A. and Johnson , D. M. 2001 . Land‐surface phenologies from AVHRR using discrete fourier transform. . Remote Sensing of Environment , 75 : 305 – 323 .
   Web of Science ®Google Scholar
• Moulin , S. , Kergoat , L. , Viovy , N. and Dedieu , G. G. 1997 . Global‐scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. . Journal of Climate , 10 : 1154 – 1170 .
   Web of Science ®Google Scholar
• Myneni , R. B. , Keeling , C. D. , Tucker , C. J. , Asrar , G. and Nemani , R. R. 1997 . Increased plant growth in the north high latitudes from 1981 to 1991. . Nature , 386 : 698 – 702 .
   Web of Science ®Google Scholar
• Potter , C. S. and Brooks , V. 1998 . Global analysis of empirical relations between annual climate and seasonality of NDVI. . International Journal Remote Sensing , 19 : 2921 – 2948 .
   Web of Science ®Google Scholar
• Reed , B. C. , Brown , J. F. , Vanderzee , D. , Loveland , T. S. , Merchant , J. W. and Ohlen , D. O. 1994 . Measuring phenological variability from satellite imagery. . Journal of Vegetation Science , 5 : 703 – 714 .
   Web of Science ®Google Scholar
• Roerink , G. J. , Meneti , M. , Soepboer , W. and Su , Z. 2003 . Assessment of climate impact on vegetation dynamics by using remote sensing. . Physics and Chemistry of the Earth , 28 : 103 – 109 .
   Web of Science ®Google Scholar
• Rudolf , B. , Fuchs , T. , Schneider , U. and Meyer‐Christoffer , A. 2003 . Introduction of the Global Precipitation Climatology Centre (GPCC), Deutscher Wetterdienst, Offenbach a.M. 16 Available on request by email from [email protected]
   Google Scholar
• Running , S. W. , Loveland , T. R. and Pierce , L. L. 1994 . A remote sensing based vegetation classification logic for use in global biogeochemical models. . Ambio , 23 : 77 – 81 .
   Web of Science ®Google Scholar
• SAWS, South African Weather Service. Avaliable online at: http://www.weathersa.co.za/ (accessed October 2003)
   Google Scholar
• Schaaf , C. B. , Gao , F. , Strahler , A. H. , Lucht , W. , Li , X. , Tsang , T. , Strugnell , N. , Zhang , X. Y. , Jin , Y. , Muller , J. P. , Lewis , P. , Barnsley , M. , Hobson , P. , Disney , M. , Roberts , G. , Dunderdale , M. , Doll , C. , d'Entremont , R. P. , Hu , B. , Liang , S. and Privette , J. L. 2002 . First operational BRDF, albedo and nadir reflectance products from MODIS. . Remote Sensing of Environment , 83 : 135 – 148 .
   Web of Science ®Google Scholar
• Stone , T. A. , Schlesinger , S. , Houghton , R. A. and Woodwell , G. M. 1994 . A map of the vegetation of South America based on satellite imagery. . Photogrammetric Engineering and Remote Sensing , 60 : 541 – 551 .
   Web of Science ®Google Scholar
• Tateishi , R. and Ebata , M. 2004 . Analysis of phenological change patterns using 1982–2000 Advanced Very High Resolution Radiometer (AVHRR) data. . International Journal of Remote Sensing , 25 : 2287 – 2300 .
   Web of Science ®Google Scholar
• Tobias , R. D. 1997 . An Introduction to Partial Least Squares Regression , Cary, NC : SAS Institute .
   Google Scholar
• UN/ReliefWeb, United Nations, Contributions for Natural Disasters. Available online at: http://www.reliefweb.int/ (accessed May 2004)
   Google Scholar
• USGS/FEWS Famine Early Warning System. Available online at: http://www.info.usaid.gov/fews (accessed March 2004)
   Google Scholar
• Vermote , E. , Tanre , D. , Deuze , J. L. , Herman , M. and Morcette , J.‐J. 2002 . Second simulation of the satellite signal in the solar spectrum (6S). . IEEE Transactions on Geoscience and Remote Sensing , 35 : 675 – 685 .
   Google Scholar
• White , F. 1983 . The Vegetation of Africa (Memoir to Accompany UNESCO Vegetation Map of Africa). Natural Resource Research , Paris : ORSTOM UNESCO . Volume XX
   Google Scholar
• White , M. A. , Thornton , P. E. and Running , S. W. 1997 . A continental phenology model for monitoring vegetation responses to inter‐annual climatic variability. . Global Biogeochemical Cycles , 11 : 217 – 234 .
   Web of Science ®Google Scholar
• Wolfe , R. E. , Nishihama , M. , Fleig , A. J. , Kuyper , J. A. , Roy , D. P. , Storey , J. C. and Patt , F. S. 2002 . Achieving sub‐pixel geolocation accuracy in support of MODIS land science. . Remote Sensing of Environment , 83 : 31 – 49 .
   Web of Science ®Google Scholar
• Zhang , X. , Friedl , M. A. , Schaaf , C. B. , Strahler , A. H. , Hodges , J. C. F. , Gao , F. , Reed , B. C. and Huete , A. 2003 . Monitoring vegetation phenology using MODIS. . Remote Sensing of Environment , 84 : 471 – 475 .
   Web of Science ®Google Scholar
• Zhang , X. , Friedl , M. A. , Schaaf , C. B. and Strahler , A. H. 2005 . Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. . Journal of Geophysical Research , 110 D12103
   Google Scholar