
doi: 10.1111/geb.12256
AbstractIn 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 remote‐sensing 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.
spatial modelling, Ecology (science-metrix), 550, 0602 Ecology (for), 4102 Ecological applications (for-2020), 3103 Ecology (for-2020), Clinical Research (rcdc), remote sensing, Clinical Research, lidar, tropical forests, 4102 Ecological Applications (for-2020), Allometry, 31 Biological Sciences (for-2020), tree height, Ecology, 41 Environmental Sciences (for-2020), biomass, 0501 Ecological Applications (for), Biological Sciences, wood density, 4104 Environmental management (for-2020), 4104 Environmental Management (for-2020), Environmental Management, Ecological Applications, 0406 Physical Geography and Environmental Geoscience (for), Environmental Sciences
spatial modelling, Ecology (science-metrix), 550, 0602 Ecology (for), 4102 Ecological applications (for-2020), 3103 Ecology (for-2020), Clinical Research (rcdc), remote sensing, Clinical Research, lidar, tropical forests, 4102 Ecological Applications (for-2020), Allometry, 31 Biological Sciences (for-2020), tree height, Ecology, 41 Environmental Sciences (for-2020), biomass, 0501 Ecological Applications (for), Biological Sciences, wood density, 4104 Environmental management (for-2020), 4104 Environmental Management (for-2020), Environmental Management, Ecological Applications, 0406 Physical Geography and Environmental Geoscience (for), Environmental Sciences
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