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Estimates of the area of land cover classes or land change are frequently calculated from land cover classification maps by counting the pixels labeled as each class in the map. This procedure is known to produce biased estimates of area for many widely used classification algorithms, including random forests. Poststratification estimation using the mapped classes as strata has been proposed to obtain unbiased estimates of the class areas. Still, the method requires additional sampling units, which may not be available or be the most efficient method depending on the application. Alternatively, consistent estimates of class areas can be obtained using class membership probabilities estimates from a random forest classification. This article demonstrates that, for a large sample and proper set of explanatory variables, the error of the predicted class membership probabilities obtained from a random forest classification converges to zero. Therefore, the expected class areas calculated from these probabilities converge to the population class areas. On average, the relative error of the expected class proportions computed by class membership probabilities from a random forests model was 40% points lower than the proportions estimated by pixel counting. Our proposed approach is also comparable to the area-adjusted method, which is currently considered the best practice by the remote sensing community. We recommend that class probability estimates area always retained and used for calculating expected class areas or area proportions based on our results. Our method reduces bias compared to statistics calculated by pixel counting and circumvents the need for poststratification area estimates under certain conditions. [ABSTRACT FROM AUTHOR]

• Overall, REDD+ interventions were found to be aligned with deforestation drivers. • Local interventions predominantly target small-scale drivers. • No single data acquisition method suffices to assess multifaceted deforestation drivers. • Multidisciplinary data sources complement each other in information on drivers. Efforts to reduce emissions from deforestation and forest degradation and enhancing forest carbon stocks (REDD+) have evolved over the past decade. Early REDD+ programs and local/subnational projects used various interventions (i.e. enabling measures, disincentives and incentives), implemented by government, the commercial and non-commercial private sector, but are currently understudied vis-à-vis their effectiveness to address site-specific drivers of deforestation and forest degradation (DD). We assess how well REDD+ interventions addressed DD at five project sites in Peru (1), Brazil (1), Vietnam (1) and Indonesia (2). Our study design includes an integrated assessment of remotely sensed, spatially modelled, and locally reported drivers. First, we observe follow-up land use from high resolution imagery as proxy for direct deforestation drivers. Second, spatial Random Forest modelling of DD drivers allows for influence quantification of topographic, climatic and proximity variables at each site. Third, we report direct and indirect DD drivers from pre-intervention surveys and semi-structured interviews with five REDD+ implementers, 40 villages and 1200 households. Data gathered included perceived changes in forest cover and quality, and their causes. We found general agreement between observed, modelled and reported local DD drivers, yet some were inadequately addressed by interventions. Intra-site differences in drivers underscores the importance of analysing micro-level DD drivers. Our interdisciplinary approach reveals the complexities of local direct and indirect DD drivers, and the complementarity of remotely sensed, spatially modelled and locally reported methods for driver identification. A better understanding of the alignment between DD drivers and REDD+ interventions is vital for practitioners and policy makers to enhance the effectiveness, efficiency, equity and co-benefits of REDD+ at the local level. [ABSTRACT FROM AUTHOR]

