Linking vegetation heterogeneity and functional attributes of temperate grasslands through remote sensing

Applied Vegetation Science

Volumn 11, Issue 1, 2008, Pages 117-130

  Aragón, Roxana ^a   Oesterheld, Martín ^a  

^a UNIVERSIDAD DE BUENOS AIRES   (Argentina)

Author keywords

Canopy dynamics; Land cover classification; Landsat 5 TM; Landscape heterogeneity; MODIS; NDVI

Indexed keywords

EID: 46049117042     PISSN: 14022001     EISSN: None     Source Type: Journal    
DOI: 10.3170/2007-7-18429     Document Type: Article

References (63)

1. Adler, P.B. & Morales, J.M. 1999. Influence of environmental factors and sheep grazing on an Andean grassland. Journal of Range Management 52: 471-481.
2. Alvarez, R., Bonifaz, R., Lunetta, R.S., García, C., Gómez, G., Castro, R., Bernal, A. & Cabrera, A.L. 2003. Multitemporal land-cover classification of Mexico using Landsat MSS imagery. International Journal of Remote Sensing 24: 2505-2514.
3. Aragón, R., Carilla, J. & Cristobal, L. 2005. Fire, plant species richness and aerial biomass distribution in mountain grasslands of NW Argentina. In: Spehn, E., Lieberman, M. & Körner, C. (eds.) Land use changes and mountain biodiversity, pp. 93-103. Boca Raton, FL, US.
4. Brest, C.L. & Goward, S.N. 1987. Deriving surface albedo measurements from narrow band satellite data. International Journal of Remote Sensing 8: 351-367.
5. Burkart, S.E., León, R.J. & Movia, C.P. 1990. Inventario fitosociológico del pastizal de la depresión del Salado (Prov. de Bs. As) en un área representativa de sus principales ambientes. Darwiniana 30: 27-69.
6. Burkart, S.E., León, R.J.C., Perelman, S.B. & Agnusdei, M. 1998. The grasslands of the Flooding Pampa (Argentina): floristic heterogeneity of natural communities of the southern Rio Salado basin. Coenoses 13: 17-27.
7. Campbell, J.B. 1996. Introduction to remote sensing. 2nd. ed. Taylor & Francis, London, UK.
8. Chander, G. & Markham, B. 2003. Revised Landsat 5 TM radiometric calibration procedures and post-calibration dynamic ranges. IEEE Transactions on Geoscience and Remote sensing 41: 2674-2677.
9. Chaneton, E.J., Lemcoff, J.H. & Lavado, R.S. 1996. Nitrogen and phosphorus cycling in grazed and ungrazed plots of a temperate subhumid grassland in Argentina. Journal of Applied Ecology 33: 291-302.
10. Chaneton, E.J., Perelman S. & León R.J.C. 2005. Floristic heterogeneity of Flooding Pampa grasslands: a multi-scale analysis. Plant Biosystems 139: 245-254.
11. Cingolani, A.M., Renison, D., Zak, M.R. & Cabido, M.R. 2004. Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units. Remote Sensing of Environment 92: 84-97.
12. Demaria, M.R., McShea, W.J., Koy, K. & Maceira, N.O. 2004. Pampas deer conservation with respect to habitat loss and protected area considerations in San Luis, Argentina. Biological Conservation 115: 121-130.
13. Díaz, S., Acosta, A. & Cabido, M. 1992. Morphological analysis of herbaceous communities under different grazing regimes. Journal of Vegetation Science 3: 689-696.
14. Eyre, M.D., Luff, M.L., Staley, J.R. & Telfer, M.G. 2003. The relationship between British ground beetles (Coleoptera. Carabidae) and land cover. Journal of Biogeography 30: 719-730.
15. Fairbanks, D.H.K. & McGwire K.C. 2004. Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI. Global Ecology and Biogeography 13: 221-235.
16. Field, C.B., Randerson, J.T. & Malmstrom, C.M. 1995. Global net primary production: combining ecology and remote sensing. Remote Sensing of Environment 51: 74-88.
17. Fisher, R.A. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7: 179-188.
18. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Peñuelas, J. & Valentini, R. 1995. Relationships between NDVI, canopy structure and photosynthesis in three Californian vegetation types. Ecological Applications 5: 28-41.
