Environmental Monitoring and Assessment

, Volume 185, Issue 4, pp 3057–3079 | Cite as

Ecosystem classifications based on summer and winter conditions

  • Margaret E. Andrew
  • Trisalyn A. Nelson
  • Michael A. Wulder
  • George W. Hobart
  • Nicholas C. Coops
  • Carson J. Q. Farmer
Article

Abstract

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

Keywords

Classification agreement Ecological regionalization Environmental domain classification Fraction of absorbed photosynthetically active radiation (fPAR) Map comparison Snow water equivalent (SWE) 

Abbreviations

ANOSIM

Analysis of similarity

DHI

Dynamic habitat index

fPAR

Fraction of absorbed photosynthetically active radiation

MODIS

Moderate-resolution imaging spectrometer

SSM/I

Special sensor microwave/imager

SWE

Snow water equivalent

References

  1. Andrew, M. E., Wulder, M. A., & Coops, N. C. (2011). How do butterflies define ecosystems? A comparison of ecological regionalization schemes. Biological Conservation, 144, 1409–1418.CrossRefGoogle Scholar
  2. Angert, A., Biraud, S., Bonfils, C., Henning, C. C., Buermann, W., Pinzon, J., et al. (2005). Drier summers cancel out the CO2 uptake enhancement induced by warmer springs. Proceedings of the National Academy of Sciences of the United States of America, 102, 10823–10827.CrossRefGoogle Scholar
  3. Bailey, R. G. (1995). Description of the ecoregions of the United States, 2nd edn. US Department of Agriculture, Forest Service, Miscellaneous Publication 1391.Google Scholar
  4. Bailey, R. G. (2004). Identifying ecoregion boundaries. Environmental Management, 34, S14–S26.CrossRefGoogle Scholar
  5. Bailey, R. G., Pfister, R. D., & Henderson, J. A. (1978). Nature of land and resource classification—review. Journal of Forestry, 76, 650–655.Google Scholar
  6. Bailey, R. G., Zoltai, S. C., & Wiken, E. B. (1985). Ecological regionalization in Canada and the United States. Geoforum, 16, 265–275.CrossRefGoogle Scholar
  7. Baker, W. L., & Weisberg, P. J. (1997). Using GIS to model tree population parameters in the Rocky Mountain National Park forest–tundra ecotone. Journal of Biogeography, 24, 513–526.CrossRefGoogle Scholar
  8. Banner, A., Meidinger, D. V., Lea, E. C., Maxwell, R. E., & Von Sacken, B. C. (1996). Ecosystem mapping methods for British Columbia. Environmental Monitoring and Assessment, 39, 97–117.CrossRefGoogle Scholar
  9. Barton, J. L., & Metzeling, L. (2004). The development of biological objectives for streams in a single catchment: a case study on the Catchment of Western Port Bay, Victoria, Australia. Environmental Monitoring and Assessment, 95, 239–256.CrossRefGoogle Scholar
  10. Beauchesne, P., Ducruc, J. P., & Gerardin, V. (1996). Ecological mapping: a framework for delimiting forest management units. Environmental Monitoring and Assessment, 39, 173–186.CrossRefGoogle Scholar
  11. Brown, R. D. (2000). Northern hemisphere snow cover variability and change, 1915-97. Journal of Climate, 13, 2339–2355.CrossRefGoogle Scholar
  12. Brown, R. D., & Braaten, R. O. (1998). Spatial and temporal variability of Canadian monthly snow depths, 1946-1995. Atmosphere-Ocean, 36, 37–54.CrossRefGoogle Scholar
  13. Bunn, A. G., & Goetz, S. J. (2006). Trends in satellite-observed circumpolar photosynthetic activity from 1982 to 2003: the influence of seasonality, cover type, and vegetation density. Earth Interactions, 10, 12.CrossRefGoogle Scholar
  14. Carmean, W. H. (1996). Forest site-quality estimation using forest ecosystem classification in northwestern Ontario. Environmental Monitoring and Assessment, 39, 493–508.CrossRefGoogle Scholar
  15. Castilla, G., Larkin, K., Linke, J., & Hay, G. J. (2009). The impact of thematic resolution on the patch-mosaic model of natural landscapes. Landscape Ecology, 24, 15–23.CrossRefGoogle Scholar
  16. Cheruvelil, K. S., Soranno, P. A., Bremigan, M. T., Wagner, T., & Martin, S. L. (2008). Grouping lakes for water quality assessment and monitoring: the roles of regionalization and spatial scale. Environmental Management, 41, 425–440.CrossRefGoogle Scholar
  17. Clarke, K. R. (1993). Nonparametric multivariate analyses of changes in community structure. Australian Journal of Ecology, 18, 117–143.CrossRefGoogle Scholar
