Characterizing Global Land Cover Type and Seasonal Land Cover Dynamics at Moderate Spatial Resolution With MODIS Data

Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 11)


Until recently, the only land cover datasets available with global coverage were compiled from pre-existing maps and atlases based on ground surveys, national mapping programs, and highly generalized biogeographic maps (e.g., Olson and Watts 1982; Matthews 1983; Wilson and Henderson-Sellers 1985). In the 1990s, when Advanced Very High Resolution Radiometer (AVHRR)-derived global remote sensing datasets became available, it was possible to map land cover based on observable land cover properties (DeFries and Townshend 1994; DeFries et al. 1998; Loveland et al. 2000). As newer and better remote sensing data sources have emerged (e.g., MODIS, SPOT, MERIS), global land cover products continue to progress in both methodological maturity and map quality.


Land Cover Land Surface Temperature Land Cover Type Advance Very High Resolution Radiometer Advance Very High Resolution Radiometer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Arora V (2002) Modeling vegetation as dynamic component in soil-vegetation-atmosphere ­transfer schemes and hydrological models. Rev Geophys 40(2):3-1–3-26Google Scholar
  2. Asner GP, Townsend AR (2000) Satellite observation of El Niño effects on Amazon forest ­phenology and productivity. Geophys Res Lett 27(7):981–984ADSCrossRefGoogle Scholar
  3. Baldocchi D, Falge E, Wilson K (2001) A spectral analysis of biosphere–atmosphere trace gas flux densities and meteorological variables across hour to multi-year time scales. Agric For Meteorol 107(1):1–27CrossRefGoogle Scholar
  4. Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105–139CrossRefGoogle Scholar
  5. Bonan GB, Levis S, Kergoat L, Oleson KW (2002) Landscapes of plant functional types: an integrating concept for climate and ecosystem models. Global Biogeochem Cycles 16(2):doi:10.1029/2000GB001360Google Scholar
  6. de Beurs KM, Henebry GM (2005) Land surface phenology and temperature variation in the International Geosphere-Biosphere Programme high latitude transects. Global Chang Biol 11(5):779–790CrossRefGoogle Scholar
  7. DeFries RS and Townshend JRG (1994) Global land cover: comparison of ground-based datasets to classifications with AVHRR data. In: Environmental remote sensing from regional to global scales (ed. by G Foody and P Curran), John Wiley and Sons, ChichesterGoogle Scholar
  8. DeFries R, Hansen M, Townshend JGR, Sohlberg R (1998) Global land cover classifications at 8 km resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int J Remote Sens 19:3141–3168CrossRefGoogle Scholar
  9. Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139–158CrossRefGoogle Scholar
  10. Di Gregorio A, Jansen LJM (2000) Land Cover Classification System Concepts and User Manual. United Nations Food and Agriculture Organization Publishing Service, Report GCP/RAF/287/ITA, Rome, ItalyGoogle Scholar
  11. Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice IC, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science 309:570–574ADSCrossRefGoogle Scholar
  12. Foody GM, Campbell NA, Trodd NM, Wood TF (1992) Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric Engineering & Remote Sensing 58:1335–1341Google Scholar
  13. Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256–285MathSciNetMATHCrossRefGoogle Scholar
  14. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139MathSciNetMATHCrossRefGoogle Scholar
  15. Friedl MA, Brodley CE (1997) Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 61:399–409CrossRefGoogle Scholar
  16. Friedl MA, Brodley CE, Strahler AH (1999) Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans Geosci Remote Sens 37(2):969–977ADSCrossRefGoogle Scholar
  17. Friedl MA, Muchoney D, McIver DK, Gao F, Hodges JCF, Strahler AH (2000) Characterization of North American land cover from NOAA-AVHRR data using the EOS MODIS land cover classification algorithm. Geophys Res Lett 27(7):977–980ADSCrossRefGoogle Scholar
  18. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover ­mapping from MODIS: algorithms and early results. Remote Sens Environ 83(1–2):287–302CrossRefGoogle Scholar
  19. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–374MathSciNetMATHCrossRefGoogle Scholar
  20. Hansen M, Dubayah R, DeFries R (1996) Classification trees: an alternative to traditional land cover classifiers. Int J Remote Sens 17:1075–1081CrossRefGoogle Scholar
  21. Hansen MC, Defries RS, Townshend JRG, Sohlberg R (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21(6–7):1331–1364CrossRefGoogle Scholar
  22. Huete A, Didan K, Miura T, Rodriquez EP, Gao X, Ferreira LG (2002) Overview of radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213CrossRefGoogle Scholar
  23. Lotsch A, Tian Y, Friedl MA, Myneni RB (2001) Land cover mapping in support of LAI/FAPAR retrievals from EOS MODIS and MISR: classification methods and sensitivities to errors. Int J Remote Sens 24(10):1997–2016Google Scholar
  24. Loveland TR, Belward AS (1997) The IGBP-DIS global 1-km land cover dataset, DIScover: first results. Int J Remote Sens 65(9):1021–1031Google Scholar
  25. Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L, Merchant JW (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21(6–7):1303–1365CrossRefGoogle Scholar
  26. Lubchenco J (1998) Entering the Century of the Environment: a new social contract for science. Science 279:491–497ADSCrossRefGoogle Scholar
