Articles | Volume 6, issue 9
Biogeosciences, 6, 1883–1902, 2009

Special issue: Biogeochemistry and function of Amazon Forest

Biogeosciences, 6, 1883–1902, 2009

  08 Sep 2009

08 Sep 2009

Influence of landscape heterogeneity on spatial patterns of wood productivity, wood specific density and above ground biomass in Amazonia

L. O. Anderson1, Y. Malhi1, R. J. Ladle1, L. E. O. C. Aragão1, Y. Shimabukuro2, O. L. Phillips3, T. Baker3, A. C. L. Costa4, J. S. Espejo5, N. Higuchi6, W. F. Laurance7, G. López-González3, A. Monteagudo8, P. Núñez-Vargas9, J. Peacock3, C. A. Quesada3,6, and S. Almeida10 L. O. Anderson et al.
  • 1School of Geography and the Environment, University of Oxford, Oxford, UK
  • 2Remote Sensing Division, National Institute for Space Research, São José dos Campos, Brazil
  • 3Earth and Biosphere Institute, School of Geography, University of Leeds, LS2 9JT, England, UK
  • 4Universidade Federal de Pará, Belém, Brazil
  • 5Universidad Nacional San Antonia Abad del Cusco, Cusco, Perú
  • 6Institito National de Pesquisas da Amazônia, Manaus, Brazil
  • 7Smithsonian Tropical Research Institute, Balboa, Panama
  • 8Herbario Vargas, Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú
  • 9Herbario Vargas, Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú
  • 10Museu Paraense Emilio Goeldi, Belém, Brazil

Abstract. Long-term studies using the RAINFOR network of forest plots have generated significant insights into the spatial and temporal dynamics of forest carbon cycling in Amazonia. In this work, we map and explore the landscape context of several major RAINFOR plot clusters using Landsat ETM+ satellite data. In particular, we explore how representative the plots are of their landscape context, and test whether bias in plot location within landscapes may be influencing the regional mean values obtained for important forest biophysical parameters. Specifically, we evaluate whether the regional variations in wood productivity, wood specific density and above ground biomass derived from the RAINFOR network could be driven by systematic and unintentional biases in plot location. Remote sensing data covering 45 field plots were aggregated to generate landscape maps to identify the specific physiognomy of the plots. In the Landsat ETM+ data, it was possible to spectrally differentiate three types of terra firme forest, three types of forests over Paleovarzea geomorphologycal formation, two types of bamboo-dominated forest, palm forest, Heliconia monodominant vegetation, swamp forest, disturbed forests and land use areas. Overall, the plots were generally representative of the forest physiognomies in the landscape in which they are located. Furthermore, the analysis supports the observed regional trends in those important forest parameters. This study demonstrates the utility of landscape scale analysis of forest physiognomies for validating and supporting the finds of plot based studies. Moreover, the more precise geolocation of many key RAINFOR plot clusters achieved during this research provides important contextual information for studies employing the RAINFOR database.

Final-revised paper