  18. Commission for Environmental Cooperation. (1997). Ecological regions of North America: toward a common perspective. Montreal: Commission for Environmental Cooperation.Google Scholar
  19. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.CrossRefGoogle Scholar
  20. Convention on Biological Diversity. (2004). Decisions adopted by the conference of the parties to the convention on biological diversity at its seventh meeting. Montreal: Convention on Biological Diversity.Google Scholar
  21. Coops, N. C., Wulder, M. A., Duro, D. C., Han, T., & Berry, S. (2008). The development of a Canadian dynamic habitat index using multi-temporal satellite estimates of canopy light absorbance. Ecological Indicators, 8, 754–766.CrossRefGoogle Scholar
  22. Coops, N. C., Wulder, M. A., & Iwanicka, D. (2009). An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, 4, 8–22.CrossRefGoogle Scholar
  23. Cushman, S. A., McGarigal, K., & Neel, M. C. (2008). Parsimony in landscape metrics: strength, universality, and consistency. Ecological Indicators, 8, 691–703.CrossRefGoogle Scholar
  24. Dark, S. J., & Bram, D. (2007). The modifiable areal unit problem (MAUP) in physical geography. Progress in Physical Geography, 31, 471–479.CrossRefGoogle Scholar
  25. Derksen, C., Wulder, M., Ledrew, E., & Goodison, B. (1998). Associations between spatially autocorrelated patterns of SSM/I-derived prairie snow cover and atmospheric circulation. Hydrological Processes, 12, 2307–2316.CrossRefGoogle Scholar
  26. Derksen, C., Walker, A., & Goodison, B. (2005). Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada. Remote Sensing of Environment, 96, 315–327.CrossRefGoogle Scholar
  27. Dungan, J. L., Perry, J. N., Dale, M. R. T., Legendre, P., Citron-Pousty, S., Fortin, M. J., et al. (2002). A balanced view of scale in spatial statistical analysis. Ecography, 25, 626–640.CrossRefGoogle Scholar
  28. Duro, D., Coops, N. C., Wulder, M. A., & Han, T. (2007). Development of a large area biodiversity monitoring system driven by remote sensing. Progress in Physical Geography, 31, 235–260.CrossRefGoogle Scholar
  29. Dussault, C., Ouellet, J. P., Courtois, R., Huot, J., Breton, L., & Jolicoeur, H. (2005). Linking moose habitat selection to limiting factors. Ecography, 28, 619–628.CrossRefGoogle Scholar
  30. Ecological Stratification Working Group. (1995). A national ecological framework for Canada. Ottawa: Agriculture and Agri-Food Canada, Research Branch, Centre for Land and Biological Resources Research and Environment Canada, State of the Environment Directorate, Ecozone Analysis Branch.Google Scholar
  31. ESRI. (2008). ArcMap, version 9.3. Redlands: ESRI.Google Scholar
  32. Evans, K. L., Warren, P. H., & Gaston, K. J. (2005). Species-energy relationships at the macroecological scale: a review of the mechanisms. Biological Reviews, 80, 1–25.CrossRefGoogle Scholar
  33. Farmer, C. J. Q., Nelson, T. A., Wulder, M. A., & Derksen, C. (2010). Identification of snow cover regimes through spatial and temporal clustering of satellite microwave brightness temperatures. Remote Sensing of Environment, 114, 199–210.CrossRefGoogle Scholar
  34. Fernández, N., Paruelo, J. M., & Delibes, M. (2010). Ecosystem functioning of protected and altered Mediterranean environments: a remote sensing classification in Doñana, Spain. Remote Sensing of Environment, 114, 211–220.CrossRefGoogle Scholar
  35. Foody, G. M. (2006). What is the difference between two maps? A remote senser's view. Journal of Geographical Systems, 8, 119–130.CrossRefGoogle Scholar
  36. Foster, J. L., Sun, C. J., Walker, J. P., Kelly, R., Chang, A., Dong, J. R., et al. (2005). Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sensing of Environment, 94, 187–203.CrossRefGoogle Scholar
  37. Fotheringham, A. S., & Wong, D. W. S. (1991). The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23, 1025–1044.CrossRefGoogle Scholar
  38. Fritz, S., & See, L. (2008). Identifying and quantifying uncertainty and spatial disagreement in the comparison of global land cover for different applications. Global Change Biology, 14, 1057–1075.CrossRefGoogle Scholar
  39. Giri, C., Zhu, Z. L., & Reed, B. (2005). A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 94, 123–132.CrossRefGoogle Scholar
  40. Goetz, S. J., Bunn, A. G., Fiske, G. J., & Houghton, R. A. (2005). Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proceedings of the National Academy of Sciences of the United States of America, 102, 13521–13525.CrossRefGoogle Scholar
  41. Government of Canada. (1996). The state of Canada's environment. Ottawa: Government of Canada.Google Scholar
  42. Graef, F., Schmidt, G., Schröder, W., & Stachow, U. (2005). Determining ecoregions for environmental and GMO monitoring networks. Environmental Monitoring and Assessment, 108, 189–203.CrossRefGoogle Scholar
  43. Hansen, M. C., Defries, R. S., Townshend, J. R. G., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.CrossRefGoogle Scholar
  44. Hawkins, C. P., & Vinson, M. R. (2000). Weak correspondence between landscape classifications and stream invertebrate assemblages: implications for bioassessment. Journal of the North American Benthological Society, 19, 501–517.CrossRefGoogle Scholar
  45. Hawkins, C. P., Norris, R. H., Gerritsen, J., Hughes, R. M., Jackson, S. K., Johnson, R. K., et al. (2000). Evaluation of the use of landscape classifications for the prediction of freshwater biota: synthesis and recommendations. Journal of the North American Benthological Society, 19, 541–556.CrossRefGoogle Scholar
  46. Hawkins, B. A., Field, R., Cornell, H. V., Currie, D. J., Guégan, J. F., Kaufman, D. M., et al. (2003). Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84, 3105–3117.CrossRefGoogle Scholar
  47. Herold, M., Mayaux, P., Woodcock, C. E., Baccini, A., & Schmullius, C. (2008). Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112, 2538–2556.CrossRefGoogle Scholar
  48. Host, G. E., Polzer, P. L., Mladenoff, D. J., White, M. A., & Crow, T. R. (1996). A quantitative approach to developing regional ecosystem classifications. Ecological Applications, 6, 608–618.CrossRefGoogle Scholar
  49. Huang, C., Geiger, E. L., & Kupfer, J. A. (2006). Sensitivity of landscape metrics to classification scheme. International Journal of Remote Sensing, 27, 2927–2948.CrossRefGoogle Scholar
  50. ITT Visual Information Solutions. (2009). ENVI, version 4.7. Boulder: ITT Visual Information Solutions.Google Scholar
  51. Jelinski, D. E., & Wu, J. G. (1996). The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology, 11, 129–140.CrossRefGoogle Scholar
  52. Jenerette, G. D., Lee, J., Waller, D. W., & Carlson, R. E. (2002). Multivariate analysis of the ecoregion delineation for aquatic systems. Environmental Management, 29, 67–75.CrossRefGoogle Scholar
  53. Jung, M., Henkel, K., Herold, M., & Churkina, G. (2006). Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, 534–553.CrossRefGoogle Scholar
  54. Kaptué-Tchuenté, A. T., Roujean, J. L., & De Jong, S. M. (2011). Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation, 13, 207–219.CrossRefGoogle Scholar
  55. Kerr, J. T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18, 299–305.CrossRefGoogle Scholar
  56. Kimball, J. S., Keyser, A. R., Running, S. W., & Saatchi, S. S. (2000). Regional assessment of boreal forest productivity using an ecological process model and remote sensing parameter maps. Tree Physiology, 20, 761–775.CrossRefGoogle Scholar
  57. Kimball, J. S., McDonald, K. C., Running, S. W., & Frolking, S. E. (2004). Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sensing of Environment, 90, 243–258.CrossRefGoogle Scholar
  58. Kirkpatrick, J. B., & Brown, M. J. (1994). A comparison of direct and environmental domain approaches to planning reservation of forest higher-plant communities and species in Tasmania. Conservation Biology, 8, 217–224.CrossRefGoogle Scholar
  59. Lafleur, P. M., & Humphreys, E. R. (2007). Spring warming and carbon dioxide exchange over low arctic tundra in central Canada. Global Change Biology, 14, 740–756.CrossRefGoogle Scholar
  60. Leathwick, J. R., Overton, J. M., & Mcleod, M. (2003). An environmental domain classification of New Zealand and its use as a tool for biodiversity management. Conservation Biology, 17, 1612–1623.CrossRefGoogle Scholar
  61. Litaor, M. I., Williams, M., & Seastedt, T. R. (2008). Topographic controls on snow distribution, soil moisture, and species diversity of herbaceous alpine vegetation, Niwot Ridge, Colorado. Journal of Geophysical Research-Biogeosciences, 113, G02008. doi:10.1029/2007JG000419.CrossRefGoogle Scholar
  62. Loveland, T. R., & Merchant, J. M. (2004). Ecoregions and ecoregionalization: geographical and ecological perspectives. Environmental Management, 34, S1–S13.CrossRefGoogle Scholar
  63. Mac Nally, R., Bennett, A. F., Brown, G. W., Lumsden, L. F., Yen, A., Hinkley, S., et al. (2002). How well do ecosystem-based planning units represent different components of biodiversity? Ecological Applications, 12, 900–912.CrossRefGoogle Scholar
  64. Mackey, B. G., Berry, S. L., & Brown, T. (2008). Reconciling approaches to biogeographical regionalization: a systematic and generic framework examined with a case study of the Australian continent. Journal of Biogeography, 35, 213–229.CrossRefGoogle Scholar
  65. Madsen, J., Tamstorf, M., Klaassen, M., Eide, N., Glahder, C., Rigét, F., et al. (2007). Effects of snow cover on the timing and success of reproduction in high-Arctic pink-footed geese Anser brachyrhynchus. Polar Biology, 30, 1363–1372.CrossRefGoogle Scholar
  66. McCormick, F. H., Peck, D. V., & Larsen, D. P. (2000). Comparison of geographic classification schemes for Mid-Atlantic stream fish assemblages. Journal of the North American Benthological Society, 19, 385–404.CrossRefGoogle Scholar
  67. McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-GTR-351. Portland: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.Google Scholar
  68. McMahon, G., Wiken, E. B., & Gauthier, D. A. (2004). Toward a scientifically rigorous basis for developing mapped ecological regions. Environmental Management, 34, S111–S124.CrossRefGoogle Scholar
  69. McRae, D. J. (1996). Use of forest ecosystem classification systems in fire management. Environmental Monitoring and Assessment, 39, 559–570.CrossRefGoogle Scholar
  70. Moody, E. G., King, M. D., Schaaf, C. B., Hall, D. K., & Platnick, S. (2007). Northern hemisphere five-year average (2000-2004) spectral albedos of surfaces in the presence of snow: statistics computed from Terra MODIS land products. Remote Sensing of Environment, 111, 337–345.CrossRefGoogle Scholar
  71. Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G., & Nemani, R. R. (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–702.CrossRefGoogle Scholar
  72. Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., et al. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83, 214–231.CrossRefGoogle Scholar
  73. Oksanen, J., Kindt, R., Legendre, P., O'Hara, B., Simpson, G. L., Solymos, P., et al. (2008). The vegan package. http://vegan.r-forge.r-project.org/.
  74. Oliver, I., Holmes, A., Dangerfield, J. M., Gillings, M., Pik, A. J., Britton, D. R., et al. (2004). Land systems as surrogates for biodiversity in conservation planning. Ecological Applications, 14, 485–503.CrossRefGoogle Scholar
  75. Olson, D. M., & Dinerstein, E. (1998). The Global 200: a representation approach to conserving the Earth's most biologically valuable ecoregions. Conservation Biology, 12, 502–515.CrossRefGoogle Scholar
  76. Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., et al. (2001). Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience, 51, 933–938.CrossRefGoogle Scholar
  77. Omernik, J. M. (1987). Ecoregions of the conterminous United States. Annals of the Association of American Geographers, 77, 118–125.CrossRefGoogle Scholar
  78. Omernik, J. M. (2004). Perspectives on the nature and definition of ecological regions. Environmental Management, 34, S27–S38.CrossRefGoogle Scholar
  79. Parker, W. H., Van Niejenhuis, A., & Ward, J. (1996). Genecological variation corresponding to forest ecosystem classification vegetation and soil types for jack pine and black spruce from northwestern Ontario. Environmental Monitoring and Assessment, 39, 589–599.CrossRefGoogle Scholar
  80. Parks Canada. (1997). National parks system plan. Ottawa: Parks Canada.Google Scholar
  81. Patton, D. R. (1975). A diversity index for quantifying habitat "edge". Wildlife Society Bulletin, 3, 171–173.Google Scholar