19. Ginzo, H.D., Collantes, M.B. & Caso, O.H. 1982. Fertilization of a native grassland in the "Depresión del Rio Salado", Province of Buenos Aires: herbage dry matter accumulation and botanical composition. Journal of Range Management 35: 35-39.
20. Ginzo, H.D., Collantes, M.B. & Caso, O.H. 1986. Fertilization of a halophytic natural grassland in Argentina: Herbage dry matter, botanical composition, and mineral content. Turrialba 36: 453-460.
21. Giri, C. & Shrestha, S. 2000. Technical note: Forest fire mapping in Huay Kha Khaeng Wildlife Sanctuary, Thailand. International Journal of Remote Sensing 21: 2023-2030.
22. Grigera, G., Oesterheld, M. & Pacin, F. 2007. Monitoring forage production for farmers decision making. Agricultural Systems 94: 637-648.
23. Guerschman, J.P., Paraelo, J.M., Bella, C.D., Giallorenzi, M.C. & Pacin, F. 2003. Land cover classification in the Argentina Pampas using multi-temporal Landsat TM data. International Journal of Remote Sensing 24: 3381-3402.
24. Gutman, G. & Ignatov, A. 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing 19: 1533-1543.
25. Hidalgo, L. & Cauhepé, M. A. 1991. Producción de forraje de las comunidades forrajeras de la Pampa Deprimida. Revista de los CREA 145: 58-64.
26. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195-213.
27. Kerr, J.T. & Ostrovsky, M. 2003. From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution 18: 299-305.
28. Liang, S. 2000. Narrowband to broadband conversion of land surface albedo I Algorithms. Remote Sensing of Environment 76: 213-238.
29. Magrin, G.O., Travasso, M.I. & Rodríguez, G.R. 2005. Changes in climate and crop production during the 20th century in Argentina. Climatic Change 72: 229-249.
30. Mehner, H., Cutler, M., Fairbairn, D. & Thompson, G. 2004. Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors. Global Ecology and Biogeography. 13: 359-369.
31. Messina, J. P. & Walsh, S. J. 2001. 2.5D Morphogenesis: modeling landuse and landcover dynamics in the Ecuadorian Amazon. Plant Ecology 156: 75-88.
32. Milchunas, D.G. & Laurenroth, W.K. 1993. Quantitative effects of grazing on vegetation and soils over a global range of environments. Ecoogical Monographs 63: 327-366.
33. Monteith, J.L. 1981. Climatic variation and the growth of crops. Quarterly Journal Review of the Meteorological Society 107: 749-774.
34. Oesterheld, M. & Leon, R.J.C. 1987. El envejecimiento de las pasturas implantadas. Su efecto sobre la productividad. Turrialba 37: 29-36.
35. Parmesan, C. & Yohe, G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 37-42.
36. Paruelo, J.M., Oesterheld, M., Bella, C.M.D., Arzadum, M., Lafontaine, J., Cahuepe, M. & Rebella, C.M. 2000. Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science 3: 189-195.
37. Paruelo, J.M., Golluscio, R.A., Guerschman, J. P., Cesa, A., Jouvet, V.V. & Garbulsky, M. F. 2004. Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes. Global Ecology and Biogeography 13: 385-395.
38. Peet, N.B., Watkinson, A. R., Bell, D. J. & Kattel, B. J. 1999. Plant diversity in the threatened sub-tropical grasslands of Nepal. Biological Conservation 88: 193-206.
39. Perelman, S.B., León, R.J. & Oesterheld, M. 2001. Cross-scale vegetation patterns of Flooding Pampa grasslands. Journal of Ecology 89: 562-577.
40. Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J. & Stenseth, N.C. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution 20: 503-510.
41. Piñeiro, G., Oesterheld, M. & Paruelo, J.M. 2006. Seasonal variation in aboveground production and radiation use efficiency of temperate rangelands estimated through remote sensing. Ecosystems 9:357-373.
42. Posse, G. & Cingolani, A.M. 2004. A test of the use of NDVI data to predict secondary productivity. Applied Vegetation Science 7: 201-208.
43. Posse, G., Oesterheld, M. & Bella, C.M.D. 2005. Landscape, soil and meteorological influences on canopy dynamics of northern flooding Pampa grasslands, Argentina. Applied Vegetation Science 8: 49-56.
44. Prince, S.D. 1991. A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing 12: 1313-1330.
45. Ramankutty, N. & Foley, J.A. 1999. Estimating historical changes in land cover: North American croplands from 1850 to 1992. International Journal of Remote Sensing 8: 381-396.
46. Rao, C. R. 1948. The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society 10: 159-203.
47. Root, L. T., Price, J. T., Hall, K. R., Schneider, C., Rosenzweig, C. & Pounds, J. A. 2003. Fingerprints of global warming on wild animals and plants. Nature 421: 57-60.
48. Ruimy, A., Saugier, B. & Dedieu, G. 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. Journal of Geophysical Research 99: 5263-5283.
49. Ruiz-Luna, A. & Berlanga-Robles, C. A. 2003. Land use, land cover changes and coastal lagoon surface reduction associated with urban growth in northwest Mexico. Landscape Ecology 18: 159-171.
50. Sala, O.E. & Austin, A.T. 2000. Methods of estimating aboveground net primary productivity. In: Sala, O. E., Jackson, R.B., Mooney, H.A. & Howarth, R.W. (eds.) Methods in ecosystem science, pp. 31-43. Springer-Verlag, New York, NY, US.
51. Sala, O., Biondini, M. & Lauenroth, W. 1988. Bias in estimates of primary production: an analytical solution. Ecological Modelling 44: 43-55.
52. Scurlock, J. M. O., Johnson, K. & Olson, R. J. 2002. Estimating net primary productivity from grassland biomass dynamics measurements. Global Change Biology 8: 736-753.
53. Sims, P., Singh, J. S. & Lauenroth, W. K. 1978. The structure and function of ten western North American grasslands. Journal of Ecology 66: 251-285.
54. Singh, J.S., Lauenroth, W.K. & Steinhorst, R.K. 1975. Review and assessment of various techniques for estimating net aerial primary production in grasslands from harvest data. Botanical Review 41: 181-232.
55. Soriano, A., León, R.J.C., Sala, O.E., Lavado, R.S., Deregibus, V.A., Cauhepe, M.A., Scaglia, O.A., Velázquez, C.A. & Lemcoff, J.H. 1991. Rio de la Plata grasslands. In: Coupland, R. T (ed.) Natural grasslands, pp. 367-407. Elsevier, Amsterdam, NL.
56. Townsend, P.A. 2002. Relationships between forest structure and the detection of flood inundation in forested wetlands using C-band SAR. International Journal of Remote Sensing 23: 443-460.
57. Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E. & Steininger, M. 2003. Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution 18: 306-314.
58. van der Sande, C.J., de Jong, S.M. & de Roo, A.P.J. 2003. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation 4: 217-229.
59. Venier, L.A., Pearce, J., McKee, J., McKenney, D.W. & Niemi, G.J. 2004. Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes basin. Journal of Biogeography 31: 315-331.
60. Vervoorst, F.B. 1967. Las comunidades vegetales de la Depresión del Salado (Prov. de Buenos Aires). In: Serie Fitogeográfica, pp. 7-262. INTA, Buenos Aires, BR.
61. Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, B.K. & Driel, N.V. 2001. Completion of the 1990's National Land Cover Set for the Conterminous United States from Landsat Thematic Mapper Data and ancillary data sources. Photogrammetric Engineering and Remote Sensing 67: 650-662.
62. Wang, Y. P. & Barret, D.J. 2003. Estimating regional terrestrial carbon fluxes for the Australian continent using a multiple-constraint approach. Tellus 55b: 270-289.
63. Xiao, X., Hollinger, D., Aber, J., Goltz, M., Davidson, E.A., Zhang, Q. & Moore III, B. 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment 89: 519-534.