Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation

This study aims to contribute to the understanding of the Congo Basin forests by delivering a detailed map of vegetation types with an improved spatial discrimination and coherence for the whole Congo Basin region. A total of 20 land cover classes were described with the standardized Land Cover Classification System (LCCS) developed by the FAO. Based on a semi-automatic processing chain, the Congo Basin vegetation types map was produced by combining 19 months of observations from the Envisat MERIS full resolution products (300 m) and 8 yr of daily SPOT VEGETATION (VGT) reflectances (1 km). Four zones (north, south and two central) were delineated and processed separately according to their seasonal and cloud cover specificities. The discrimination between different vegetation types (e.g. forest and savannas) was significantly improved thanks to the MERIS sharp spatial resolution. A better discrimination was achieved in cloudy areas by taking advantage of the temporal consistency of the SPOT VGT observations. This resulted in a precise delineation of the spatial extent of the rural complex in the countries situated along the Atlantic coast. Based on this new map, more accurate estimates of the surface areas of forest types were produced for each country of the Congo Basin. Carbon stocks of the Basin were evaluated to a total of 49 360 million metric tons. The regional scale of the map was an opportunity to investigate what could be an appropriate tree cover threshold for a forest class definition in the Congo Basin countries. A 30% tree cover threshold was suggested. Furthermore, the phenology of the different vegetation types was illustrated systematically with EVI temporal profiles. This Congo Basin forest types map reached a satisfactory overall accuracy of 71.5% and even 78.9% when some classes are aggregated. The values of the Cohen's kappa coefficient, respectively 0.64 and 0.76 indicates a result significantly better than random

Deforestation: Correlations, Possible Causes and Some Implications

Changes in national forest areas during 1990-2000 are contrasted with other variables to illustrate correlations and provoke discussion about possible causes. Twenty-five statistically-significant correlations (including rural population, life expectancy, GDP, literacy, commerce, agriculture, poverty and inflation) are illustrated and a statistical model suggests that good governance, alternative employment opportunities, and payments for environmental services may be effective in combating deforestation. The data suggest that a global forest convention may need to be supported by substantial and carefully-targeted development assistance to foster good governance

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Antenatal HIV-1 RNA load and timing of mother to child transmission; a nested case-control study in a resource poor setting

<p>Abstract</p> <p>Objective</p> <p>To determine HIV-1 RNA load during the third trimester of pregnancy and evaluate its effect on <it>in utero </it>and intra-partum/postpartum transmissions in a breastfeeding population.</p> <p>Design</p> <p>A nested case-control study within a PMTCT cohort of antiretroviral therapy naive pregnant women and their infants.</p> <p>Methods</p> <p>A case was a mother who transmitted HIV-1 to her infant (transmitter) who was matched to one HIV-1 positive but non-transmitting mother (control).</p> <p>Results</p> <p>From a cohort of 691 pregnant women, 177 (25.6%) were HIV-1 positive at enrolment and from these 29 (23%) transmitted HIV-1 to their infants, 10 and 19 during <it>in utero </it>and intra-partum/postpartum respectively. Twenty-four mothers sero-converted after delivery and three transmitted HIV-1 to their infants. Each unit increase in log₁₀viral load was associated with a 178 cells/mm³and 0.2 g/dL decrease in TLC and hemoglobin levels, p = 0.048 and 0.021 respectively, and a 29% increase in the risk of transmission, p = 0.023. Intra-partum/postpartum transmitters had significantly higher mean viral load relative to their matched controls, p = 0.034.</p> <p>Conclusion</p> <p>Antenatal serum HIV-1 RNA load, TLC and hemoglobin levels were significantly associated with vertical transmission but this association was independent of transmission time. This finding supports the rationale for preventive strategies designed to reduce vertical transmission by lowering maternal viral load.</p

Perinatal acquisition of drug-resistant HIV-1 infection: mechanisms and long-term outcome