  82. Petit, S., Firbank, R., Wyatt, B., & Howard, D. (2001). MIRABEL: models for integrated review and assessment of biodiversity in European landscapes. Ambio, 30, 81–88.Google Scholar
  83. Pharo, E. J., & Beattie, A. J. (2001). Management forest types as a surrogate for vascular plant, bryophyte and lichen diversity. Australian Journal of Botany, 49, 23–30.CrossRefGoogle Scholar
  84. Pressey, R. L., & Logan, V. S. (1994). Level of geographical subdivision and its effects on assessments of reserve coverage—a review of regional studies. Conservation Biology, 8, 1037–1046.CrossRefGoogle Scholar
  85. Pyne, M. I., Rader, R. B., & Christensen, W. F. (2007). Predicting local biological characteristics in streams: a comparison of landscape classifications. Freshwater Biology, 52, 1302–1321.CrossRefGoogle Scholar
  86. R Core Development Team. (2008). R, version 2.8.1. R Core Development Team, http://www.r-project.org.
  87. Rabus, B., Eineder, M., Roth, A., & Bamler, R. (2003). The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. ISPRS Journal of Photogrammetry and Remote Sensing, 57, 241–262.CrossRefGoogle Scholar
  88. Räisänen, J. (2008). Warmer climate: less or more snow? Climate Dynamics, 30, 307–319.CrossRefGoogle Scholar
  89. SPSS Inc. (2008). SPSS, version 17.0. Chicago: SPSS Inc.Google Scholar
  90. StatSoft Inc. (2008). STATISTICA, version 8.0. Tulsa: StatSoft Inc.Google Scholar
  91. Tait, A. B. (1998). Estimation of snow water equivalent using passive microwave radiation data. Remote Sensing of Environment, 64, 286–291.CrossRefGoogle Scholar
  92. Thompson, R. S., Shafer, S. L., Anderson, K. H., Strickland, L. E., Pelltier, R. T., Bartlein, P. J., et al. (2004). Topographic, bioclimatic, and vegetation characteristics of three ecoregion classification systems in North America: comparisons along continent-wide transects. Environmental Management, 34, S125–S148.CrossRefGoogle Scholar
  93. Trakhtenbrot, A., & Kadmon, R. (2006). Effectiveness of environmental cluster analysis in representing regional species diversity. Conservation Biology, 20, 1087–1098.CrossRefGoogle Scholar
  94. Van Sickle, J., & Hughes, R. M. (2000). Classification strengths of ecoregions, catchments, and geographic clusters for aquatic vertebrates in Oregon. Journal of the North American Benthological Society, 19, 370–384.CrossRefGoogle Scholar
  95. Walker, D. A., Halfpenny, J. C., Walker, M. D., & Wessman, C. A. (1993). Long-term studies of snow-vegetation interactions. Bioscience, 43, 287–301.CrossRefGoogle Scholar
  96. Wells, F., Metzeling, L., & Newall, P. (2002). Macroinvertebrate regionalisation for use in the management of aquatic ecosystems in Victoria, Australia. Environmental Monitoring and Assessment, 74, 271–294.CrossRefGoogle Scholar
  97. Wiken, E. B., Gauthier, D., Marshall, I., Lawton, K., & Hirvonen, H. (1996). A perspective on Canada's ecosystems: an overview of the terrestrial and marine ecozones. Occasional paper no. 14. Ottawa: Canadian Council on Ecological Areas.Google Scholar
  98. Wright, R. G., Murray, M. P., & Merrill, T. (1998). Ecoregions as a level of ecological analysis. Biological Conservation, 86, 207–213.CrossRefGoogle Scholar
  99. Zhang, T., Ramakrishnan, R., & Livny, M. (1997). BIRCH: a new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, 1, 141–182.CrossRefGoogle Scholar
  100. Zhou, L. M., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., & Myneni, R. B. (2001). Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research-Atmospheres, 106, 20069–20083.CrossRefGoogle Scholar

Copyright information

© © Her Majesty the Queen in Right of Canada 2012

Authors and Affiliations

  • Margaret E. Andrew
    • 1
  • Trisalyn A. Nelson
    • 2
  • Michael A. Wulder
    • 1
  • George W. Hobart
    • 1
  • Nicholas C. Coops
    • 3
  • Carson J. Q. Farmer
    • 4
  1. 1.Canadian Forest Service (Pacific Forestry Centre)Natural Resources CanadaVictoriaCanada
  2. 2.Spatial Pattern Analysis and Research (SPAR) Laboratory, Department of GeographyUniversity of VictoriaVictoriaCanada
  3. 3.Department of Forest Resource ManagementUniversity of British ColumbiaVancouverCanada
  4. 4.National Centre for GeocomputationNational University of Ireland MaynoothMaynoothIreland

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