This article describes and compares six disaggregation methods used to produce a dasymetric population density grid of the European Union at a 100 m resolution. Population data were initially available at commune level. The main ancillary information source was CORINE land cover, a land cover map distributed by the European Environment Agency. Information from the Eurostat point survey, land use/cover area frame survey, was also integrated in the parameter estimation of some of the approaches tested. Accurate population data for 1 km cell grids were provided by the Statistical Offices of Austria, Denmark, Finland, the Netherlands, Northern Ireland, Estonia and Sweden. These data provided the basic reference to quantify the accuracy of each method. The best results were obtained with a modified version of the limiting variable method (Eicher, C. and Brewer, C., 2001. Dasymetric mapping and areal interpolation: implementation and evaluation. Cartography and Geographic Information Science, 28, 125–138) that could be implemented, thanks to the national reference grids. For other methods the parameters could be estimated without using the reference grids; among them a method based on logit regression gave the best results. Compared with the traditional choropleth maps that represent a homogeneous density in each commune, the accuracy improvement of the disaggregated maps ranged between 20% and 67% (between 46% and 67% for the best method).

This paper outlines the methods and results for monitoring forest change and resulting carbon emissions for the 1990–2000 and 200–2005 periods carried out over tropical Central and South America. To produce our forest change estimates we used a systematic sample of medium resolution satellite data processed to forest change maps covering 1230 sites of 20 km by 20 km, each located at the degree confluence. Biomass data were spatially associated to each individual sample site so that annual carbon emissions could be estimated. For our study area we estimate that forest cover in the study area had fallen from 763 Mha (s.e. 10 Mha) in 1990 to 715 Mha (s.e. 10 Mha) in 2005. During the same period other wooded land (i.e., non-forest woody vegetation) had fallen from 191 Mha (s.e. 5.5 Mha) to 184 Mha (s.e. 5.5 Mha). This equates to an annual gross loss of 3.74 Mha∙y⁻¹ of forests (0.50% annually) between 1990 and 2000, rising to 4.40 Mha∙y⁻¹ in the early 2000s (0.61% annually), with Brazil accounting for 69% of the total losses. The annual carbon emissions from the combined loss of forests and other wooded land were calculated to be 482 MtC∙y⁻¹ (s.e. 29 MtC∙y⁻¹) for the 1990s, and 583 MtC∙y⁻¹ (s.e. 48 MtC∙y⁻¹) for the 2000 to 2005 period. Our maximum estimate of sinks from forest regrowth in tropical South America is 92 MtC∙y⁻¹. These estimates of gross emissions correspond well with the national estimates reported by Brazil, however, they are less than half of those reported in a recent study based on the FAO country statistics, highlighting the need for continued research in this area.

Very high resolution (VHR) images are a valuable information source to estimate land cover area and land cover change. When full coverage of a region with VHR images is not affordable, a sample of images can be considered. Square grids provide a practical sampling frame for VHR images. When using a land cover map as pseudo-truth, the sampling variance is easily assessed but may be overestimated if the land cover map has a coarse resolution. To estimate the potential sampling variance of a cluster sampling scheme, we propose a method based on intra-cluster correlation (ICC) computed from a correlogram. The ‘equivalent number of points’ is a useful indicator to quantify cost-efficiency of sites of a given size. We obtained poor efficiency results for area estimation of major land cover types in the European Union (EU) with a sample of 10 km × 10 km sites, but results are more encouraging for classes with a more scattered layout or for land cover change.