<p>Abstract</p> <p>Background</p> <p>Primary-HIV-1-infection in newborns that occurs under antiretroviral prophylaxis that is a high risk of drug-resistance acquisition. We examine the frequency and the mechanisms of resistance acquisition at the time of infection in newborns.</p> <p>Patients and Methods</p> <p>We studied HIV-1-infected infants born between 01 January 1997 and 31 December 2004 and enrolled in the ANRS-EPF cohort. HIV-1-RNA and HIV-1-DNA samples obtained perinatally from the newborn and mother were subjected to population-based and clonal analyses of drug resistance. If positive, serial samples were obtained from the child for resistance testing.</p> <p>Results</p> <p>Ninety-two HIV-1-infected infants were born during the study period. Samples were obtained from 32 mother-child pairs and from another 28 newborns. Drug resistance was detected in 12 newborns (20%): drug resistance to nucleoside reverse transcriptase inhibitors was seen in 10 cases, non-nucleoside reverse transcriptase inhibitors in two cases, and protease inhibitors in one case. For 9 children, the detection of the same resistance mutations in mothers' samples (6 among 10 available) and in newborn lymphocytes (6/8) suggests that the newborn was initially infected by a drug-resistant strain. Resistance variants were either transmitted from mother-to-child or selected during subsequent temporal exposure under suboptimal perinatal prophylaxis. Follow-up studies of the infants showed that the resistance pattern remained stable over time, regardless of antiretroviral therapy, suggesting the early cellular archiving of resistant viruses. The absence of resistance in the mother of the other three children (3/10) and neonatal lymphocytes (2/8) suggests that the newborns were infected by a wild-type strain without long-term persistence of resistance when suboptimal prophylaxis was stopped.</p> <p>Conclusion</p> <p>This study confirms the importance of early resistance genotyping of HIV-1-infected newborns. In most cases (75%), drug resistance was archived in the cellular reservoir and persisted during infancy, with or without antiretroviral treatment. This finding stresses the need for effective antiretroviral treatment of pregnant women.</p

Long-term carbon loss in fragmented Neotropical forests

Tropical forests play an important role in the global carbon cycle, as they store a large amount of carbon (C). Tropical forest deforestation has been identified as a major source of CO2 emissions, though biomass loss due to fragmentation—the creation of additional forest edges—has been largely overlooked as an additional CO2 source. Here, through the combination of remote sensing and knowledge on ecological processes, we present long-term carbon loss estimates due to fragmentation of Neotropical forests: within 10 years the Brazilian Atlantic Forest has lost 69 (±14) Tg C, and the Amazon 599 (±120) Tg C due to fragmentation alone. For all tropical forests, we estimate emissions up to 0.2 Pg C y−1 or 9 to 24% of the annual global C loss due to deforestation. In conclusion, tropical forest fragmentation increases carbon loss and should be accounted for when attempting to understand the role of vegetation in the global carbon balance.This study was part of the project ‘Biodiversity conservation in a fragmented landscape at the Atlantic Plateau of São Paulo’ (BIOTA/Caucaia and BioCAPSP) funded by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, project no. 99/05123-4, 01/13309-2, 02/02125-0, 02/02126-7), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, project no. 690144/01-6), Fundação O Boticário de Proteção à Natureza, and by BMBF (German Federal Ministry of Education and Research, project n. 01LB0202). J.P.M. and M.C.R. thank the Brazilian Science Council (Conselho Nacional de Desenvolvimento Científico) for his research fellowship (process no. 307934/2011-0 and 312045/2013-1, respectively). A.H. and S.P. were supported by the ERC advanced grant 233066. M.M. has been supported by BMBF (project n. 01LB0202), and the Department of Ecological Modelling of the Helmholtz Centre for Environmental Research (UFZ). We thank Birgit Felinks for the support during the Mata Atlântica project. Florian Hartig provided valuable comments on an earlier version of this manuscript. S.P. has been funded by the Helmholtz Association of German Research Centres within the project ‘Biomass and Bioenergy systems’. A.H. was also supported by the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. A.H. thanks C. Wissel and H. Bossel for supporting the FORMIND project over the years

Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda

<p>Abstract</p> <p>Background</p> <p>Assessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset.</p> <p>Results</p> <p>The comparison of the biomass/carbon maps show strong disagreement between the products, with estimates of total aboveground biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Compared to the reference map based on country-specific field data and a national Land Cover (LC) dataset (estimating 468 Tg), maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change (IPCC) default values, and global LC datasets tend to strongly overestimate biomass availability of Uganda (ranging from 578 to 2201 Tg), while maps based on satellite data and regression models provide conservative estimates (ranging from 343 to 443 Tg). The comparison of the maps predictions with field data, upscaled to map resolution using LC data, is in accordance with the above findings. This study also demonstrates that the biomass estimates are primarily driven by the biomass reference data while the type of spatial maps used for their stratification has a smaller, but not negligible, impact. The differences in format, resolution and biomass definition used by the maps, as well as the fact that some datasets are not independent from the reference data to which they are compared, are considered in the interpretation of the results.</p> <p>Conclusions</p> <p>The strong disagreement between existing products and the large impact of biomass reference data on the estimates indicate that the first, critical step to improve the accuracy of the biomass maps consists of the collection of accurate biomass field data for all relevant vegetation types. However, detailed and accurate spatial datasets are crucial to obtain accurate estimates at specific locations.</p

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets