BGD Biogeosciences Discussions BGD 1810-6285 Copernicus GmbH Göttingen, Germany 10.5194/bgd-6-2039-2009 Influence of landscape heterogeneity on spatial patterns of wood productivity, wood specific density and above ground biomass in Amazonia Anderson L. O. 1 Malhi Y. 1 Ladle R. J. 1 Aragão L. E. O. C. 1 Shimabukuro Y. 2 Phillips O. L. 3 Baker T. 3 Costa A. C. L. 4 Espejo J. S. 5 Higuchi N. 6 Laurance W. F. 7 López-González G. 3 Monteagudo A. 8 Núñez-Vargas P. 9 Peacock J. 3 Quesada C. A. 3 6 Almeida S. 10 Vásquez R. 8 School of Geography and the Environment, University of Oxford, Oxford, UK Remote Sensing Division, National Inst. for Space Research, São José dos Campos, Brazil Earth and Biosphere Inst., School of Geography, University of Leeds, LS2 9JT, England, UK Universidade Federal de Pará, Belém, Brazil Universidad Nacional San Antonia Abad del Cusco, Cusco, Perú Institito National de Pesquisas da Amazônia, Manaus, Brazil Smithsonian Tropical Research Institute, Balboa, Panama Herbario Vargas, Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú Herbario Vargas, Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú Museu Paraense Emilio Goeldi, Belém, Brazil 18 02 2009 6 1 2039 2083 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.biogeosciences-discuss.net/6/2039/2009/bgd-6-2039-2009.html The full text article is available as a PDF file from http://www.biogeosciences-discuss.net/6/2039/2009/bgd-6-2039-2009.pdf

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 <i>terra firme</i> forest, three types of alluvial terrain forest, two types of bamboo-dominated forest, palm forest, <i>Heliconia</i> 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.

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