  27. Lucht W, Prentice IC, Myneni RB, Sitch S, Friedlingstein P, Cramer W, Bousquet P, Buermann W, Smith B (2002) Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296(5573):1687–1689ADSCrossRefGoogle Scholar
  28. Matthews E (1983) Global vegetation and land-use: new-high resolution data bases for climate studies. J Clim Appl Meteorol 22:474–487ADSCrossRefGoogle Scholar
  29. McIver DK, Friedl MA (2001) Estimating pixel-scale land cover classification confidence using non-parametric machine learning methods. IEEE Trans Geosci Remote Sens 39(9):1959–1968ADSCrossRefGoogle Scholar
  30. McIver DK, Friedl MA (2002) Using prior probabilities in decision tree classification of remotely sensed data. Remote Sens Environ 81:253–261CrossRefGoogle Scholar
  31. Mingers J (1989) An empirical comparison of pruning methods for decision tree induction. Mach Learn 4:227–243CrossRefGoogle Scholar
  32. Muchoney D, Strahler A, Hodges J, LoCastro J (1999) The IGBP DISCover confidence sites and the system for terrestrial ecosystem parameterization: tools for validating global land-cover data. Photogramm Eng Remote Sensing 65(9):1061–1067Google Scholar
  33. Myneni RB, Nemani RR, Running SW (1997) Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Trans Geosci Remote Sens 35(6):1380–1393ADSCrossRefGoogle Scholar
  34. Olson JS, Watts J (1982) Major world ecosystem complexes. In: Jones DB (ed) Earth’s vegetation and atmospheric carbon dioxide, carbon dioxide review. Oxford University Press, Oxford, pp 388–399Google Scholar
  35. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42ADSCrossRefGoogle Scholar
  36. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CAGoogle Scholar
  37. Ramankutty N, Foley JA (1998) Characterizing patterns of global land use: an analysis of global crop yield data. Global Biogeochem Cycles 12(4):667–685ADSCrossRefGoogle Scholar
  38. Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite data. J Veg Sci 5:703–714CrossRefGoogle Scholar
  39. Running SW, Hunt ER Jr (1993) Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, an application for global scale models. In: Ehleringer JR, Field CB (eds) Scaling physiological processes: leaf to globe. Academic Press, San Diego, pp 141–158CrossRefGoogle Scholar
  40. Running SW, Nemani RR (1991) Regional hydrologic and carbon balance response of forests resulting from potential climate change. Clim Change 19(4):349–368CrossRefGoogle Scholar
  41. Running SW, Loveland TR, Pierce LL, Nemani R, Hunt ER Jr (1995) A remote sensing-based classification logic for global land cover analysis. Remote Sens Environ 51(1):39–48CrossRefGoogle Scholar
  42. Sanderson EW, Malanding J, Levy MA, Redford KH, Wannebo AV, Woolmer G (2002) The human footprint and the last of the wild. Bioscience 52(10):891–904CrossRefGoogle Scholar
  43. Schaaf CB, Gao F, Strahler AH, Lucht W, Li X, Tsang T, Strugnell N, Zhang X, Jin Y, Muller J-P, Lewis PE, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, d’Entremont RP, Hu B, Liang S, Privette J, Roy DP (2002) First operational BRDF, albedo and nadir reflectance products from MODIS. Remote Sens Environ 83:135–148CrossRefGoogle Scholar
  44. Schowengerdt RA (1997) Remote sensing models and methods for image processing, 2nd edn. Academic Press, San DiegoGoogle Scholar
  45. Sellers PJ, Dickinson RE, Randall DA, Betts AK, Hall FG, Berry JA, Collatz GJ, Denning AS, Mooney HA, Nobre CA, Sato N, Field CB, Henderson-Sellers A (1997) Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275(5299):502–509CrossRefGoogle Scholar
  46. Strahler A, Muchoney D, Borak J, Gao F, Friedl M, Gopal S, Hodges J, Lambin E, McIver D, Moody A, Schaaf C, Woodcock C (1999) MODIS Land Cover Product, Algorithm Theoretical Basis Document (ATBD), Version 5.0. Center for Remote Sensing, Department of Geography, Boston University, Boston, MAGoogle Scholar
  47. Vitousek PM, Mooney HA, Lubchenco J, Melillo JM (1997) Human domination of earth’s ecosystems. Science 277:494–499CrossRefGoogle Scholar
  48. Wan Z, Zhang Y, Zhang YQ, Li Z-L (2002) Validation of the land-surface temperature products retrieved from Moderate Resolution Imaging Spectroradiometer data. Remote Sens Environ 83:163–180Google Scholar
  49. White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem Cycles 11:217–234ADSCrossRefGoogle Scholar
  50. Wilson KB, Baldocchi DD (2000) Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America. Agric For Meteorol 100(1):1–18CrossRefGoogle Scholar
  51. Wilson M, Henderson-Sellers A (1985) A global archive of land cover and soils data for use in general circulation climate models. J Climatol 5:119–143CrossRefGoogle Scholar
  52. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–575CrossRefGoogle Scholar
  53. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Schneider A (2004a) The footprint of urban climates on vegetation phenology. Geophys Res Lett 31:L12209. doi:10.1029/2004GL020137ADSCrossRefGoogle Scholar
  54. Zhang X, Friedl MA, Schaaf CB, Strahler AH (2004b) Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Chang Biol 10:1133–1145CrossRefGoogle Scholar
  55. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Liu Z (2005) Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. J Geophys Res – Atmospheres 110:D12103ADSCrossRefGoogle Scholar
  56. Zhang X, Friedl MA, Schaaf CB (2006) Global vegetation phenology from MODIS: evaluation of global patterns and comparison with in-situ measurements. J Geophys Res – Biogeosciences 111:G04017. doi:10.1029/2006JG00217CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Geography and EnvironmentCenter for Remote Sensing, Boston UniversityBostonUSA

Personalised recommendations