The Agriculture, Forestry and Other Land Use (AFOLU) sector contributes with ca. 20-25% of global anthropogenic emissions (2010), making it a key component of any climate change mitigation strategy. AFOLU estimates, however, remain highly uncertain, jeopardizing the mitigation effectiveness of this sector. Comparisons of global AFOLU emissions have shown divergences of up to 25%, urging for improved understanding of the reasons behind these differences. Here we compare a variety of AFOLU emission datasets and estimates given in the Fifth Assessment Report for the tropics (2000-2005) to identify plausible explanations for the differences in (i) aggregated gross AFOLU emissions, and (ii) disaggregated emissions by sources and gases (CO2, CH4, N2O). We also aim to (iii) identify countries with low agreement among AFOLU datasets to navigate research efforts. The datasets are FAOSTAT (Food and Agriculture Organization of the United Nations, Statistics Division), EDGAR (Emissions Database for Global Atmospheric Research), the newly developed AFOLU "Hotspots", "Houghton", "Baccini", and EPA (US Environmental Protection Agency) datasets. Aggregated gross emissions were similar for all databases for the AFOLU sector: 8.2 (5.5-12.2), 8.4, and 8.0 PgCO2 eq. yr-1 (for Hotspots, FAOSTAT, and EDGAR respectively), forests reached 6.0 (3.8-10), 5.9, 5.9, and 5.4 PgCO2 eq. yr-1 (Hotspots, FAOSTAT, EDGAR, and Houghton), and agricultural sectors were with 1.9 (1.5-2.5), 2.5, 2.1, and 2.0 PgCO2 eq. yr-1 (Hotspots, FAOSTAT, EDGAR, and EPA). However, this agreement was lost when disaggregating the emissions by sources, continents, and gases, particularly for the forest sector, with fire leading the differences. Agricultural emissions were more homogeneous, especially from livestock, while those from croplands were the most diverse. CO2 showed the largest differences among the datasets. Cropland soils and enteric fermentation led to the smaller N2O and CH4 differences. Disagreements are explained by differences in conceptual frameworks (carbon-only vs. multi-gas assessments, definitions, land use vs. land cover, etc.), in methods (tiers, scales, compliance with Intergovernmental Panel on Climate Change (IPCC) guidelines, legacies, etc.) and in assumptions (carbon neutrality of certain emissions, instantaneous emissions release, etc.) which call for more complete and transparent documentation for all the available datasets. An enhanced dialogue between the carbon (CO2/ and the AFOLU (multi-gas) communities is needed to reduce discrepancies of land use estimates. [ABSTRACT FROM AUTHOR]

According to the latest report of the Intergovernmental Panel on Climate Change (IPCC), emissions must be cut by 41-72 % below 2010 levels by 2050 for a likely chance of containing the global mean temperature increase to 2°C. The AFOLU sector (Agriculture, Forestry and Other Land Use) roughly contributes with a quarter (~ 10-12 PgCO2e.yr-1) of the net anthropogenic GHG emissions mainly from deforestation, fire, wood harvesting, and agricultural emissions including croplands, paddy rice and livestock. In spite of the importance of this sector, it is unclear where are the regions in the planet with AFOLU emissions hotspots, and how uncertain these emissions are. Here we present a novel spatially comparable dataset containing annual mean estimates of gross AFOLU emissions (CO2, CH4, N2O), associated uncertainties, and leading emission sources, in a spatially disaggregated manner (0.5°), for the tropics, for the period 2000-2005. Our data highlight: i) the existence of AFOLU emissions hotspots on all continents, with particular importance of evergreen rainforest deforestation in Central and South America, fire in dry forests in Africa, and both peatland emissions and agriculture in Asia; ii) a predominant contribution of forests and CO2 to the total AFOLU emissions (75 %) and to their uncertainties (98 %), iii) higher gross fluxes from forests coincide with higher uncertainties, making agricultural hotspots more appealing for effective mitigation action, and iv) a lower contribution of non-CO2 agricultural emissions to the total gross budget (ca. 25 %) with livestock (15.5 %) and rice (7 %) leading the emissions. Gross AFOLU tropical emissions 8.2 (5.5-12.2) were in the range of other databases 8.4 and 8.0 PgCO2e.yr-1 (FAOSTAT and EDGAR respectively), but we offer a spatially detailed benchmark for monitoring progress on reducing emissions from the land sector in the tropics. The location of the AFOLU hotspots of emissions and data on their associated uncertainties, will assist national policy makers, investors and other decision-makers who seek to understand the mitigation potential of the AFOLU sector. [ABSTRACT FROM AUTHOR]

The assessment of positional uncertainty in line and area features is often based on uncertainty in the coordinates of their elementary vertices which are assumed to be connected by straight lines. Such an approach disregards uncertainty caused by sampling and approximation of a curvilinear feature by a sequence of straight line segments. In this article, a method is proposed that also allows for the latter type of uncertainty by modelling random rectangular deviations from the conventional straight line segments. Using the model on a dense network of sub-vertices, the contribution of uncertainty due to approximation is emphasised; the sampling effect can be assessed by applying it on a small set of randomly inserted sub-vertices. A case study demonstrates a feasible way of parameterisation based on assumptions of joint normal distributions for positional errors of the vertices and the rectangular deviations and a uniform distribution of missed sub-vertices along line segments. Depending on the magnitudes of the different sources of uncertainty, not accounting for potential deviations from straight line segments may drastically underestimate the positional uncertainty of line features. [ABSTRACT FROM AUTHOR]