Seeing the forest beyond the trees.

• Autor(a) principal: SAATCHI, S.
• Data de Publicação: 2014
• Outros Autores: MASCARO, J., XU, L., KELLER, M., YANG, Y., DUFFY, P., ESPÍRITO-SANTO, F., BACCINI, A., CHAMBERS, J., SCHIMEL, D.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1001136
• topic: Spatial modelling; Tree height; Allometry; Biomass; Lidar; Remote sensing; Tropical forests; Wood density

In a recent paper (Mitchard et al. 2014, Global Ecology and Biogeography, 23, 935?946) a new map of forest biomass based on a geostatistical model of field data for the Amazon (and surrounding forests) was presented and contrasted with two earlier maps based on remotesensing data Saatchi et al. (2011; RS1) and Baccini et al. (2012; RS2). Mitchard et al. concluded that both the earlier remote-sensing based maps were incorrect because they did not conform to Mitchard et al. interpretation of the field-based results. In making their case, however, they misrepresented the fundamental nature of primary field and remote-sensing data and committed critical errors in their assumptions about the accuracy of research plots, the interpolation methodology and the statistical analysis. By ignoring the large uncertainty associated with ground estimates of biomass and the significant under-sampling and spatial bias of research plots, Mitchard et al. reported erroneous trends and artificial patterns of biomass over Amazonia. Because of these misrepresentations and methodological flaws, we find their critique of the satellite-derived maps to be invalid.